Spatial electric load forecasting

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Spatial Electric Load Forecasting Second Edition, Revised and Expanded

H. Lee Willis ABB Inc. Raleigh, North Carolina

M A R C E L

MARCEL DEKKER, INC. D E K K E R

NEW YORK • BASEL

ISBN: 0-8247-0840-7 This book is printed on acid-free paper. Headquarters Marcel Dekker, Inc. 270 Madison Avenue, New York, NY 10016 tel: 212-696-9000; fax: 212-685-4540 Eastern Hemisphere Distribution Marcel Dekker AG Hutgasse 4, Postfach 812. CH-4001 Basel, Switzerland tel: 41-61-260-6300; fax: 41-61-260-6333 World Wide Web http ://www.dekker. com The publisher offers discounts on this book when ordered in bulk quantities. For more information, write to Special Sales/Professional Marketing at the headquarters address above. Copyright © 2002 by Marcel Dekker, Inc. All Rights Reserved. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage and retrieval system, without permission in writing from the publisher. Current printing (last digit): 10 9 8 7 6 5 4 3 2 1 PRINTED IN THE UNITED STATES OF AMERICA

POWER ENGINEERING Series Editor

H. Lee Willis ABB Inc. Raleigh, North Carolina

1. Power Distribution Planning Reference Book, H. Lee Willis 2. Transmission Network Protection: Theory and Practice, Y. G. Paithankar 3. Electrical Insulation in Power Systems, N. H. Malik, A. A. AI-Arainy, and M. I. Qureshi 4. Electrical Power Equipment Maintenance and Testing, Paul Gill 5. Protective Relaying: Principles and Applications, Second Edition, J. Lewis Blackburn 6. Understanding Electric Utilities and De-Regulation, Lorrin Philipson and H. Lee Willis 7. Electrical Power Cable Engineering, William A. Thue 8. Electric Systems, Dynamics, and Stability with Artificial Intelligence Applications, James A. Momoh and Mohamed E. EI-Hawary 9. Insulation Coordination for Power Systems, Andrew R. Hileman 10. Distributed Power Generation: Planning and Evaluation, H. Lee Willis and Walter G. Scott 11. Electric Power System Applications of Optimization, James A. Momoh 12. Aging Power Delivery Infrastructures, H. Lee Willis, Gregory V. Welch, and Randall R. Schrieber 13. Restructured Electrical Power Systems: Operation, Trading, and Volatility, Mohammad Shahidehpour and Muwaffaq Alomoush 14. Electric Power Distribution Reliability, Richard E. Brown 15. Computer-Aided Power System Analysis, Ramasamy Natarajan 16. Power System Analysis: Short-Circuit Load Flow and Harmonics, J. C. Das 17. Power Transformers: Principles and Applications, John J. Winders, Jr. 18. Spatial Electric Load Forecasting: Second Edition, Revised and Expanded, H. Lee Willis

ADDITIONAL VOLUMES IN PREPARATION

Dielectrics in Electric Fields, Gorur G. Raju

Series Introduction A critical function contributing to the success of any enterprise is its ability to dependably forecast the volume and character of its future business. Good forecasts permit a company to keep capacity margins and inventory lean, delay spending until the last moment, but still satisfy customer needs. For a power delivery utility, forecasts of its future business volume must describe both how much power its customers will need and where that power must be delivered, so that the utility can make arrangements to convey the amounts required to the locations where it will be needed. The power delivery utility needs a spatial electric load forecast, a projection of future electric demand that identifies both how much power will be needed and where that future electric demand will be located. The updates and additions included in this second edition of Spatial Electric Load Forecasting reflect the larger changes that have transformed the electric power industry in the last ten years. First, this edition has grown by 63%. Modern power delivery utilities operate with narrower capacity and financial margins and are held to higher performance standards than in the past. This means that they need both more accurate and more comprehensively detailed processes, including forecasts, which must be applied with much more focus and far less tolerance for error. Simply put, the job of running an electric delivery utility has become much more difficult, and this is reflected in all aspects of its work, including its forecasting. Second, while this book includes updated and expanded treatment of technical theory, it focuses much more on practical application, particularly in near- and medium-term time frames. Modern utilities must put added emphasis

iv

Series Introduction

on short-term improvement and maintaining good performance in the near term as they build for the future. Finally, this book is more accessible, with expanded introductory-level material and new tutorial and self-study chapters, reflecting the growing need throughout the power industry to develop new experts as an aging workforce of planners and engineers retires. As both the author of this book and the editor of the Power Engineering Series, I am proud to include Spatial Electric Load Forecasting: Second Edition, Revised and Expanded, among this important group of books. Like all books in Marcel Dekker, Inc.'s Power Engineering Series, this book discusses modern power technology in a context of proven, practical applications. It is useful as a reference book as well as for self-study and advanced classroom use. Marcel Dekker, Inc.'s Power Engineering Series includes books that cover the entire field of power engineering, in all of its specialties and sub-genres, all aimed at providing practicing power engineers with the knowledge and techniques they need to meet the electric industry's challenges in the 21st century. H. Lee Willis

Preface This second edition of Spatial Electric Load Forecasting is both a reference and a tutorial guide on forecasting of electric load for power delivery system planning. Like the first edition, it provides nearly 450 pages of detailed discussion on forecasting and forecasting methods. But in addition, this revised edition provides nearly 300 new pages emphasizing practical application - how planners can evaluate their forecasting needs, what methods they should select for which situations, where they can find required data, and how to apply both data and method to best support the planning process. Spatial electric load forecasting is as critical a first step in transmission and distribution (T&D) planning as it has ever been. As was always the case, the efficiency, economy, and reliability of modern power systems depend not only on how well the equipment is selected, sized, and engineered, but on how well it is sited - put in the optimal locations with respect to area needs. A spatial load forecast addresses the where aspect of T&D planning, by studying load growth on a coordinated locational basis throughout the system. Where will demand grow? How much will consumer demand change at this location? The spatial load forecast answers these and other questions about future demand in a way that supports effective T&D planning. This -where, or spatial, dimension is what sets spatial load forecasting apart from other types of energy forecasting. The three-dimensional nature of spatial forecasting (predicting load growth as a function of the two X-Y dimensions in space, plus the traditional forecasting dimension of time) makes it particularly challenging. It also means that many of the "rules" encountered, and tradeoffs

vi

Preface

made in accommodating them, will run counter to traditional "projection in time" paradigms. In addition, in the last decade the needs for and application of spatial forecasting have expanded beyond the traditional least-cost planning venue. Modern utilities as pushing their systems to achieve higher utilization rates (higher ratios of peak demand served to total installed capacity) in order to improve investment efficiency, while making firm commitments to high reliability levels. Spatial forecasting is now important as a basis for reliability and reliability-based planning, too, and new methodology has been developed to accommodate this need. This second edition was written to serve the needs of modern electric power delivery planners. It reflects the changing nature of the industry in myriad ways, but three changes seem most significant: This edition includes greatly expanded discussion of applications. The original edition was aimed primarily at experienced planners and focused mostly on theory and methodology. By the author's survey, when first published, the average owner of that edition had over 15 years of experience in T&D planning. A similar survey in the last year indicated the average user of the book now has less than three years experience as a planner. To meet this need, Chapters 18 through 22, comprising nearly 200 pages, cover in detail the nuances of forecasting in urban, suburban, and rural areas. They focus on the characteristics that can be expected when forecasting in rapidly developing nations, in urban areas with a lot of redevelopment, or in a variety of common types of unusual forecasting contexts which planners may encounter. These chapters not only address forecasting but discuss some of the best practices related to using a forecast and interfacing it with the planning functions that follow it. Weather adjustment of load data and normalization of forecasts to standard design conditions is the subject of 74 pages of new material (Chapters 5 and 6). Measured by page count, the book has changed from a 1% focus on weather adjustment in the first edition to a 9% focus in the second edition. This meets a dramatically growing need in the utility industry. As more utilities push their system utilization rates upward, the risk associated with extreme weather must be a real consideration in their planning. Many of the large interruption events that plagued utilities in the past decade were caused at least in part by incorrect anticipation of peak demands tied to extreme weather. The term "utility customer" has been replaced with "energy consumer." In a de-regulated industry it is often difficult to determine who is whose customer - the homeowner is the ESCo's customer, who in turn is the Distco's customer. To clear up any ambiguity in the use of the word

Preface

vii

customer and because it seems a more apt description of the energy user's role, the term "consumer" is used throughout to identify the households and businesses that are actually using the power. The book is organized into three sections, or groups of chapters, which deal respectively with background and basics, methodology, and application. Chapters 1 - 8 discuss the requirements for and characteristics of spatial forecasts and provide tutorials on a number of basic and background issues associated with modern forecasting and planning. Chapter 1 begins this group of chapters with a tutorial on T&D systems, T&D planning, and spatial electric load forecasting, for those unfamiliar with the field. It is a good stand-alone overview but will prove somewhat redundant to experienced forecasters and planners. Chapter 2 looks at the basis underlying electric load, including the reasons why consumers need power (end uses) and the characteristics of the appliances, devices, and machines that translate end-use demand into electric demand. It provides a foundation for planners who want to understand load and reliability needs at the most rudimentary level, which is often necessary in particularly challenging forecast situations. Chapter 3 takes what might be termed the opposite view of electric load from Chapter 2. Here, electric demand is viewed from the perspective of the power system, regardless of its causes and consumer profiles. The characteristics of electric demand, including temporal behavior and particularly coincidence of load as a function of consumer group size, are examined in a comprehensive manner. Chapter 4 merges the themes of Chapters 2 and 3 and develops them into various types of load models, each appropriate to different types of demand or consumer analysis situations. The chapter looks at quantitative formats, and various types of end-use representations, that analyze and model load as a function of time. Some of this detail is needed as a foundation for Chapters 5 and 6. Chapter 5 is the first of two chapters on weather, its impact on load, and the issues and methods that planners must deal with in the forecasting and planning process. It is basically a tutorial on weather as viewed by the load forecaster and delivery planner, and on how this relationship can be modeled for forecasting purposes. Chapter 6 builds on Chapter 5, looking at extreme weather and the electric peaks it causes. What is extreme weather? How does a utility analyze and characterize it? Most important, how do planners identify an "extreme enough" weather target for planning? The chapter reviews several methods of increasing complexity and quality to address this last question. Chapter 7 is the first to take up the spatial aspect of electric load growth in detail. It focuses on spatial growth behavior - on how electric load growth

viii

Preface

"looks" as a ftmction of location and how it spreads or diffuses over wider areas as cities and towns grow. It develops and explores the consequences of the fundamental rule of spatial load growth - the hierarchical "S" curve growth characteristic. Chapter 8 looks at what qualities are most importance in a good load forecast. In particular, it explores the meaning of "accuracy" in the spatial format, when an error can include merely getting the location wrong, even if the amount of load growth and its timing was forecast correctly. Accuracy and error behavior in a spatial context often seem counterintuitive to the uninitiated. Once understood, however, the concepts are simple, even if the mathematics behind them are not. Chapters 9 through 16 constitute the second part of the book and cover forecasting methods including basic approaches, data usage and algorithms, and procedures. These chapters are largely taken from the first edition, but have been updated as appropriate. Chapter 15, on hybrid algorithms, is new, covering methods that had not been developed when the first edition was published. Chapter 9, on trending methods, covers methods that forecast future load growth by working with historical load values themselves. Extrapolation of past area load growth trends using polynomial curve fitting (by regression) is the classic trending method and the technique most people think of first when they face spatial forecasting problems. But as the chapter makes clear, there are a host of innovative and worthwhile improvements on that basic method, and there are several entirely different trending approaches worth considering. Chapter 10 addresses methods that forecast spatial load growth by modeling the root causes of load growth, rather than just trending historical load measurements themselves. This chapter introduces the basic definitions and concepts, which are developed in subsequent chapters Chapter 11 covers an actual simulation-based forecast in a step-by-step manner, where every computation and data manipulation is done manually, rather than by computer. Actually performing a simulation forecast manually would be prohibitively expensive for most utilities, but it proves quite illuminating as a learning example and gives a very thorough example for anyone needing to learn the simulation technique. Chapters 12, 13, and 14 constitute a three-chapter discussion of simulation algorithms and how they are rendered as computer programs. These chapters cover, respectively, the overall organization of data flow, functions, and the division of the forecasting process in major program modules; the various computational engines that are used in each of the major modules of a simulation program, and their variations; and a number of "tricks" or advanced features that address special situations or accelerate analytical speed and accuracy. These chapters go beyond the understanding of methodology required by most forecasters, but include the detail needed by those who are writing or tailoring algorithms.

Preface

ix

Chapter 15 covers a relatively new category of forecasting algorithm in which elements of trending and simulation are melded together to obtain (at least hopefully) advantages over either of the two approaches alone. The chapter introduces an information-usage perspective for evaluating what forecast methods do with their data, after which it looks at three hybrid algorithms, each a very different mix of features taken from the trending and simulation worlds. Chapter 16 addresses two challenging areas of modeling consumer demand in space and time. The first involves using a simulation method to forecast demand for not only electric energy, but gas and fuel oil too. Such forecasts are very useful in "converged" planning by gas and electric utilities. The second area of advanced modeling seeks to analyze demand for power quality, and how that varies with location, time, and consumer class. Chapters 17 through 22 constitute the final section of the book, addressing practical application. Chapter 17 presents a comprehensive "level playing field" evaluation of major forecasting methods including a look at 19 specific forecast programs and methods, examples of trending, simulation, and hybrid approaches. It presents and then applies by example case study a method to evaluate forecast needs and select the most appropriate forecasting method for any electric utility situation. Chapter 18 introduces the concept of development dimensionality, a measure of how local-area load growth interacts with growth in those areas around it, and how the complexity or "dimension" of this process increases over time. In some sense this chapter can be viewed as an advanced discussion of load growth behavior and the "S" curve growth characteristics covered in Chapter 7. However, the focus here is on practical application, on understanding this dimensionality and how it impacts the approaches planners must use in various types of forecast situations. Chapter 19 looks in detail at load growth caused by urban "redevelopment," which occurs whenever existing land use in an area of a city is replaced by some (usually higher density) type of new development. Such growth is responsible for 25% of all load growth. The chapter begins with a quantitative look at redevelopment and its characteristics. It classifies redevelopment into five categories and discusses nine methods of redevelopment forecasting, and their advantages and disadvantages. Chapter 20 discusses spatial forecasting in developing economies, particularly where forecasters must predict the load in rural provinces previously without electric power but scheduled for "electrification." Forecasting methods and procedures appropriate to these situations are discussed. A forecast of such an area is used as an example and method, data, and results analyzed in detail. Chapter 21 is an accumulation of observations and advice on how to maximize forecast accuracy and minimize effort expended in practical spatial forecasting. Although its focus is on simulation methods, it will be of use to anyone trying to get the most out of any forecasting method, simulation or

x

Preface

trending. Its first two sections, on application and calibration, are meant to be of use to planners regardless of the particular program or approach they are using. The remaining two sections address how to work around or "live with" limitations and features common to several specific spatial simulation programs in wide use. Chapter 22 wraps up the book with a summary of forecast priorities and a presentation of recommendations and pitfalls. Together with Chapter 1, it provides a good managerial-level tutorial on spatial forecasting, its priorities, and the chief concerns that both management and technician must keep in mind to do the job right. I wish to thank my many colleagues and co-workers who have provided so much assistance and advice on this book, in particular, Drs. Richard Brown and Andrew Hanson and David Farmer, Randy Schrieber, and Greg Welch for their valuable assistance and good-natured skepticism and encouragement. I also owe a debt of gratitude to Mike Engel of Midwest Energy, David Helwig of Commonwealth Edison, and Jim Bouford of National Grid for their advice and suggestions. In addition, as always, Rita Lazazzaro, Lila Harris, and Russell Dekker of Marcel Dekker, Inc., have done an outstanding job of providing encouragement and support. H. Lee Willis

Contents

Series Introduction Preface 1

Spatial Electric Load Forecasting 1.1 1.2 1.3 1.4

2

Hi v 1

Spatial Load Forecasting T&D Planning Requirements for a T&D Load Forecast Summary References

1 4 25 35 35

Consumer Demand for Power and Reliability

37

2.1 2.2 2.3 2.4 2.5

37 38 49 65 70 71

The Two Qs: Quality and Quantity of Power Electric Consumer Demand for Quantity of Power Electric Consumer Demand for Quality of Power Two-Q Analysis: Quantity and Quality versus Cost Conclusion and Summary References

XI

xii

3

4

Contents

Coincidence and Load Behavior

73

3.1 3.2 3.3 3.4

73 74 85 94 94

Introduction Peak Load, Diversity, and Load Curve Behavior Measuring and Modeling Load Curves Summary References

Load Curve and End-Use Modeling 4.1 4.2 4.3 4.4 4.5

End-Use Analysis of Electric Load The Basic "Curve Adder" End-Use Model Advanced End-Use Models Application of End-Use Models Computer Implemented End-Use Load Curve Model References

95 95 102 109 112 121 126

Weather and Electric Load 5.1 Introduction 5.2 Weather and Its Measurement 5.3 Weather's Variation with Time and Place 5.4 Weather and Its Impact on Electric Demand References

129 129 131 137 147 165

Weather Design Criteria and Forecast Normalization 6.1 Introduction 6.2 Extreme Weather 6.3 Standardized Weather "Design Criteria" 6.4 Analyzing Weather's Impact on Demand Curve Shape 6.5 Risk-Based Determination of Weather Criteria 6.6 Example: Risk-Based Weather-Related Demand Target Analysis 6.7 How Often Will a Utility See "Weather-Related Events?" 6.8 Summary and Guidelines References

167 167 169 174 180 185 194 197 200 202

Contents

xiii

7

Spatial Load Growth Behavior

203

7.1 7.2 7.3 7.4

203 204 211 228 229

8

9

Introduction Spatial Distribution of Electric Load Small Area Load Growth Behavior Summary References

Spatial Forecast Accuracy and Error Measures

231

8.1 8.2 8.3 8.4

231 232 245 257 259

Introduction Spatial Error: Mistakes in Location Spatial Frequency Perspective on Error Impact Conclusions and Guidelines References

Trending Methods 9.1 9.2 9.3 9.4 9.5

Introduction Trending Using Polynomial Curve Fit Improvements to Regression-Based Curve Fitting Methods Other Trending Methods Summary References

10 Simulation Method: Basic Concepts 10.1 10.2 10.3 10.4 10.5

Introduction Simulation of Electric Load Growth Land-Use Growth: Cause and Effect Quantitative Models of Land-Use Interaction Summary of Key Concept References

11 A Detailed Look at the Simulation Method 11.1 Introduction

261 261 262 273 -293 300 301

303 303 304 314 321 324 324 325 325

xiv

Contents 11.2 Springfield 11.3 The Forecast 11.4 Critique and Commentary on Manual Simulation References

12 Basics of Computerized Simulation 12.1 12.2 12.3 12.4 12.5

Introduction Overall Structure and Common Features Small Area Spatial Module Top-Down Structure Summary and Conclusion References and Bibliography

13 Analytical Building Blocks for Spatial Simulation 13.1 13.2 13.3 13.4

Introduction Land-Use Input-Output Matrix Model Activity Center Gravity Models Consumer-Class Spatial Allocation Using Preference Matching References and Bibliography

14 Advanced Elements of Computerized Simulation 14.1 14.2 14.3 14.4 14.5

Introduction Simulation Program Structure and Function Fast Methods for Spatial Simulation Growth Viewed as a Frequency Domain Process Summary References

15 Hybrid Trending-Simulation Methods 15.1 15.2 15.3 15.4

Introduction Using Information in a Spatial Forecast Land-Use Classified Multivariate Trending (LCMT) Extended Template Matching (ETM)

326 330 369 372

373 373 374 382 391 393 394

397 397 397 405 410 418

421 421 421 436 441 444 445

447 447 448 451 455

Contents

xv

15.5 SUSAN - A Simulation-Driven Trending Method 15.6 Summary and Guidelines References

16 Advanced Demand Methods: Multi-Fuel and Reliability Models 16.1 16.2 16.3 16.4

462 474 475

477

Introduction Simultaneous Modeling of Multiple Energy Types Spatial Value-Based Analysis Conclusion References

477 478 494 504 505

17 Comparison and Selection of Spatial Forecast Methods

507

17.1 17.2 17.3 17.4 17.5 17.6

Introduction Classification of Spatial Forecast Methods Comparison Test of Nineteen Spatial Forecast Methods Data and Data Sources Selecting a Forecast Method Example Selection of a Spatial Forecast Method by a Utility References

18 Development Dimensionality: Urban, Rural and Agrarian Areas 18.1 18.2 18.3 18.4 18.5

Introduction Regional Types and Development Dimension Forecasting Load Growth in Rural Regions Forecasting Load Growth in Agrarian Regions Summary and Guidelines Reference

19 Metropolitan Growth and Urban Redevelopment 19.1 19.2 19.3 19.4

Introduction Redevelopment Is the Process of Urban Growth Representing Redevelopment in Spatial Forecasts Eight Simulation Approaches to Redevelopment Forecasting

507 508 513 533 540 548 565

569 569 571 577 586 590 590 591 591 592 603 606

xvi

Contents 19.5 Recommendations for Modeling Redevelopment Influences 19.6 Summary References

20 Spatial Load Forecasting in Developing Economies 20.1 20.2 20.3 20.4 20.5

Introduction Modeling Load Growth Due to Latent Demand Example Latent Demand Forecast New-City Load Growth Summary and Guidelines

21 Using Spatial Forecasting Methods Well 21.1 21.2 21.3 21.4 21.5 21.6

Introduction Forecast Application Calibration of a Spatial Forecast Model Tricks and Advice for Using Simulation Programs Well Partial-Region Forecast Situations Forecasts That Require Special Methodology References

22 Recommendations and Guidelines 22.1 22.2 22.3 22.4 22.5 Index

Introduction Spatial Forecasting Priorities Recommendations for Successful Forecasting Pitfalls to Avoid How Good Is Good Spatial Forecasting?

626 629 630

631 631 634 643 660 670

673 673 674 680 695 700 709 716 717 717 717 720 730 736 739

1 Spatial Electric Load Forecasting 1.1 SPATIAL LOAD FORECASTING In order to plan the efficient operation and economical capital expansion of an electric power delivery system, the system owner must be able to anticipate the need for power delivery - how much power must be delivered, and where and when it will be needed. Such information is provided by a spatial load forecast, a prediction of future electric demand that includes location (where) as one of its chief elements, in addition to magnitude (how much) and temporal (when) characteristics. Figure 1.1 shows the spatial nature of electric load growth over time, in this case the anticipated growth of electric demand in a metropolitan area in the central United States. Over a twenty-year period, the total electric demand in this city is expected to increase by nearly fifty percent. Growth is expected to affect numerous existing areas of the system - those where load already exists — where the present demand is expected to increase substantially. Facilities in these areas can be expected to be much more heavily loaded in the future, and may need enhancement or redesign to greater capacity. The growth also includes considerable spread of electric demand into currently vacant areas, where no electric demand exists. Here, the utility must schedule additions of equipment and facilities to meet the demand as it develops.

Chapter 1

2011 WINTER PEAK 3442MVA

1991 WINTER PEAK 2310 MVA

Ten miles

Figure 1.1 Maps of peak annual demand for electricity in a major American city, showing the expected growth in demand during a 20-year period as determined using a comprehensive simulation-based method. Growth in some parts of the urban core increases considerably, but in addition, electric load spreads into currently vacant areas as new suburbs are built to accommodate an expanded population.

In addition, a forecast as shown in Figure 1.1 identifies areas where no electric load growth is expected - information quite useful to the utility planner, for it indicates those areas where no new facilities will be needed. In other areas, electric demand may decrease over time, due to numerous causes, particularly the deliberate and planned actions that the utility or the energy consumers on its system may take to reduce energy consumption - demand side management (DSM). Spatial analysis of how these reductions might impact future transmission and distribution (T&D) requirements is a useful feature of electric utility planning, and can be accommodated by some types of spatial forecasting methods. In addition, competitive market assessment and open access planning of electric systems require spatial analysis of the market - the electric demand, and how it will react to changes in price and availability of electric power and its competing energy sources, such as gas and solar power.

Spatial Electric Load Forecasting

3

Small Area Forecasting A very wide variety of methods exist to forecast electric demand growth on a spatial basis. In all of these, geographic location of load growth is accomplished by dividing the utility service territory into many small areas, as shown in Figure 1.2. These might be irregularly shaped areas of varying size, as for example, the service areas of substations or feeders in the system, or they might be square areas defined by a grid. Any technique that forecasts load by extrapolating recent growth trends on a feeder - or substation area- basis is a small area forecasting method (although perhaps not a very accurate one) that works on irregularly shaped and sized areas. The spatial forecasts shown in Figure 1.1 were accomplished by dividing the region studied into 60,000 square areas, each 1/4 mile wide (40 acres). Some spatial forecast methods work with square areas as small as 2.5 acres (1/16 mile wide), analyzing as many as 3,000,000 at one time. Regardless, all spatial forecasts work on a small area basis, but as will be discussed later in this book, not all small area forecasts are spatial forecasts. A spatial forecast involves the coordinated forecast of all small areas in a region, using a consistent and coherent set of assumptions and characteristic factors. Many small area forecast methods, particularly trending, do not, strictly speaking, accomplish this coordination.

Equipment areas

N

llnifnvm ttr

Figure 1.2 Spatial load forecasts are accomplished by dividing the service territory into small areas, either irregularly shaped areas, perhaps associated with equipment service areas, or elements of a uniform grid.

4

Chapter 1

1.2 T&D PLANNING Electric power delivery is among the most capital intensive of businesses. The required transmission and distribution (T&D) facilities need rights-of-way and substation sites, power equipment for transmission, distribution, protection and control, and extensive construction labor, all involving considerable expense. Arrangements for new or expanded facilities normally require several years, meaning that a power delivery utility usually must plan at least five years ahead. For a variety of reasons, most utilities wants to evaluate the wisdom and value of their investments over a portion - roughly the first half - of their service lifetimes, meaning that long-range planning needs to look out another ten to fifteen years into the future. Thus, power delivery utilities need to plan, in various ways, over a period as far as fifteen or twenty years into the future. Planning is a decision-making process that seeks to identify the best schedule of future resource commitments and actions to achieve the utility's goals. Ordinarily, these goals include financial considerations — minimizing cost and maximizing profit - along with service quality and reliability standards, as well as other criteria, including environmental impact, public image, and future flexibility. Generally, the objective of the T&D planning process is to determine an orderly and economical expansion of equipment and facilities to meet the utility's future electric demand with an acceptable level of reliability. This involves determining the sizes, locations, interconnections, and timing of future additions to transmission, substation, and distribution facilities, and perhaps a compatible program of "nontraditional distributed resource" commitments as well - such things as demand-side management, distributed generation and storage, and automation. Such planning is a difficult task, compounded by recent trends of tightening design margins, lengthening equipment lead times, and increasing regulatory scrutiny. Traditional T&D Planning Traditionally, T&D planning was a standards-based planning process. An electric utility's Engineering Department set certain standards for equipment type, characteristics, loading, and usage, as well as standards for voltage drop, power factor, utilization, contingency margin, and other operating parameters for the system (see Willis, 1998, Chapters 4 and 5). Planners then attempted to develop a plan that met all standards and criteria and had the lowest possible cost. "Lowest cost" was rigorously defined by decree/agreement with state regulatory authorities. It usually meant that all costs were considered, and that the utility attempted to minimize the total revenue requirements needed to serve


TRANSMISSION SYSTEM PLANNING 5 - 25 yrs.

t SUBSTATION SYSTEM PLANNING 3 - 20 yrs.

t FEEDER SYSTEM PLANNING 1 - 1 5 yrs.

Figure 1.3 The traditional T&D planning process in a vertically integrated electric utility. The spatial load forecast is the first step, driving the rest of the planning process.

Transco

LDC or DistCo LOAD FORECAST 1-25 yrs. ahead

TRANSMISSION SYSTEM PLANNING 5-25 yrs.

ESCo

CONTRACTS 1-25 yrs. ahead

SUBSTATION SYSTEM PLANNING 3 -20 vrs. k

/

'

r ^ FEEDER SYSTEM PLANNING 1-15 yrs.

Figure 1.4 The T&D planning process in a de-regulated power industry. "Planning" (the study of the future, assessment of needs and options, selection of strategy and development of tactics to achieve it) is now part of three organizations. These include one or more electric service companies (ESCos), the local distribution company, which may be a pure wire company (Distco) or have an interest in selling energy, too (LDC - local distribution company), and the transmission owner-operator (Transco or regional transmission authority). Communications between all (crossing the dotted lines) are heavily regulated and formalized. Both the ESCos and delivery companies have an interest in spatial forecasting.

Chapter 1

its consumers' demand. Minimizing revenue requirements meant that the utility considered operating and financing costs as well as initial capital costs in deciding if one option was less expensive than another. Figure 1.3 shows the traditional T&D planning process as it was often represented, consisting of transmission, substation, and distribution-level planning. The exact organization and emphasis of these individual planning steps would vary from one utility to another. Regardless, a key element of the overall planning was the load forecast, the first step. That defined the capabilities the future system needed to possess. If the forecast was poorly or inappropriately done, then subsequent steps were directed at planning for future loads different than would develop, and the entire planning process was at risk. Table 1.1 shows typical traditional lead times required to plan, permit, and put into place facilities at various levels of an electric T&D system. Conditions and requirements varied from one situation to another, but the values shown are illustrative. Larger, high voltage equipment requires longer lead times. Smaller equipment at the lower voltage levels can be installed with a shorter lead time. Modern T&D Planning T&D planning at the beginning of the 21st century is decidedly different from traditional T&D planning, in both operating environment and planning focus. To begin, most of the power production and bulk transmission in the power industry has been de-regulated. It is under federal regulatory control, and regulated in a way that fosters competition and provides minimal protection for business risk, but a great deal of reliability protection and a certain amount of price protection for energy consumers. De-regulation has brought about disaggregation of power delivery systems, and a change in the entities planning "their parts" of the utility industry, as shown in Figure 1.4. Somewhere in the chain of power flow from generator to consumer, a dividing line has been drawn between the wholesale grid and the energy delivery systems, and perhaps between the energy delivery and the retail marketer(s). The de-regulated electric power landscape provides a bewildering array of slightly different ways of organizing the local electric industry. Readers needing more detail may wish to consult a reference on the industry's structure (e.g., Philipson and Willis, 1999). Under de-regulation, transmission is owned by a Transco (transmission company) and operated by an ISO (independent system operator) or RTO (regional transmission operator). Distribution is owned by a Distco or an LDC (local distribution company). For purposes of this discussion, four entities will be identified as composing the "power industry," only three of which are discussed here:


7

Genco - the various companies that own and operate generation, sell power into the competitive wholesale grid and provide related services (load following, etc). They are not a concern here and are listed only for completeness. Trans Co - the owner(s) and/or operator of the bulk power transmission system in the region. "Bulk power transmission" is defined here as transmission connected to generators, but not to consumers (except very large industries that buy power on the competitive market). Strictly speaking, Transco means the transmission owner, but it will be used here to refer to whoever is making the plotting the future of and the decisions about expansion and investment in the bulk power transportation system for the region. DistCo or Local Distribution Company (LDC) - the company that operates the wires down to and including the connection to the end energy consumers. A "DistCo" owns and operates the wires, providing the service of connectivity and power delivery, but sells no power (it merely moves power for ESCos). A local distribution company (LDC) provides this service, and sells the power itself. Energy service companies (ESCos) retail power, buying it at the wholesale level and selling it to retail consumers, and paying transmission and local delivery charges to the Transco and Distco, respectively, to have it delivered. This disaggregation of ownership and operation, with the slightly different perspectives it brings to each entity, and the formalized, regulated communication between them, is the first major difference in a "modern" power industry. The T&D system is no longer owned and operated by a single company which can plan it "as a piece" and make compromises between or commitments from T to D and vice versa, as it sees fit. Modern planning of transmission and distribution is done separately, with only limited communication between planning staffs. Often, regulatory requirements put up a "firewall" between the different organizations, setting tight limits and regulations on what information must be and cannot be shared and how and when these organizations will communicate with one another, and how they must do it. The dividing line between "T" and "D" varies quite greatly from one state jurisdiction to another, a remarkable fact considering that the power systems are (or were) rather similar regardless of location. Just where this line is drawn has a great deal to do with the nature of planning in a region. Table 1.1 shows the dividing line between T and D for several states in the U.S., at the time this was written. Dividing lines and their interpretation will change over time. Generally,

8

Chapter 1

the "D" or distribution utility (often called the delivery utility) owns all facilities from the energy consumer's meter box up to and including the switches on the high side of the transformers that accept power at the "T" voltage from the transmission utility. A second difference between traditional and modern T&D planning is that many states have moved toward performance-based regulation of utilities. Performance-based rates usually mean that the utility's earnings are tied to the reliability of service it gives its consumers. The issues are much too complicated and vary too much from one state to another to cover in detail here. However, many utilities have a very real incentive to focus on achieving certain reliability targets. Beyond this, electric delivery utilities are seeing much more financial pressure to cut costs than they saw traditionally. Traditionally, a utility had a regulatory obligation to consider alternatives and select the least-cost options in every case. But it was permitted a great deal of discretion in how it defined the standards and criteria that set limits on how far it could trim costs, and in how it planned its system expansion and spending. Capital dollars went into the "rate base" and earned the permitted profit margin. So in a way, a utility had an incentive to keep justifiable spending as high as possible so that the rate base, and hence the base on which it earned money, would grow. Modern power delivery utilities are still subject to the same regulatory paradigm, but they have a host of new financial pressure points that control their planning priorities so they often need to spend far less than would have been justifiable under the traditional paradigm. First, many can no longer assume that

Table 1.1 Regulatory "Dividing Line" between T and D* State

Level

Florida

35 kV

Wisconsin

50 kV

Maine

99 kV

Texas

138kV

Pennsylvania

138kV

Illinois

138 kV

* Some of the data here is based on proposed regulations and may be superceded.


9

all "justifiable costs" will be passed into their rate base and therefore earn them a profit. Many utilities are under rate freezes: if costs rise, they must cut back somewhere because they cannot pass the cost on to their consumers. A few are luckier, their rates will increase, but no matter what they need or could justify, their spending cannot grow beyond a certain rate. And a few utilities are under mandated rate reductions: their spending has to go down, period. But beyond this, there is a further set of self-imposed financial constraints. Executive management at many utilities has determined that they cannot accept the risk, lack of financial leverage, and other consequences that high capital spending brings. This is because they feels their companies must improve their stockholder performance. As one utility executive said, "I will not put my company in financial jeopardy to keep our power system out of jeopardy." Many utilities have cut capital spending by 30 - 40% below traditional levels. Thus, modern utility planners face a variety of new pressures in charting their system's expansion to handle future demands. These are summarized in Table 1.2. The utility must do more with less. The "more" includes achieving specific levels reliability as measured by regulatory-defined formula or rules in the actual performance of the system. "The "less" includes less information: delivery planners can obtain all the information they used to from transmission and retail sources.

Table 1.2 Added Considerations for Modern T&D Planners Pressure Dis-aggregated T&D

Focus on reliability

Rate freezes/reductions

Reductions in capital

Consequence Power delivery planning is now "stand alone." It must produce explicit requests for wholesale delivery "feed points" into its locale delivery system. Delivery planners must consider bulk delivery and formalize their plans and requests, much like T&D utilities used to formalize generation plans. The utility must achieve certain levels of overall consumerlevel reliability or suffer certain financial and political consequences. Planners must plan a system that can deliver the required reliability. Often, the manner in which projects and spending are justified to regulators has changed, requiring more detail and a wider span of consideration. Reductions in operating cost often dictate reductions in planning staffs and support resources. Regulators and upper management alike have told planners they most "do much more with much less." There is 30 to 40% less capital to spend.

10

Chapter 1

Planning Consequences of De-Regulation Perhaps the largest shift away from traditional utility procedures in the new "do more with less" environment has been a strong move toward higher utilization rates for all system equipment. Traditionally, utilities loaded substation transformers to about 66% of capacity during normal operation - planning standards dictated that a transformer was "overloaded" if its forecast peak demand exceeded 66% of its normal load rating. Today that utilization rate hovers right at 80% for the industry, with some utilities actually operating at 90% or above. The increase in utilization rate was a direct result of financial pressures - an increase from 66% to 80% loading means that a transformer can serve 21% more load. Put another way, the money spent on that transformer will support 21% more revenue "flowing through it." Similarly, a host of other traditional standards and criteria have been "loosened" in an attempt to push the system harder and get more for less. This increase in equipment and system utilization rates and other associated changes in criteria has had a beneficial financial impact - spending at nearly every electric utility went down during the 1990s. But it often had undesirable effects on reliability, some of which were of a type that could not be assessed by traditional planning methods.1 In many cases the increases in utilization rates were not the direct cause of reliability problems, but were blamed for them. Power systems can operate reliably at very high (near 100%) peak utilization rates, but traditional standard-based planning methods and tools cannot engineer them so such performance is assured. An intense focus on reliability is the second important consequence of the modern paradigm. Some utilities have developed innovative programs to identify the causes of poor reliability, prioritize those causes and identify cures, and rank and select those curves that are most cost effective. However, a good many have bogged down trying to apply traditional planning methods to this new paradigm: they spend extensive effort measuring reliability and developing extensive "data mines" related to equipment reliability and service problems, but do very little that is effective in managing reliability with the resulting data. The key to success with regard to reliability (among those utilities that appear to be succeeding) is the move to a reliability-based planning paradigm. In this, traditional standards are made somewhat flexible: where a substation can be operated reliably at high utilization, that option is selected; where that would inhibit reliability, another option is taken. Results, in particular the expected reliability of the future system, rather than standards, control the planning process. In consequence T&D system planning puts less emphasis on capacity margin and far more on configuration, switching and "operability." 1

See Willis et al (2001) for more details.


11

Short-Range Planning and Its Forecasting Needs Short-range planning is that part of the planning process that assures lead time requirements will be meet. Short-range planning's purpose is to identify if something needs to be started now, or if the utility can defer action for another year. When action is needed (i.e., when the lead time has been reached), shortrange planning must identify, justify, authorize and schedule the required facilities or equipment. Thus, short-range planning must look into the future at least as far ahead as the lead time to assure that facilities of sufficient capability and location will be available when needed. Thus, if the lead time on substations is five years, then the T&D planner must plan at least five years ahead to assure that any new additions are ready when needed. For example, if it takes five years to permit, order equipment, construct, test and commission a new substation, then there is no reason to start that process more than five years ahead. But it must be started five years prior to need or it will not be available when required. Good or bad, the utility has to commit to construction or no construction at the lead time; waiting any longer means that it selects a "do nothing" or "wait another year" option by default. Lead times vary from level to level of a power system. Table 1.3 gives a rough idea of the lead time required by level of the power system, although they vary depending on region, utility, and regulatory environment.

Table 1.3 Typical Lead Times for T&D System Equipment Additions in an Electric Power System.

Level

Years Ahead

Nuclear generation (> 250 MVA) 10 Small generation (< 250 MVA) 4 Transmission (138 kV and above) 10 Transmission switching stations 7 Sub-transmission (34kV - 13 8kV) 6 Distribution substations 4 Control centers or dispatch centers 3 Primary three-phase feeders 3 Single-phase laterals 1 Distributed generation 1 Customer-site specific reliability equip. % 1 Service transformers and secondary A

Chapter 1

12

Short-range planning is action specific and project oriented Short-range planning is project-oriented, as shown in Figure 1.4. The initial short-range planning/forecasting process seeks only to identify where problems exist and by when additions or changes must be made to solve them. This initiates individual projects aimed at solving each problem, each project taking on a life of its own, as it were, to be completed in order to solve its designated problem. At many utilities these projects are not limited to planning in the purest sense (development and evaluation of feasible alternatives, leading to selection of the preferred option) but include actual detailed engineering, perhaps including design, and always generally leading to an authorization for construction. Short-range planning is action directed - it is intended to lead to specific actions (or a decision not to take them).

Load forecast for the lead time year(s)

Existing system & planned additions thru lead time

Short Range Planning Process,

Identified area capacity shortfalls

Figure 1.5 The short-range planning process consists of an initial planning phase in which the capability of existing facilities is compared to short-term (lead time) needs. Where capacity falls short of need, projects are initiated to determine how to best correct the situation. The load forecast is primarily an input to the short-range planning process - i.e., it is used to compare with existing capacity to determine if and where projects need to be initiated.


13

For short-range planning and its subsequent project development, the most important aspects of forecasting are accuracy in predicting the timing of future load growth - when it will reach levels that require something to be done to enhance system capability. Timing is truly critical at this stage for determining when these projects must be started (often involving commitment of considerable capital expense). There is a lead time, and if the timing is poorly estimated this might be missed. Thus, the most important role of the load forecast in supporting the shortrange planning is to sound a reliable alarm about when the present facilities will first be insufficient. Figure 1.6 illustrates the basic concept: the short-range forecast is a trigger which sets in motion the project(s) required to keep capability sufficient to deal with future load. Clearly, a second priority is identification of how much load growth should be anticipated, but this is not quite as important: once the project is triggered, it can focus as much attention as needed on trying to identify how much load will develop.

100

Peak Load 50 MVA

5 10 Years into the Future

15

Figure 1.6 Three projections of load growth for an area of 30 square miles agree on the eventual "build out" peak load but differ in the rate of growth. Existing facilities will be intolerably overloaded when the peak load reaches 57 MVA, and a new substation must be added, costing $6,500,000. A year's delay in this expense means a present worth savings of $500,000, but delaying a year too long means poor reliability and consumer dissatisfaction. Therefore, accuracy in predicting timing of growth - in knowing which of these three forecasts is most likely to be correct - is critical to good planning.

14

Chapter 1

Long-Range Planning and Forecasting Needs Long-range planning looks beyond the lead time in order to assure that the shortrange decisions provide lasting value. Unlike short-range planning, long-range planning is not concerned with ensuring that the system additions are made hi time to meet needs. It is done to assure that those additions have lasting investment and performance value. For example, planner's might have decided to meet the needs forecast load growth in an area of the system by adding a new 50 MVA substation five years from now. However, if continued growth is expected after that, it might require expansion only a few years later, at a considerable additional cost. It might be more economical to install a larger facility initially, or to design the substation so it can be quickly upgraded at low cost when needed. Beyond that, if growth in the area is expected to continue, it might be best to build the substation at a location other than what is optimum from only the five-year ahead perspective, perhaps locating it closer to the expected center of long-term load growth. Each of these questions requires looking at the substation in the period after it is installed and operating — beyond the lead time required to build it. To make certain they leave no "if only we had . . . " regrets, the substation's planners must study it over a period that begins on the day it goes into service and includes a reasonable portion of its lifetime — at least the first ten to fifteen years. (Such a ten- to fifteen-year period represents a majority of the substation's present worth on the day it is put in service. To meet this need, the load forecast must provide a projection of load growth for ten or more years beyond the lead time. Table 1.4 gives typical long-range planning periods for T&D equipment.

Table 1.4 Typical Long-Range Periods For T&D System Equipment Planning In An Electric Power System. Level

Years Ahead

Large generation (> 250 MVA) Small generation (< 250 MVA) Transmission/switching (138 kV and above) Sub-transmission (34kV - 138kV) Distribution substations Distribution feeders Renewable generation Distributed generation Customer site - specific reliability equipment

30 20 25 20 20 15 20 7 4


15

The Long-Range Plan To study facilities or other options in detail over the period five to twenty-five years ahead, the utility's planners must have a good idea of the conditions under which that equipment will function. In other words, they need a long-range plan to provide a backdrop against which they can evaluate the value of their shortrange projects. This plan must lay out the economics (e.g., value of losses) and operating criteria that will apply in the future. It must specify the load and reliability needs that the system will have to meet (long-range demand forecast), and the locations of this new demand. Finally, it must specify how the T&D planners expect to handle that load growth - what other new facilities will be installed in the long run to accommodate the future pattern of load growth. Going back to the example substation discussed above, a great deal of its value to the utility will depend on what other new substations will or will not be built nearby during its lifetime. The purpose of long-range planning is to provide assessment of the wisdom, or optimality if one prefers, of the short-range decisions — those commitments that have to be made now. Its value is the improvement in investment value bang-for-the-buck, return on assets, lifetime utilization or however measured — that this evaluation will provide. Long-range planning should be judged by how it contributes to improving the utilization (value) of existing facilities and the long-term value of new investments in the system. For this reason, long-range planning does not lead to a set of projects as does short-range planning. In fact, the long-range planning process does not lead directly to any actions, nor will it ever "authorize" any projects. Its only "product" is the long-range plan itself: the material needed and foundation for that long-range assessment of short-range decisions. Development and maintenance of the long-range plan is the major goal of the long-range planning process: a continuously updated long-range vision of needs and plans that provides a basis for evaluation, and guidelines and direction to short-range planning. This is shown in Figure 1.7. The long-range plan is used to judge the effectiveness of all short-range commitments. As an example, consider the substation required to be built in five years, discussed earlier in this chapter. Perhaps the long-range plan shows that while load growth in the area will continue, it will be handled in the long run by other new substations, to be added later. In such a case, the substation need be built only in its initial configuration and any plans to expand it or build it to a larger size initially can be considered to have a low priority compared to cutting initial cost as much as possible. The long-range plan needs only detail sufficient to permit resolution of questions such as that, to study the economics and "fit" of short-range decisions against the systems long-range trends and needs. Effort to provide detail and precision of sites, routes, and exact equipment description beyond this is wasted

16

Chapter 1

Spatial Load Forecast

Existing System & Committed Additions

Long-Range Planning Process

for evaluation of possible short range projects

possible revision of plan Long-Range Plan

Figure 1.7 The long-range planning process, where the major goal is the maintenance of a long-range plan that identifies needs and development of the system, so that short-range decisions can be judged against long-range goals. Here, the load forecast is a part of the plan, and there is no "project oriented" part of the activities.

100

Peak Load 50

MVA

5

10

15

Years into the Future Figure 1.8 Long-range planning requirements are less concerned with "when" and more sensitive to "how much will eventually develop." The three forecasts shown here, all for the same substation area discussed in Figure 1.4, lead to the conclusion that the new substation is needed in five years, but differ in their projections of the long-term load. They would lead to far different conclusions about how short-range commitments can match long-range needs, and probably to a different decision about what to do in the short run as well as the long run.


17

wasted (Tram et al., 1983). As a result, long-range T&D planning requirements for a spatial load forecast are oriented less toward the "when" aspect (as was short-range planning) and more toward the "what" and "how much," as shown in Figure 1.8. For long-range planning, knowing what will eventually be needed is more important than knowing exactly when. Long-range plans can be moved forward or back in time as needed, but what hurts is if the overall plan is moving toward the wrong "picture" of future needs, calling for too much or too little, or just enough, but in the wrong locations. Thus, timing is not a priority in a long-range plan, because most of the elements of a long-range plan do not have to be built anytime soon. By definition, its timeframe is beyond the lead time. Consider a new substation specified for construction by the long-range plan for eleven years from the present. Given the five-year lead time required, the utility has another six years before it must commit to that substation's construction. In the meantime conditions or expectations may change. Thus, no action must be taken anytime soon. The substation and other elements exist in the plan only to show the planners how they will meet long-term needs, so that they know what is and is not expected of the substation and other equipment that must be committed now. Temporal Resolution Requirements Temporal resolution is the when of the forecast, the degree of detail on timing of future growth in a forecast. (See Figure 1.9). Most forecasts have a temporal resolution of one year - they forecast future load growth in a series of one-year increments. This is true even if they generate hourly load curves, seasonal peaks, or other time-related "details." Those details are based on one-year "snapshots" of growth generated by the forecasts. Daily load curves, and seasonal peaks, for example, are based upon projections of conditions in that year. One-year resolution is generally sufficient for T&D planning (given that those "details" are supplied, because planning is done on an annual basis. Generally, as a planning study moves into the long-range time period, it will skip selected years, spacing out the time between "snapshots" of the future. A typical forecast, and the planning based on it, therefore includes a base year (calibrated study of present load), a forecast for years 1, 2, 3, 4, 5 ahead (shortrange period), and a forecast for years 7, 9, 12, 15, and 20 years ahead, for longrange analysis. This satisfies both short- and long-range planning needs Spatial Resolution Requirements Spatial resolution is the where of a load forecast. Conceptually, spatial or geographic resolution is the "area size" that the forecast uses to locate load and growth. A forecast that identifies load on an acre basis has more resolution than

Chapter 1

18 20 years ahead 15 years ahead 12 years ahead I 9 years ahead 7 years ahead | 5 years ahead | 4 years ahead | 3 years ahead | 2 years ahead | 1 year ahead I Base year data

Figure 1.9 Both planning and forecasting are usually done with a temporal resolution of one year - forecast "snapshots" of peak conditions in future years. T&D planning involves examining expansion needs for a series of future years in both the short- and long-range periods. The load forecast must accommodate these needs, providing forecasts - maps of projected load density throughout the service territory - for the years to be planned.

one that locates load and growth only to within square miles. The spatial resolution required in planning varies from level to level of a power system, being roughly proportional to the capacity of the power equipment involved. A typical distribution substation (primary voltage 12.47 kV) might serve an area of 25 square miles. A distribution feeder serves a much smaller area, perhaps only 4 square miles. In every sense, feeder planning will be more sensitive to detail about where load is located than the substation planning. How this sensitivity to spatial detail varies with level of the system will be discussed in Chapter 8. But qualitatively, levels of the system composed of larger equipment serving larger service areas can be planned with lower resolution spatial resolution, while levels composed of smaller, lower voltage equipment generally require more detail about where load is located.

19

Spatial Electric Load Forecasting Table 1.5 Typical Service Area Sizes For T&D System Equipment In An Electric Power System And Spatial Resolution Needed To Model Their Loads. Service area - mi.2

Level Large generation (> 150 MVA) Small generation (< 150 MVA) Transmission (138 kV and above) Sub-transmission (34kV - 138kV) Distribution substations Primary three-phase feeders Single-phase laterals Service transformers and secondary Distributed generation (« 50 kW)

entire system 50 100 50 30 5 .25 .01 .01

Resolution - mi.2 entire system 4 9 4 2 .5 .02 consumer consumer

Entire System

M

M

01

o> o

e

[

53

0>

£ 5

-

•^

8 B [-] 1 LN

"r"""~~ffl~7Jrini

ll 1 irll Btf Dlliln li Substations

Transmission

-i

o» o

u u Major equipment

Dis tribution

K.

ft 0

Spatial Resolution - acres

Generation

0

5

10 15 Years Ahead

20

25

Figure 1.10 Planning period and spatial resolution for various levels of the power system are inversely related. Forecasts for "large equipment" levels of the system must be done farther into the future, but require lower spatial resolution than lower voltage/size levels of the distribution system.

20

Chapter 1

Table 1.5 gives typical service area sizes for various levels of the power system, along with resolution requirements (area size to which load levels must be studied on an individual basis to assure adequate planning of the level). These differences in service area sizes by level of the power system mean that, while larger equipment levels require planning farther into the future as was shown earlier in Tables 1.3 and 1.4, the geographic detail required is less. Thus, load forecasting requirements farther into the future generally require less spatial resolution than short-range forecasts, as shown in Figure 1.10. Uncertainty and Multi-Scenario Planning Planning and forecasting always face uncertainty about future developments. Will the economy continue to expand so that load growth will develop as forecasted? Will a possible new factory (employment center) develop as rumored, causing a large and as yet unforecasted increment of growth? Will the bond election approve the bonds for a port facility (which would boost growth and increase load growth)? Situations like these confront nearly every planner. Those of most concern to the distribution planner are factors that will increase growth and change its location. In the presence of uncertainty about the future, utility planners face a dilemma. They do not want to commit to investment that may not be needed. However, they cannot ignore the fact that the development could happen, and that they must commit soon if they are to fit within the lead time so they have the facilities in place if the growth does occur. Given the reality of lead times, planners must sometimes commit without certainty that the events they are planning for will, in fact, come to pass. A clearly desirable goal of the planning process is to minimize the risk due to uncertainty. Ideally, plans can be developed to confront any, or at least the most likely, eventualities, as illustrated in Figure 1.11. Multiple long-range plans, all stemming from the same short-range decisions (decisions that must be made because of lead times) cover the various possible events. This type of planning, called multi-scenario planning, involves explicit enumeration of plans to cover the various likely outcomes of future events. One of the worst mistakes that can be made in T&D planning, integrated or otherwise, is to try to circumvent the need for multi-scenario planning by using "average" or "probabilistic forecasts." Generally, this approach leads to plans that combine poor performance with high cost. As an example, the forecast maps in Figure 1.12 show forecasts of spatial distribution of load for a large city. Shown at the top are maps of load in 1994 and as projected for twenty years later. The difference in these maps represents the task ahead of the T&D planners - they must design a system to deliver an additional 1,132 MW, in the geographic pattern shown.


21 Neither

Event A

Event B

Time period during vhich commitments must be made now Present

+ 4 gears

Events A andB

+ 8 years

+12 years

Figure 1.11 Multi-scenario planning uses several long-range plans to evaluate the shortrange commitments, assuring that the single short-range decision that is being made fits the various long-range situations that might develop. This shows the scenario variations for the case involving two possible future events.

At the bottom of Figure 1.12 are two alternate forecasts that include a possible new "theme park and national historical center" built at either of two possible sites. Given voter approval (unpredictable) and certain approvals and incentives from state officials (likely, but not certain), the theme park/resort developer would build at one of the two sites. The park itself would create only 11.3 MW, of new load (it has some on-site generation to serve its 25 MW peak load). But it would generate 12,000 new jobs and bring other new industries to the area, causing tremendous secondary and tertiary growth, and leading to an average annual growth rate of about 1% more than in the base forecast during the decade following its opening. Thus, the theme park means an additional 260 MW of growth on top of the already healthy expansion of this city. The maps in Figure 1.12 show where and how much growth is expected under each of three scenarios: no park (considered 50% likely), site A (25%), or site B (25% likely). The spatial distribution of the secondary and tertiary growth will be very much a function of the new park location. Hence, the where aspect, as well as the how much, of the utility's T&D plan will change, too. Feeders, substations, and transmission will have to be relocated and re-sized to fit the scenario that develops. The planners need to study the T&D needs of each of the three growth scenarios and develop a plan to serve that amount and geographic pattern of load. Ideally, they will be able to develop a short-range plan that "branches"

Chapter 1

22

2O13 - Base Forecast 3442 MV •'•'.&>

4

(0

•a

9) 0)

3

O X

111 n o

2 I

2 | A

8760 Hours per Year

Figure 2.9 Solid line indicates the load duration curve for a typical 5 kW residential water heater. Compare to Figures 3.3 and 3.4. Dotted line shows the load duration curve that would result from a 2 kW water heater serving the same end-use demand.

Chapter 2

48

Spatial Patterns of Electric Demand An electric utility must not only produce or obtain the power required by its consumers, but also must deliver it to their locations. Electric consumers are scattered throughout the utility service territory, and thus the electric load can be thought of as distributed on a spatial basis as depicted in Figure 2.10. Just as load curves show how electric load varies as a function of time (and can help identify when certain amounts of power must be provided), so spatial load analysis helps identify -where load growth will be located and how much capacity will be needed in each locality. The electric demand in an electric utility service territory varies as a function of location depending on the number and types of consumers in each locality, as shown by the load map in Figure 2.10. Load densities in the heart of a large city can exceed 1 MW/acre, but usually average about 5 MW per square mile over the entire metropolitan area. In sparsely populated rural areas, farmsteads can be as far as 30 miles apart, and load density as low as 75 watts per square mile. Chapter 7 and Table 7.1 gives more details on typical load densities in various types of areas. Regardless of whether an area is urban, suburban, or rural, electric load is a function of the types of consumers, their number, their uses for electricity, and the appliances they employ. Other aspects of power system performance, including capability, cost, and reliability, can also be analyzed on a location basis, an often important aspect of siting for facilities, including DG.

" LcwJensRes. i Resifenlnl || Aptrtntnts

Ljl M

Med.W Hvykid.

Figure 2.10 (Left) Map showing types of consumer by location for a small city. (Right) Map of electric demand for this same city.


49

2.3 ELECTRIC CONSUMER DEMAND FOR QUALITY OF POWER As mentioned in this chapter's introduction, a central issue in consumer value of service analysis is matching availability and power quality against cost. T&D systems with near perfect availability and power quality can be built, but their high cost will mean electric prices the utility consumers may not want to pay, given the savings an even slightly less reliable system would bring. All types of utilities have an interest in achieving the correct balance of quality and price. The traditional, franchised monopoly utility, in its role as the "electric resource manager" for the consumers it serves, has a responsibility to build a system whose quality and cost balances its consumers' needs. A competitive retail distributor of power wants to find the best quality-price combination - only in that way will it gain a large market share. While it is possible to characterize various power quality problems in an engineering sense, characterizing them as interruptions, voltage sags, dips, surges, or harmonics the consumer perspective is somewhat different. Consumers are concerned with only two aspects of service quality: 1. They want power when they need it. 2. They want the power to do the job. If power is not available, neither, aspect is provided. However, if power is available, but quality is low, only the second is not provided. Assessing Value of Quality by Studying the Cost of a Lack of It In general, consumer value of reliability and service quality are studied by assessing the "cost" that something less than perfect reliability and service quality creates for consumers. Electricity provides a value, and interruptions or poor power quality decrease that value. This value reduction - cost - occurs for a variety of reasons. Some costs are difficult if not impossible to estimate: rescheduling of household activities or lack of desired entertainment when power fails;3 or flickering lights that make reading more difficult. But often, very exact dollar figures can be put on interruptions and poor power quality. Food spoiled due to lack of refrigeration; wages and other operating costs at an industrial plant during time without power; damage to product caused by the sudden cessation of power; lost data and "boot up" time for computers; equipment destroyed by harmonics; and so forth. Figure 2.11 shows two examples of such cost data. 3

No doubt, the cost of an hour-long interruption that began fifteen minutes from the end of a crucial televised sporting event, or the end of a "cliffhanger" movie, would be claimed to be great.

Chapter 2

50 20

sf 40,

15

o * 30

10

20!

o

'0

15 30 45 60 75 90 Interruption Duration - Minutes

0 10 20 30 Total Harmonic Voltage Distortion - %

Figure 2.11 Left, cost of a weekday interruption of service to a pipe rolling plant in the southeastern United States, as a function of interruption duration. An interruption of any length costs about $5,000 - lost wages and operating costs to unload material in process, bring machinery back to "starting" position and restart - and a nearly linear cost thereafter. At right, present worth of the loss of life caused by harmonics in a 500horsepower three-phase electric motor installed at that same industrial site, as a function of harmonic voltage distortion.

Value-Based Planning To be of any real value in utility planning, information of the value consumers put on quality must be usable in some analytical method that can determine the best way to balance quality against cost. Value-based planning (VBP) is such a method: it combines consumer-value data of the type shown in Figure 2.11 with data on the cost to design the T&D system to various levels of reliability and power quality, in order to identify the optimum balance. Figure 2.12 illustrates the central tenet of value-based planning. The cost incurred by the consumer due to various levels of reliability or quality, and the cost to build the system to various levels of reliability, are added to get the total cost of power delivered to the consumer as a function of quality.4 The minimum value is the optimum balance between consumer desire for reliability and aversion to cost. This approach can be applied for only reliability aspects, i.e., value-based reliability planning, or harmonics, or power quality overall. Generally, what makes sense is to apply it on the basis of whatever qualities (or lack of them) impact the consumer - interruptions, voltage surges, harmonics, etc., in which case it is comprehensive value-based quality of service planning.

4

Figure 2.12 illustrates the concept of VBP. In practice, the supply-side reliability curves often have discontinuities and significant non-linearities that make application difficult.

Consumer Demand for Power and Reliability Customer Vaue of Odrty

§

51

Utility Cost of ProvidngCldity

Sun of Costs

(A O O

o

Low

Low

Qjalily

He*

OdHy

ION Qelity

Figure 2.12 Concept of value-based planning. The consumer's cost due to poorer quality (left) and the cost of various power delivery designs with varying levels of quality (center) are computed over a wide range. When added together (right) they form the total cost of quality curve, which identifies the minimum cost reliability level (point A).

Cost of Interruptions The power quality issue that affects the most consumers, and which receives the most attention, is cessation of service, often termed "service reliability." Over a period of several years, almost all consumers served by any utility will experience at least one interruption of service. By contrast, a majority will never experience serious harmonics, voltage surge, or electrical noise problems. Therefore, among all types of power quality issues, interruption of service receives the most attention from both the consumers and the utility. A great deal more information is available about cost of interruptions than about cost of harmonics or voltage surges. Voltage Sags Cause Momentary Interruptions The continuity of power flow does not have to be completely interrupted to disrupt service: If voltage drops below the minimum necessary for satisfactory operation of an appliance, power has effectively been "interrupted" as illustrated in Figure 2.13. For this reason many consumers regard voltage dips and sags as momentary interruptions - from their perspective these are interruptions of the end-use service they desire, if not of voltage. Much of the electronic equipment manufactured in the United States, as well as in many other countries has been designed to meet or exceed the CBEMA

Chapter 2

52

Figure 2.13 Output of a 5.2-volt DC power supply used in a desktop computer (top) and the incoming AC line voltage (nominal 113 volts). A voltage sag to 66% of nominal causes power supply output to cease within three cycles.

200

£ 150 ra

I .2 100

50

1

10'

10"°

101

102

10J

104

105

106

10'

Time in Cycles

Figure 2.14 CBEMA curve of voltage deviation versus period of deviation, with the sag shown in Figure 2.13 plotted (black dot).


53

(Computer and Business Equipment Manufacturer's Association) recommended curves for power continuity, shown in Figure 2.14. If a disturbance's voltage deviation and duration characteristics are within the CBEMA envelope, then normal appliances should operate normally and satisfactorily. However, many appliances and devices in use will not meet this criterion at all. Others will fail to meet it under the prevailing ambient electrical conditions (i.e., line voltage, phase unbalance power factor and harmonics may be less than perfect). The manner of usage of an appliance also affects its voltage sag sensitivity. The voltage sag illustrated in Figure 2.13 falls just within the CBEMA curve, as shown in Figure 2.14. The manufacturer probably intended for the power supply to be able to withstand nearly twice as long a drop to 66% of nominal voltage before ceasing output. However, the computer in question had been upgraded with three times the standard factory memory, a second and larger hard drive, and optional graphics and sound cards, doubling its power usage and the load on the power supply. Such situations are common and this means that power systems that deliver voltage control within recommended CBEMA standards may still provide the occasional momentary interruption. For all these reasons, there are often many more "momentary interruptions" at a consumer site than purely technical evaluation based on equipment specifications and T&D engineering data would suggest. Momentary interruptions usually cause the majority of industrial and commercial interruption problems. In addition, they can lead to one of the most serious consumer dissatisfaction issues. Often utility monitoring and disturbance recording equipment does not "see" voltage disturbances unless they are complete cessation of voltage, or close to it. Many events that lie well outside the CBEMA curves and definitely lead to unsatisfactory equipment operation are not recorded or acknowledged. As a result, a consumer can complain that his power has been interrupted five or six times in the last month, and the utility will insist that its records show power flow was flawless. The utility's refusal to acknowledge the problem irks most consumers more than the power quality problem itself. Frequency and Duration of Interruptions Both Impact Cost Traditional power system reliability analysis concepts recognize that service interruptions have both frequency and duration (see Chapter 4). Frequency is the number of times during some period (usually a year) that power is interrupted. Duration is the amount of time power is out of service. Typical values for urban/suburban power system performance in North America are about 2 interruptions per year with about 100 to 120 minutes total duration. Both frequency and duration of interruption impact the value of electrical service to the consumer and must be appraised in any worthwhile study of consumer value of service. A number of reliability studies and value-based

54

Chapter 2

planning methods have tried to combine frequency and duration in one manner or another into "one dimension." A popular approach is to assume all interruptions are of some average length (e.g., 2.2 interruptions and 100 minutes is assumed to be 2.2 interruptions per year of 46 minutes each). Others have assumed a certain portion of interruptions are momentary and the rest of the duration is lumped into one "long" interruption (i.e., 1.4 interruptions of less than a minute, and one 99-minute interruption per year). Many other approaches have been tried (see References and Bibliography). But all such methods are at best an approximation, because frequency and duration impact different consumers in different ways. No single combination of the two aspects of reliability can fit the value structure of all consumers. Figure 2.15 shows four examples of the author's preferred method of assessing interruption cost, which is to view it as composed of two components, a fixed cost (Y intercept) caused when the interruption occurred, and a variable cost that increases as the interruption continues. As can be seen in Figure 2.15, consumer sensitivity to these two factors varies greatly. The four examples are:

1.

A pipe-rolling factory (upper left). After an interruption of any length, material in the process of manufacturing must be cleared from the welding and polishing machinery, all of which must be reset and the raw material feed set up to begin the process again. This takes about 1/2 hour and sets a minimum cost for an interruption. Duration longer than that is simply a linear function of the plant operating time (wages and rent, etc., allocated to that time). Prior to changes made by a reliability study, the "re-setting" of the machinery could not be done until power was restored (i.e., time during the interruption could not be put to use preparing to re-start once it was over). The dotted line shows the new cost function after modifications to machinery and procedure were made so that preparations could begin during the interruption.

2.

An insurance claims office (upper right) suffers loss of data equivalent to roughly one hour's processing when power fails. According to the site supervisor, an unexpected power interruption causes loss of about one hour's work as well as another estimated half hour lost due to the impact of any interruption on the staff. Thus, the fixed cost of each interruption is equivalent to about ninety minutes of work. After one-half hour of interruption, the supervisor's policy is to put the staff to work "on other stuff for a while," making cost impact lower (some productivity); thus, variable interruption cost goes down. The dotted line shows the cost impact

55


Insurance Claims Office

Pipe Rolling Plant

5

20

o

15

o o x *«•

10

~ o o

0

4 3

2

0

15 30 45 60 75 90 Interruption Duration - minutes

Chemical Process Plant


Residence

400

40

300

30

8 20

200

o

10

100

0


0


Figure 2.15 The author's recommended manner of assessing cost of interruptions includes evaluation of service interruptions on an event basis. Each interruption has a fixed cost (Y-intercept) and a variable cost, which increases as the interruption continues. Examples given here show the wide range of consumer cost characteristics that exist. The text gives details on the meaning of solid versus dotted lines and the reasons behind the curve shape for each consumer.

56

Chapter 2 of interruptions after installation of an uninterruptible power supply (UPS) on the computer system, which permits orderly shutdown in the event of an interruption. 3.

An acetate manufacturing and processing plant (lower left) has a very non-linear cost curve. Any interruption of service causes $38,000 in lost productivity and after-restoration set-up time. Cost rises slowly for about half an hour. At that point, molten feedstock and interim ingredients inside pipes and pumps begin to cool, requiring a daylong process of cleaning hardened stock from the system. The dotted line shows the plant's interruption cost after installation of a diesel generator, started whenever interruption time exceeds five minutes.

4.

Residential interruption cost function (lower right), estimated by the author from a number of sources including a survey of consumers made for a utility in the northeastern United States in 1992, shows roughly linear cost as a function of interruption duration, except for two interesting features. The first is the fixed cost equal to about eight minutes of interruption at the initial variable cost slope, which reflects "the cost to go around and re-set our digital clocks," along with similar inconvenience costs. Secondly, a jump in cost between 45 and 60 minutes, which reflect inconsistencies in human reaction to outage, time on questionnaires. The dotted line shows the relation the author uses in such analysis, which makes adjustments thought reasonable to account for these inconsistencies.

This recommended analytical approach, in which cost is represented as a function of duration on a per event basis, requires more information and more analytical effort than simpler "one-dimensional" methods, but the results are more credible. Interruption Cost Is Lower if Prior Notification Is Given Given sufficient time to prepare for an interruption of service, most of the momentary interruption cost (fixed) and a great deal of the variable cost can be eliminated by many consumers. Figure 2.16 shows the interruption cost figures from Figure 2.15 adjusted for "24-hour notification given." This is the cost for "scheduled outages." Cost of Interruption Varies by Consumer Class Cost of power interruption varies among all consumers, but there are marked distinctions among classes, even when cost is adjusted for "size" of load by computing all cost functions on a per kW basis. Generally, the residential class has the lowest interruption cost per kW and commercial the highest. Table 2.1

57

Consumer Demand for Power and Reliability Table 2.1 Typical Interruption Costs by Class for Three Utilities - Daytime, Weekday (dollars per kilowatt hour) Class Agricultural Residential Retail Commercial Other Commercial Industrial Municipal

1

2

3

3.80 4.50 27.20 34.00 7.50 16.60

4.30 5.30 32.90 27.40 11.20 22.00

7.50 9.10 44.80 52.10 13.90 44.00

Insurance Claims Office

Pipe Rolling Plant

20

§ 15

o X

~ 10 8 0

5 0

0


Chemical Process Plant


Residence

§ 300

8 20

u

10 0


'0


Figure 2.16 If an interruption of service is expected, consumers can take measures to reduce its impact and cost. Solid lines are the interruption costs (the solid lines from Figure 2.15). Dotted lines show how 24-hour notice reduces the cost impact in each case.

58

Chapter 2

gives the cost/kW of a one-hour interruption of service by consumer class, obtained using similar survey techniques for three utilities in the United States: 1. A small municipal system in the central plains, 2. An urban/suburban/rural system on the Pacific Coast 3. An urban system on the Atlantic coast.

Cost Varies from One Region to Another Interruption costs for apparently similar consumer classes can vary greatly depending on the particular region of the country or state in which they are located. There are many reasons for such differences. The substantial difference (47%) between industrial costs in utilities 1 and 3 shown in Table 2.1 is due to differences in the type of industries that predominate in each region. The differences between residential costs of the regions shown reflect different demographics and varying degrees of economic health in their respective regions. Cost Varies among Consumers within a Class The figures given for each consumer class in Table 2.1 represent an average of values within those classes as surveyed and studied in each utility service territory. Value of availability can vary a great deal among consumers within any class, both within a utility service territory and even among neighboring sites. Large variations are most common in the industrial class, where different needs can lead to wide variations in the cost of interruption, as shown in Table 2.2. Although documentation is sketchy, indications are the major differing factor is the cost of a momentary interruption - some consumers are very sensitive to any cessation of power flow, while others are impacted only by an interruption of power longer than a few minutes. Cost of Interruption Varies as a Function of Time of Use Cost of interruption will have a different impact depending on the time of use, usually being much higher during times of peak usage, as shown in Figure 2.19. However, when adjusted to a per-kilowatt basis, the cost of interruption can sometimes be higher during off-peak than during peak demand periods, as shown. There are two reasons. First, the data may not reflect actual value. A survey of 300 residential consumers for a utility in New England revealed that consumers put the highest value on an interruption during early evening (Figure 2.17). There could be inconsistencies in the values people put on interruptions (data plotted were obtained by survey).

59


Table 2.2 Interruption Costs by Industrial Sub-Class for One hour, Daytime, Weekday (dollars per kilowatt) Class

$/kW

Bulk plastics refining Cyanide plant Weaving (robotic loom) Weaving (mechanical loom Automobile recycling Packaging Catalog distribution center Cement factory

38 87 72 17 3 44 12 8

25 20 Q. 2 15

:;S§: mwSM:^:^fifff] >

'

Thro« mil»£

Lines indicate highways and roads

Figure 2.21 Map of average reliability needs computed on a 10-acre small area grid basis for a port city of population 130,000, using a combination of an end-use model and a spatial consumer simulation forecast method, of the type discussed in Chapters 10 - 16. Shading indicates general level of reliability need (based on a willingness-to-pay model of consumer value).

2.4 TWO-Q ANALYSIS: QUANTITY AND QUALITY VERSUS COST The amount of power used and the dependability of its availability for use are both key attributes in determining the value of the electric power to each consumer. The values attached to both quantity and quality by most consumers are linked, but each is somewhat independent of the value attached to the other. The "two-dimensional" load curve shown in Figure 2.19 is a two-Q load curve, giving demand as a function of time hi both Q dimensions. In order to demonstrate this, it is worth considering two nearly ubiquitous appliances, which happen to represent opposite extremes of valuation in each of these dimensions: the electric water heater and the personal computer. A typical residential storage water heater stores about 50 gallons of hot water and has a relatively large connected load compared to most household appliances: about 5,000 watts of heating element controlled by a thermostat. Its contribution to coincident peak hourly demand in most electric systems is about 1,500 watts. The average heater's thermostat has it operating about 30% of the time (1,500/5,000) during the peak hour.

Chapter 2

66

Despite its relatively high demand for quantity, this appliance has a very low requirement for supply quality. A momentary interruption of electric flow to this device - on the order of a minute or less - is literally undetectable to anyone using it at that moment. Interruptions of up to an hour usually create little if any disruption in the end-use the device provides, which is why direct load control of water heaters is an "economy discount" option offered by many utilities. Similarly, the water heater is not critically dependent on other aspects of power quality. It will tolerate, and in fact turn into productive heat, harmonics, spikes, and other "unwanted" contamination of its supply. By contrast, a typical home computer requires much less quantity, about 180 watts. But unlike the water heater, it is rendered useless during any interruption of its power supply. Just one second without power will make it cease operation. Battery backup power supplies, called un-interruptible power supplies, can be used to avoid this problem, but at an increase in the computer's initial and

High XJ

Space heating Water heater

.1 '3 cr 0)

o

Refrigerator

CL

c ro

Computer

3

o Low High

Low

Need for Continuity of Service Figure 2.22 Electrical appliances vary in the amount of electricity they demand, and the level of continuity of service they require to perform their function adequately. Scales shown here are qualitative. Typically quantitative scales based on the most applicable consumer values are used in both dimensions.

67


O)

if

peoi

High

Low

Energy

High

Low

Load

Figure 2.23 A Two-Q diagram of a consumer's needs can be interpreted in either of two dimensions to identify the total (area under the curve) and range of needs in either Q direction, producing Q profiles of the importance of demand, or reliability to his particular needs.

operating cost. In addition to its sensitivity to supply availability, a computer is also more sensitive to harmonics, voltage dips, and other power quality issues than a water heater. Figure 2.22 shows these two appliances and several other household energy uses plotted on a two-dimensional basis according to the relative importance of both quantity and quality. This Two-Q load plot is a type of two-dimensional demand and capability analysis that treats both Qs as equally important. Traditionally, "demand" has been viewed only as a requirement for quantity of power, but as can be seen the true value of electric service depends on both dimensions of delivery. Recognition of this fact has been the great driver in moving the electric industry toward "tiered rates," "premium rates," and other methods of pricing power not only by amount and time of use, but by reliability of delivery. Either or both dimensions in the plot can be interpreted as single-dimensional profiles for quantity or quality, as the case may be, providing useful information in the analysis of consumer needs and how to best meet them (Figure 2.23).

68

Chapter 2

Two-Q Planning and Engineering Two-Q analysis using these same two dimensions also applies to the design of utility power systems. This section will only summarize Two-Q planning and engineering so that its capabilities, and how load forecasting and analysis interacts with them, can be understood. The essence of Two-Q planning is to add cost as a third dimension, and then "optimize" either rigorously or through trial, error, and judgement, until the most effective cost to obtain both Q target levels is obtained. Figure 2.24 shows the "options" for development of an existing T&D system, in this case in a growing rural area of the southern United States, currently serving a peak load of about 2 MW/square mile with a SAIDI (System Average Interruption Duration Index) of 114 minutes (indicated by a dot). A series of design studies were used to form the manifold (surface) shown. It indicates what the cost/kW would be if peak load or reliability performance levels were changed from current levels. This manifold gives a rough idea of the cost of building and operating a modified version of the existing system. (Only a rough idea, because the manifold was interpolated from a total of eight design studies. Once this has been used as a rough guide in planning target designs, specific studies then hone the plan and selection of specific detailed design elements).

Figure 2.24 Three-dimensional Two-Q plot of a power system's capabilities, in which cost has been added as the third consideration. The manifold (surface) shown represents the best possible cost of a 138 kV/12.47kV system if re-designed and optimized for various combinations of amount of power and target reliability level in a well-managed and planned system. Dot indicates 2 MW density and 114 minutes SAIDI,


69

Figure 2.25 Left - the capabilities of a 25 kV distribution system are shown by a second manifold. Right — comparison of the two manifolds results in a Two-Q diagram that shows where each system approach is most cost effective. The results shown are from a real study, but one with a number of real-world constraints. As a result the manifold shape and results shown are not generalizable, although the approach is.

Figure 2.25 shows a second manifold plotted along with the first. This second manifold represents the costs and capabilities of using a 25 kV primary distribution system for some future construction, the 25 kV being used on a targeted basis in the area for large loads and for "express" service to areas where reliability is a challenge with 12.47 kV. The plot indicates that the 25 kV design paradigm offers lower costs at higher load densities and is more expensive at lower load levels. To the right is shown a diagram developed from this data, which shows which Two-Q situations are best-served by 12.47 kV or 25 kV systems. Inspection of the shapes of the manifolds in Figures 2.24 and 2.25 will indicate to the reader the importance of accurate load and reliability targets for planning. The slopes of each manifold are steep in places — small errors in estimating the demand for either Q will generate substantial displacements in the cost estimates used in planning. Thus good forecasts are important. Further, hhe distance between the manifolds is small in many places, and the angle of divergence from where they meet is small — thus small errors in estimating the demand for either Q will yield substantial mistakes in selecting the optimal approach. The reader should note that this sensitivity is not a characteristic of the Two-Q method - the figures above are merely illustrating the cost and decision-making realities of distribution planning, sensitivities that

70

Chapter 2

would be there regardless of the method used to engineering reliability and demand capability into the system. Therefore, the lesson for the forecaster is that accurate projections of future load levels are crucial, in achieving both good reliability and cost. While this concept is fairly obvious to any experienced T&D planner, the Two-Q approach can quantitatively measure, analyze, and help manage these issues. 2.5 CONCLUSION AND SUMMARY The whole purpose of electric utility systems is to meet the electric demands of individual households, businesses, and industrial facilities. Two useful mechanisms for evaluation of consumer requirements and planning for their provision are end-use analysis and Two-Q planning which provide a useful basis for the study and analysis of both quantity and quality. In addition to having a demand for electric energy, electric consumers value availability and quality of power too, seeking satisfactory levels of both the amount of power they can obtain and the availability of its supply. Each consumer determines what is satisfactory based on specific needs. An interruption that occurs when a consumer does not need power is of no concern. Harmonics or surges that make no impact on appliances are not considered of consequence. Among the most important points in consumer value analyses are: 1.

Interruption of service is the power quality problem that has the widest effect. A majority of electric consumers in most utility systems experience unexpected interruptions at least once a year.

2.

Voltage dips and sags create momentary interruptions for many consumers that are indistinguishable in impact on their needs with short-term interruptions of power flow.

3.

Analysis of consumer needs for power quality is generally done by looking at the cost which less than perfect quality creates, not the value that near-perfect quality provides. Thus, availability of power is usually studied by analyzing the cost incurred due to interruptions, and determining the costs created by those interruptions assesses harmonics.

4.

Power quality costs and values vary greatly depending on consumer class, time of day and week, region of the country, and individual characteristics.

5.

Price concerns often outweigh quality concerns. Throughout the late 1980s and early 1990s, several independent studies indicated that roughly "30%-40% of commercial and industrial consumers are willing to pay more for higher power quality," or something similar, a statistic that seems quite believable and realistic. This led to considerable focus on improving power system reliability and quality.


71

However, it is worth bearing in mind that these same studies indicate that 20-30% is quite content with the status quo, while the remaining 30-40% would be interested in trading some existing quality for lower cost.

REFERENCES P. F. Albrecht and H. E Campbell, "Reliability Analysis of Distribution Equipment Failure Data," EEI T&D Committee Meeting, January 20,1972. R. N. Allan et al., "A Reliability Test System for Educational Purposes - Basic Distribution System Data and Results," IEEE Transactions on Power Systems, Vol. 6, No. 2, May 1991, pp. 813-821. R. E. Brown and J. R. Ochoa, "Distribution System Reliability: Default Data and Model Validation," paper presented at the 1997 IEEE Power Engineering Society Summer Meeting, Berlin. EEI Transmission and Distribution Committee, "Guide for Reliability Measurement and Data Collection," October 1971, Edison Electric Institute, New York. W. F. Horton et al., "A Cost-Benefit Analysis in Feeder Reliability Studies," Transactions on Power Delivery, Vol. 4, No. 1, January 1989, pp. 446 - 451.

IEEE

Institute of Electrical and Electronics Engineers, Recommended Practice for Design of Reliable Industrial and Commercial Power Systems, The Institute of Electrical and Electronics Engineers, Inc., New York, 1990. A. D. Patton, "Determination and Analysis of Data for Reliability Studies," IEEE Transactions on Power Apparatus and Systems, PAS-87, January 1968. N. S. Rau, "Probabilistic Methods Applied to Value-Based Planning," IEEE Transactions on Power Systems, November 1994, pp. 4082 - 4088.

3 Coincidence and Load Behavior 3.1 INTRODUCTION This chapter discusses how electric load "looks" when measured and recorded at the distribution level. Load behavior at the distribution level is dominated by individual appliance characteristics and coincidence — the fact that all customers do not demand their peak use of electricity at precisely the same time. For this reason alone, accurate load studies on the distribution system require considerable care. In addition, proper measurement and modeling of distribution loads can be quite difficult for a variety of reasons related to how coincidence phenomena interact with load metering and numerical analysis methods. Successful analysis of load data at the distribution level requires procedures different from those than typically used at the system level. This chapter addresses the reasons why and the measurement and analytical methods needed to accurately represent and handle distribution load curve data. The discussion in this chapter is divided into two sections. In 3.2, coincident and non-coincident load curve behavior are discussed - the behavior of distribution load as it actually occurs on the distribution system. Section 3.3 then looks at load curve measurement, and shows how some popular measuring systems and analytical methods interact with coincident/non-coincident behavior to distort observed load curve measurement. Finally, section 3.4 summarizes the key points and develops some guidelines for accurate load curve monitoring. 73

Chapter 3

74

3.2 PEAK LOAD, DIVERSITY, AND LOAD CURVE BEHAVIOR

Almost all electric utilities and electric service companies represent the load of consumers on a class by class basis, using smooth, 24-hour peak day load curves like those shown in Figure 3.1. These curves are meant to represent the "average behavior" or demand characteristics of customers in each class. For example, the utility system whose data are used in Figure 3.1 has approximately 44,000 residential customers. Its analysts will take the total residential customer class load (peaking at about 290 MW) and divide it by 44,000 to obtain a 'typical residential load' curve for use in planning and engineering studies, a curve with a 24-hour shape identical to the total, but with a peak of 6.59 kW (1/44,000ms of 290 MW). This is common practice, and results in the "customer class" load curves used at nearly every utility. Actually, no residential customer in any utility's service territory has a load curve that looks anything like this averaged representation. Few concepts are as

Summer and Winter Residential Class Peak Day Load Curve Summer

Winter 300

5200

>200

2

12

6

12 HOUR

12

12

12 HOUR

6

Summer and Winter Residential Peak Day Customer Load Curves Summer

Winter

7 6 • 4 23

3 22 1 0

12

12 HOUR

12

12

12 HOUR

6

Figure 3.1 Utilities often represent the individual load curves of consumers with smooth, "coincident" load curves, as shown here for the residential load in a utility system in Florida. Top, summer and winter demand of the entire residential class in a southern US utility with 44,000 residential consumers. Bottom, representation of "individual" consumer load curves. Each is 1/44,000 of the total class demand.

75


22 kW

One household

Hour Figure 3.2 Actual winter peak day load behavior for an individual household looks like this, dominated by high "needle peaks" caused by the joint operation of major appliances.

important as understanding why this is so, what actual load behavior looks like, and why the smooth representation is "correct" in many cases but not in many that apply to the distribution level. The load curve shown in Figure 3.2 is actually typical of what most residential customer load looks like over a 24-hour period. Every residential customer's daily load behavior looks something like this, with sharp "needle peaks" and erratic shifts in load as major appliances such as central heating, water heaters, washer-dryers, electric ranges, and other devices switch on and off. Appliance Duty Cycles The reason for the erratic "needle peak" load behavior shown in Figure 3.2 is that a majority of electric devices connected to a power system are controlled with what is often called a "bang-bang" control system. For example, a typical 50-gallon residential electric water heater holds 50 gallons of water, which it keeps warm by turning on its heating elements anytime the temperature of the water, as measured by its thermostat, dips below a certain minimum target value. Thus switched on, the heating elements then create a demand for power, which heats the water, and thereby the temperature begins to rise. When the water temperature has risen to where it meets the thermostat's high target temperature setting, the elements are turned off by that thermostat's relay. The on-and-off-

76

Chapter 3

75

170

Ti me

>

Figure 3.3 Top curve indicates the water temperature inside a 50-gallon water heater whose thermostat is set at 172 degrees over a period of an hour. The thermostat cycles the heater's 4 kW element on and off to maintain the water temperature near the 172 degree setting. Resulting load is shown on the bottom of the plot.

cycling of the demand, and the consequent impact on water temperature, is shown in Figure 3.3. The water heater cycles on and off in response to the thermostat - bang it is on until the water is hot enough, then bang it is off and remains so until the water is cold enough to cause the thermostat to cycle the elements back on. This bang-bang control of the water heater creates the actual demand curve of the water heater. Over a 24-hour period, it is a series of 4 kW demands, as shown in the top row of Figure 3.4. There is no "curve" in the sense of having a smooth variation in demand, or variation in demand level, beyond an up and down variation between 4 kW when the water heater is on, and 0 when it is not. Aggregation of Individual Demand Curves into Coincident Behavior Daily load curves for several residential water heaters are shown in Figure 3.4. Most of the time a water heater is off, and for a majority of the hours in the day it may operate for only a few minutes an hour, making up for thermal losses (heat gradually leaking out of the tank). Only when household activity causes a use of hot water will the water heater elements stay on for longer than a few minutes each hour. Then, the hot water use draws heated water out of the tank,

77


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Feb. 1, 1 994

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•o

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60

120

Length of Sampling Period - Minutes Figure 3.13 Measured per customer coincident peak load for a group of 100 homes, as a function of the sampling period used with period integration. For any sampling period less than 60 minutes, the measured peak load varies little, indicating that the load curve does not need to be sampled more often than hourly.

Comparison of the 60-minute sampled curves from Figures 3.12 and 3.14 shows that they are very similar. An individual customer curve, demand sampled at a slow rate, yields a good estimate of the coincident curve shape. In essence, measuring an individual customer's load at a lower rate with period integration gives a picture of that customer's contribution to overall coincident load behavior. Depending on the goals of the load measurement, this can be a positive or negative aspect of using the lower sampling rate. Determining the Required Sampling Rate Recall that the measured peak load versus sampling period curve for a single customer (Figure 3.11) bears a striking resemblance to the actual peak load versus number of customers coincidence curve (Figure 3.7). A good rule of thumb is: the demand sampling rate required to measure the load for a group of customers is that which gives the same measured coincident peak/customer when applied to an individual customer curve. For example, suppose it is desired to sample the load behavior of a group of five homes to determine peak loading, daily load curve shape, and so forth. Note that in Figure 3.8, C(5) = .8, giving a coincident load of .8 x 22 kW = 17.6 kW per household for a group of five homes.

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As Figure 3.11 shows, when applied to an individual household load curve, a sampling period in the range of 10 minutes (6 demand samples/hour) should give a peak of 18 kW. Therefore, one can conclude that a group of five homes needs to be sampled and analyzed at at least this rate to be represented accurately. Similarly, one can determine the smallest number of customers for which a particular sampling rate provides accurate data. Suppose that half hour data are to be used in a load research study. Figure 3.11 shows that this can be expected to record a peak load of 10.9 kW when applied to a single customer load curve. This is equivalent to a coincidence factor of 10.9 kW/22 kW or .49, which is equal to C(25). Thus, half hour data are accurate (in this utility) for studies of distribution loads and loading on any portion of the system serving 25 customers or more. It is not completely valid for studies on equipment serving smaller groups of customers. In a similar way, a plot of sampling period versus number of customers can be determined, as shown in Figure 3.14. This example is qualitatively generalizable but the values shown are not valid for systems other than the one used here — average individual household needle peak of 22 kW, coincident behavior as plotted here, and so forth, vary from one utility to another and often by area within utility systems. These data were taken from a utility system in the Gulf Coast region of the United States, and the results given are valid for that system. Quantitative results could be far different for systems with different customer types, load patterns, and coincidence behavior (see Electric Power Research Institute, 1990).

60 30 D) C CO

15L.S2 E i 'oT

% 'I w IU CO E, w o -in *-a en

5

10 100 1000 Number of Customers in the Group

10000

Figure 3.14 Rule-of-thumb plot of sampling period (for integrated sampling) versus number of customers in the group.

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Coincidence and Load Behavior Estimating Coincidence and Sampling Requirements

Often, a planner does not have even a few samples of high resolution (very short sampling period) load data from which to make the type of analysis described above. In that case, the author recommends that the coincidence curve (Figure 3.7) and the peak versus sampling rate curve (Figure 3.11) be estimated as best as can be done from whatever data are available. For example, suppose only 15minute period load curve data are available. Added together in blocks of two and four periods, this gives 30-minute and 60-minute sampled curves, whose peak loads can be used to provide the peak versus sampling rate curve in the 30minute to 60-minute sampling period portion of the curve. Estimates of the absolute individual household peak load can often be derived from knowledge of the types and market penetrations of appliances or by the planner's judgment. These data give an estimated value for the Y intercept of the coincidence curve. Measured peaks on distribution equipment such as service transformers and feeder line segments provide detail on the peak loads of groups of customers of various sizes. Analysis of sampling period versus number of customers in the group can then be carried out as outlined earlier.

60 .• •

30

O)

.

if co E

CO

810 0> O

?fe 0) Q_ 1/V

1

5

10

100

1000

10000

Number of Customers in the Group Figure 3.15 Example of estimating the sampling rate versus peak load relation from incomplete data, as explained in the text. Dots represent actual data taken from the system and analyzed as described in text. The solid line indicates the relationship that can be determined exactly based on this available data; the dashed line shows the estimated portion. As a general rule, the curve passes through the origin of the point (1, 1), shown by the X, a fact useful in estimating the curve shape.

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The approximate nature of the number of customers versus sampling period relation is estimated from these data, as shown in Figure 3.15. While not of the quality of accurate, high-sample rate data, the resulting curve is the best available under the circumstances and can be used, with judgment, to make decisions about the legitimate uses of whatever load data are available. 3.4 SUMMARY Distribution load behavior is dominated by coincidence, the fact that peak loads do not occur simultaneously. Individual customer load curves are erratic, formed of fast, sharp shifts in power usage. As customers are combined in groups, as when the planner analyzes load for groups served by equipment such as service transformers, laterals, and feeders, the erratic load curves add together with the sharp peaks intermingling and forming a smoother curve. As a result, when comparing the load curves for different size groups of customers, as the number of customers increases, the group load curve becomes smoother, the peak load per customer decreases, and the duration of the peak increases. Also important is the manner in which the load curves are measured. Where possible, particularly when working with small groups of customers, data should be collected and analyzed using the period integration method, and the sampling period should be quite short, generally less than 15 minutes.

REFERENCES H. L. Willis, T. D. Vismor, and R. W. Powell, "Some Aspects of Sampling Load Curves on Distribution Systems," IEEE Transactions on Power Apparatus and Systems, November 1985, p. 3221. Electric Power Research Institute, DSM: Transmission and Distribution Impacts, Volumes 1 and 2, Palo Alto, CA, August 1990, EPRI Report CU-6924.

4 Load Curve and End-Use Modeling

4.1 END-USE ANALYSIS OF ELECTRIC LOAD The ability to distinguish and model the behavior of electric demand based on class of consumer and as a function of time is an important element in many electric load forecasting applications. A wide variety of methods have been developed, but none more successful or applicable to spatial load analysis than the consumer class end-use load model. Consumer class end-use models are ubiquitous in the power industry, and a single chapter cannot begin to cover all of the variations and all of the finer points involved Instead, this discussion will concentrate on the basic concepts and those points which the author feels are important to spatial load forecasting. End-use models represent a "bottom up" approach to load modeling. They distinguish electric usage by three levels of categorization: consumer classes, end-use classes within each consumer class, and appliance categories within each end-use. Generally, they work with coincident load curve data in order to analyze and forecast curve shape, and they can incorporate weather sensitive elements where applicable. The best are nearly indispensable load analysis tools and all serious forecasters should consider the use of this workhorse of temporal modeling.

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Electricity Is Purchased for End-Uses Electricity is always purchased by the consumer as an intermediate step towards some final, non-electrical product. No one wakes up in the morning saying "I want to consume 12 kWh today." Instead, they want the products electricity can produce when applied through the actions of various appliances ~ a cool home in summer, a warm one in winter, hot water on demand, vegetables kept fresh in the refrigerator, and 48 inches of dazzling color with stereo commentary during Monday night football. These different products of electric usage are called end-uses, and they span a wide range of applications over all aspects of our society and its economy. For some end-uses there is no viable alternative to electric power (the author is aware of no manufacturer of natural-gas powered televisions or computers). For others, there are alternatives but electricity is by and large the dominant energy source (there are gas-powered refrigerators, and natural gas can be used for illumination beyond its niche application for ornamental outdoor lighting). But for many important applications, electricity is just one of several possible energy sources — water heating, home heating, cooking, clothes drying, and many industrial applications can be done with natural gas, oil, or coal. Each end-use — the need for lighting is a good example ~ is satisfied through the application of electricity to appliances or devices that convert the electric power into the desired end product. For lighting, a wide range of illumination devices can be used, from incandescent bulbs, to fluorescent tubes, to sodium vapor and high-pressure monochromatic gas-discharge tubes, and even lasers. Each uses electric power to produce visible light. Each has differences from the others that give it an appeal to some consumers and perhaps substantial advantages in some applications. And each requires electric power to function, creating an electric load when it is activated. By studying both the need for the final product (i.e., illumination) and the types of devices used to convert electricity to the final product (e.g., light bulbs), one can determine a great deal about the character of electric usage, particularly with respect to its variation with time of day, day of week, and season of the year. Further, one can determine its variation with temperature, and possible changes in future electric demand as technology, population demographics, or societal needs and values change. This is end-use analysis. Load Behavior Can Be Distinguished by Consumer Class Generally, electric utilities track sales of their product by consumer class, or more specifically, rate class, because different rates are charged to different types and sizes of electric consumer, and sales are inventoried and tracked within

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Load Curve and End-Use Modeling

19,000 GWhr

3,500 MW Peak

other other

Figure 4.1 Peak load and annual kWh sales broken down by basic consumer class for a municipal utility in the central United States.

each rate class, as shown in Figure 4.1. Many utilities charge a lower rate for usage of electricity in the home as compared to their rates for commercial or industrial applications (something called cross-subsidization). Rates are in a sense end-use based. Consumer class distinctions are nearly always used in end-use models because the basic uses of electricity and also the usage patterns vary greatly depending on the basic class of consumer (see Ceilings and Taylor, 1981). For example, while residential and commercial users of electricity both purchase a great deal of power for illumination purposes, commercial consumers use a predominance of fluorescent lighting while residential consumers use predominantly incandescent lighting. Commercial usage is high during normal business hours while residential lighting usage is highest in the early evenings. Distinction by class of consumer is an important element of end-use modeling. A class is any subset of consumers -whose distinction as a subset helps identify or track load behavior in a way that improves the effectiveness of the forecast. It is typical to study end-uses of electricity on a class-by-class basis, with classes defined so that they make important distinctions about usage. For example, commercial users might be segregated into retail and office classes, since these two applications differ substantially in both basic end-use needs (what they use electricity for) as well as the timing of their usage (retail generally

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stays open in the evenings, offices shut down after normal business hours). Similarly, the residential class might be split between homes in areas without gas distribution (thus they are more likely to be all-electric) and homes where natural gas delivery is available for alternatives to water heating and space heating. Therefore, while end-use models almost always start with the basic residential-commercial-industrial-other distinction of class, most use many more than three classes. Practical limitations place an upper limit on the number of classes that can be accurately and conveniently tracked, so most end-use models work with one or two dozen consumer classes, although successful models have been built using as few as four and as many as one hundred classes. What is important about the class definitions is that: 1) they make meaningful distinctions with regard to the load forecasting application, and 2) they are distinguishable based on available data. As an example, for analysis of usage in a seaside resort area, it might be useful to distinguish retail consumers of two classes: "retail establishments open all year" and "retail establishments open only for the tourist season." Such distinction could improve analysis by identifying seasonal versus annual users of electricity, and it might be possible to model usage in these two categories as functions of different parts of the local economy (seasonal load linked to tourism, annual users linked to other aspects) further improving the forecasting. The distinction of existing consumers into these two categories could be determined very simply from monthly billing data ~ seasonal consumers will have "zero kWh" bills for the winter months each year. Thus, this appears to be a good class definition: applicable to the forecaster's goals and distinguishable from available data. By contrast, while it might be useful in forecasting future load to distinguish residential consumer categories of "those who would spend extra to buy efficient appliances that conserve energy" and "those who will buy appliances based only on the lowest first cost," no reliable way of obtaining data to do so exists for most utilities. Similarly, just because a subclassification can be done based on available data does not mean it should be: one can group residential consumers into "those whose last names start with letters in the first half of the alphabet" and "those whose don't," but such distinctions have doubtful utility in forecasting load growth. Serendipitously, the consumer classes typically most useful in end-use modeling bear a very close match to the land-use classes used to distinguish locational growth and econometric patterns in the better types of spatial load forecasting models. Both start with the basic residential-commercial-industrialother classifications and make subclass distinctions within each. With only a little effort, spatial and end-use models can be structured so that their classifications are identical, and thus they can work in harmony.

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Load Curve and End-Use Modeling End Uses of Electricity

Within each consumer class, electric usage is distinguished by subdividing usage into end-use categories, as illustrated in Figure 4.2. As was the case with the definitions of consumer classes, the end-use categories are defined based on what is needed to improve the forecasting and analytical ability of the model for its particular application. An end-use model developed solely to analyze winter heating demand might make numerous distinctions of usage for space heating, but lump all other uses into an "other" category because detail is not particularly necessary there. Table 4.1 lists the end-use categories into which rural residential peak and energy sales for a municipal utility in the southwestern United States (same as plotted in Figure 4.1) were broken down in one end-use model developed by the author for both distribution planning and demand side management (DSM) planning. The categories shown are typical of a "full" breakdown of residential usage into categories, and similar detail is required in other consumer classes if the results are to be used in a comprehensive study.

11225GWhr

2310 MW

Water heat

Water heat

Cooling Domestic I and

omestic

Heating

Figure 4.2 Residential class from the municipal utility plotted in Figure 4.1 broken down into major end-use categories. The "domestic" category includes cooking and washing loads, entertainment (TV, stereo), and refrigerator loads.

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Table 4.1 End-Use Categories Distinguished for Residential Class Interior lighting Electric heating Water heater Freezer Washer-dryer Home entertainment

Exterior lighting Air conditioner Refrigerator Electric cooking Water well Other

Appliance Subcategories

Many end-use models make a further distinction within some or all of the enduse categories in each class, breaking the end-use load into subcategories based upon the appliances used to convert electricity into the end-use product. For example, electricity can be used to provide space heating in several ways: central resistive furnace, resistive-element circulating water, heat pump, high-efficiency heat pump, or dual fuel heat pump. Figure 4.3 illustrates a typical end-use appliance subcategory breakdown of this category.

100% of homes

Peak 440 MW

Energy 1,160 Gwhr

Figure 4.3 Residential class usage for heating. At left, breakdown of homeowners by type of appliance used to heat their homes. Middle chart shows contribution to system peak load, by appliance type, and rightmost chart shows annual kWh sales by appliance subcategory. Data are for the municipal utility data plotted in Figures 4.1 and 4.2.


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There are three reasons why appliance distinction is important in an end-use model used to forecast future peak and energy sales. The first is that the same end-use demand ("Keep the interior of my home at 68°F all through the winter.") will produce different electric demands depending on the type of appliance used. In the case cited here (home heating) the difference in appliance type can make over a two-to-one difference in the electric load created by the same household. For most applications, it is important to acknowledge such differences in conversion efficiency and to use them in the load analysis. Second, the mixture of appliance types used within a consumer base will change considerably over time, as appliances are replaced when they wear out. In most utility systems, the percentage of high efficiency heat pumps is increasing, and the application of resistive heat is declining — electric heating usage is becoming more efficient. A model broken down by appliance type has no problem tracking and even anticipating future changes in peak and energy usage due to continued shifts in appliance mixture. It can model today's usage as 28% heat pumps, 7% resistive heat, and tomorrow's as 32% heat pumps, 3% resistive, showing the difference in overall usage. Third, overall efficiency of appliances in any category changes slowly over time ~ major appliances are replaced only every ten years or so. An appliance subcategory model can represent such long-term trends by using two representations of an appliance such as a heat pump — normal and high efficiency, and varying their mixture over time to reflect the gradual shift toward higher efficiency. Load Curve Based End-Use Models Most end-use models work with load curves, most typically using coincident load curves as illustrated in Figure 4.4. Some early end-use models did not use temporal data of any type — some end-use models project peak load and energy usage by class, end-use, and appliance category without any distinction of demand as a function of time. However, since the 1980s end-use models based upon hourly, coincident load curves have been ubiquitous, for three reasons: 1. Ease of application. It takes very little additional effort to program an enduse model to handle 24 hours in a peak day than to handle just the peak hour. Hourly data are often available. 2. Load as a function of time is important. Consumer classes, end-uses, and appliance subcategory loads do not all peak at the same time nor have the same curve shape. Use of temporal modeling allows comparison of peak times and determination of coincidence (or lack of it) in category peaks.

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HOUR Space heating

Lighting

Water heating

Other

Figure 4.4 Four separate end-use category curves are shown plotted on top of one another (i.e., added together) to get one curve. This is a typical residential coincident peak day load curve for a utility in New England.

3. Curve shape is a useful clue in load research and analysis. Certain classes have identifiable curve shapes. Certain end-uses do, too. Therefore, uses of curve shape can improve the load analyst's ability to match characteristics to observed data and help explain load behavior. Temporal sampling rate and length of the period used to represent the load curves vary widely, depending on application. Hourly and quarter-hour sampling periods are most common. Some models represent only a single 24-hour period (usually the annual peak day) while others represent load behavior over many days, for example three days in each month (peak, average, and minimum). A few have used 8760 hour load curves (hourly for the entire year).

4.2 THE BASIC "CURVE ADDER" END-USE MODEL Figure 4.5 shows the basic structure of an end-use. The model distinguishes total system load as broken into a number of consumer classes, usually about a dozen. Within each consumer class, load is further broken into end-uses; within each end-use, into appliance curves. At the bottom of the model is a set of appliance load curves. In the example here, they will be represented as 24-hour, annual peak day coincident load curves. In practice the load curves could be of any temporal resolution and length required to do the job at hand.


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Figure 4.5 Overall structure of an end-use model, of the type necessary to provide a good foundation for effective load study and forecasting. The model is hierarchical and computes all the load curves except those on the bottom level of the model, which are input. Circles represent computations involving weighting factors (market penetrations, consumer counts, as appropriate -- see text). Only a small part of the overall model is shown. Squares represent load curve data on a per consumer basis (at the bottom) and as summed/weighted within classes, etc. Circles represent computations involving weighting factors (market penetration, consumer counts, etc., as appropriate - see text).

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The end-use model's structure consists of a hierarchical framework of weighting factors and pointers that designate the consumer classes, under which are end-use categories, under which are appliance subcategories. Only the appliance subcategories have load curve data associated with them. Each such load curve is the electric load over the peak day that is expected from a single appliance of this subcategory for an average consumer in this class. Figure 4.6 shows the four curves that might be included under "suburban residential heating." Also stored along with each appliance curve in the model is the market share of the appliance ~ the percent of consumers in this class that use this particular type of appliance for this particular end-use, as shown. To form an estimate of the total residential heating demand, the model sums all the residential appliance categories, weighted by their market share of the appliances. This forms an average consumer estimated heating curve — a weighted load curve sum representing a mythical consumer with a "cross section" of heating appliances ~ perhaps .2 high efficiency heat pump, .4 heat pump, and .4 resistive heater, representative of the total mix of the class. Similarly, within other end-use categories in this class, the various appliance types can be added together ~ in the interior lighting end-use, the curves for incandescent, fluorescent, e-lamps, and so forth can be summed into one lighting curve. In some end-use classes (well pumps, cooking) there may be only one load curve, indicating an average load curve of this end-use. Doing this for all end-uses in the class produces a full set of end-use load curves. Adding all these end-use curves together gives the coincident load curve for one average consumer of this class. This sum can then be multiplied by the total number of consumers in this class to obtain the total electric heating demand for the class. Other classes can be similarly computed, and all classes added together to obtain the entire system load.

Forecasting End-Use Load Changes Using Weighting Factor Trends Generally, to accommodate forecasting, end-use models have the capacity to store future values or trends in weighting factors and consumer count values at all levels of the model structure. Thus, while 28% of rural residential consumers use heat pumps today, this might drop to 18% in a decade, as most present heat pump owners opt to replace their unit when it wears out with a high efficiency heat pump (up from 11% today to perhaps 21% in ten years). Consumer counts might increase or decline and can similarly be trended in the model. Thus structured, it can "forecast" future usage by substituted future projected values for weighting factors, counts, etc., in all its computations.

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Load Curve and End-Use Modeling Heat Pump - SEER 9.2 Peak = 5.2 kW, 100 kWh 28% of class

Hi-eff. Heat Pump SEER 13.0 Peak = 4.85 kW, 92 kWh = 11% of class

Noon Time

Noon Time

Resistive Heat Peak = 8.9 kW, 164kWh 16% of class

Noon Time

Blower Motor only, non-electric Peak=.42kW, 8.5 kWh = 39% of class

Noon Time

Figure 4.6 Example of the appliance subcategory load curves at "the bottom" of the enduse model's structure. Here are the four load curves under the "space heating" end-use in the "rural residential single family home" consumer class, representing the coincident load curves for four different types of appliances, with the appropriate market penetration of each type in this class shown, so the proper weighted sum of the curves can be formed to represent a "typical" consumer's heating load. Percentage market penetrations here sum to 94% — apparently 6% of this class uses no electricity at all to heat their homes. The figures shown here match the "today" column in Table 4.2.

Variations in End-Use Model Structure The basic structure of the end-use model has been implemented in dozens of different applications, manually and particularly via computerization, going back into the early 1950s, and perhaps earlier. The author's first experience with such a model was a mainframe computer program which he helped write in FORTRAN to apply such a model at Houston Lighting and Power in the mid1970s. It was not considered a unique or innovative model at that time. If the load curve lengths are limited to less than about 100 periods (four days at hourly

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sampling or one day at quarter hour) this method can quite easily fit within an electronic spreadsheet template running on a small PC. Considerable variation has and will no doubt continue to exist in exactly how "curve adders" are implemented, due both to differences in the applications intended and to the personal preferences of those using them. Three significant areas of variation are worth comment: 1. Time period and sampling period. A few end-use models have represented less than a full day (perhaps only a ten-hour period around the peak); others represent all 8760 hours in a year. Some models have used two-hour periods (and are applied only to systems with broad, flat peak load periods), while some have used sampling rates as short as one minute in order to model noncoincident load curve behavior. Time period and sampling rate for a model depend entirely on the application's requirements. 2. Some end-use models lack the appliance subcategory level, particularly computerized models developed in the 1970s and early 1980s. Instead of modeling the various appliance types, an average load curve representing the existing appliance mix was entered for each end-use type. Changes in this curve shape over time were entered manually, having been calculated or estimated by some means outside the model. In the author's opinion, the appliance subcategory level ~ at least for major appliances ~ is what makes an end-use model especially useful as a forecasting tool. 3. Span and applicability. There is no need to limit an end-use model to the analysis of only electric load. Natural gas, fuel oil, and wood-burning energy applications can be modeled, too. This makes considerable sense for utilities that distribute both electricity and natural gas ~ such a model can study fuel switching, cross-elasticity and interdependence of energy sources. Application of an End-Use Model to Forecasting End-use models generally are applied to determine future curve shape, peak load, and energy usage that are expected to develop from expected future changes in appliance mixture, appliance technology and efficiency, consumer mix or demographics, and overall consumer count. The following examples illustrate typical applications: 1.

Change in appliance mixture. Usage for electric heating is expected to change within the rural residential class in the next decade as a new, efficient heat pump enters the market and other shifts in


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appliance mix take place. This results in the appliance mixture and heating load curves are shown in Table 4.2 and Figure 4.7. Even though one percent more of the class is expected to use electricity for heating ten years from now, and five percent more will use electricity as the primary heat source, both peak and energy usage decrease, due to the increase in efficiency of the overall mixture of appliances. Change in appliance technology. Suppose that a detailed study of federal guidelines on refrigerator energy efficiency, manufacturer's plans, and the present age distribution of refrigerators in a utility service territory establishes that the average refrigerator in a utility system will shift as shown in Table 4.3 over the next twenty years.

Table 4.2 Change in Appliance Market Share Over Next Ten Years Appliance

Peak - kW

Today

Ten Year;

18% 25% 9% 8% 35% 95%

Standard heat pump - 9.2 SEER Hi-eff. heat pump - 13 EER G.W. heat pump - 15 EER Resistive heating Non-electric fuel Total % using electricity

5.20 4.85 4.20 8.90 .42 in some form

Present Peak = 3.58kW/consumer Peak day energy - 67.7 kWh/cust.

Ten years Peak = 3.47kW/consumer Peak day energy - 65.9 kWh/cust.

Mid.

Noon Time

Mid.

Mid.

28% 11% 16% 39% 94%

Noon Time

Mid.

Figure 4.7 End-use heating curve for the rural residential SFH class for the present and ten years from now. Present curve is the weighted composite of the curves shown in Figure 4.6. "Ten years ahead" curve reflects the changes in market penetration of appliances given in Table 4.2.

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Table 4.3 Average Refrigerator Usage per Consumer Quantity Connected load Duty cycle @ peak Peak coinc. demand Annual energy Market penetration

Today

Twenty Years

500 W

280 W

.35

.45

175 W 1004kWh 103%

126 W 878 kWh 105%

This could be represented in the end-use model by creating two types of refrigerators, one with a load curve whose peak and energy match today's values of 175 W coincident peak and 1004 kWh annually, and one with values that match the projected future values of 126 W peak and 878 kWh. It is possible that the curve shape of the two is different (i.e., the future unit is not just a scaled down version of today's curve), since the duty cycle of the future unit is so much higher than today's units.1 To represent a gradual change in usage of these units over the next twenty years, market share between these two types can be varied in 5% increments from year to year, starting with a 100%-0% mix in the base year, to a 95%-5% in the next year, to 0%-100% in year twenty. This would implement a gradual, linear shift in appliance mixture. If known, a non-linear trend could be used just as easily. Maybe the 1 One way to increase the efficiency of nearly any cycled device is to arrange for it to use the same energy but at a higher duty cycle. The higher duty cycle means it goes through thermal transients caused by starting and stopping -- a major cause of inefficiency -- less often. In this case the new high-efficiency refrigerator has a compressor with a smaller net size, which will work about 10% more of the time, in addition to other changes. Consequently, the new refrigerator will not chill food put into it as fast, but it will use less energy overall. This means the future's refrigerator load curve will be flatter than the present's. Very likely, accurate data on that future load curve are not available. To obtain that curve for a forecasting study, a reasonable "assumption" would be to take the present refrigerator data curve and move its peak value "down" until it matches the new peak value of 126 W. Then, holding that peak constant, one can scale the curve so its minimum is such that the overall energy (area under the curve) matches the 878 kWh annual total. This would be a reasonable approximation, but only an approximation.


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change isn't 5% change each year, but a very gradual 1% year rate of acceptance initially, followed by an increasing rate of up to 10% market change in the final years. This or any other change can be represented with the proper trend modeling in the computer program. Note that the market penetration of refrigerators shown in Table 4.3 is more than 100%. This is typical throughout many areas of North America. Roughly 3% of residential consumers in this class (and 5% projected twenty years from now) have two refrigerators per household (beyond which roughly a third to a half may have a freezer in addition). 3.

Change in number of consumers. Today there are 19,000 rural residential consumers; in ten years there are expected to be 21,000. This can be reflected by changing the number of consumers used to multiply the completed residential curve to 21,000 instead of 16,000. In this case, it would mean that while peak heating load per consumer goes down from 3.58 kW/consumer to 3.47 kW/consumer, total contribution to system peak from this class's heating end-use will increase from 68 MW to 72.9 MW. Assuming the refrigerator trend in Table 4.3 is linear over the next twenty years, it would be half complete in ten years. Peak refrigerator load per household would thus shift from 1.03 x 175 = 180 W to 1.04 x (175 + 126)72 = 156 W, meaning total refrigerator load in this class would drop from 3.42 to 3.27 MW.

The above are offered only as examples. The real power of an end-use model is that it can represent the accumulated changes from dozens of simultaneous changes in factors like those illustrated here. Usually, those changes are developed by a forecasting department or as part of an overall econometric projection and as a set represent a future consumer "scenario."

4.3 ADVANCED END-USE MODELS The basic "curve adder" is a useful forecasting tool, but it has been called a "non-intelligent" model because it does nothing more than add together curves and trends input or developed from other models. Nevertheless, the author considers it a requirement for accurate and useful load forecasting for power system planning applications.

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Numerous "improved" end-use models have been developed to add "smarts" to the model, giving it greater accuracy or applicability. These generally fall into several categories which will be summarized here. Direct End-Use Consumption Models In this approach, end-uses are represented not by electric or gas demand curves as discussed above (Figure 4.6), but by curves that represent the end-use product demand in its raw units, such as gallons of hot water consumed on an hourly basis. These end-use product demand curves are then "interpreted" into electric or gas end-use curves by "appliance translators." Essentially, this adds another level to the structure of any computer program developed for an end-use model. "Appliance translator modules" replace the subcategory load curves in the basic model, and a single "end-use demand curve" sits below them in a model structure otherwise similar to Figure 4.5. There is only one end-use product demand curve in each end-use category, because it is assumed that demand for the end product is not a function of the energy source being used (a reasonable but not absolutely certain assumption). Generally, the "appliance translator" computation is more than just the application of a scaling factor to the end-use product curve, for it includes some time shifting and modification of the curve shape as well. For example, a translator to convert a "demand for hot water by hour" curve to an electric water heater load curve would use a factor to convert gallons of hot water to kWh, but it would also have to model a delay between the usage of hot water and when a water heater may activate, and it would need to model that the water heater will continue to demand electricity for perhaps an hour after a period of heavy usage. Thus it would have to modify the original curve shape. Appliance and Building Simulators A number of computer programs have been developed as "bootstrap" load simulators. These compute the electric load and daily peak load of individual consumers or groups of consumers with detailed data descriptions of the buildings, the appliances, the activity patterns, and the ambient weather (the last two on an hourly basis) as illustrated in Figure 4.8. The more comprehensive models represent a building in great detail, modeling the thermal losses through walls, ceiling, and roof, and even representing the circulation of air inside the building. They represent the impact of sunlight entering through windows, the effects of wind, humidity, and sunlight angle, and simulate the non-coincident operation and cycling of heating, cooling, and other appliances in great detail.


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Figure 4.8 A "bootstrap" simulator models the heating and cooling, water heating, and illumination needs of a building, and perhaps other loads as well, through a simulation of the building, the weather, water usage, and human activity patterns. Most involve very complicated numerical modeling and have a narrow range of application. In the author's experience, few are dependable, accurate models of actual load behavior.

Such models are quite involved, often requiring extensive numerical subroutines to represent the action of different appliances. Beyond this, they have to model the interaction of end-uses in their energy inventories. For example, incandescent light bulbs convert over 70% of the electric power they consume directly into heat, not light. Beyond that the light they produce is converted secondarily into heat when it eventually falls upon something. Thus, incandescent lights serve as space heater, whether desirable or not, aiding any space heating appliance, but placing an additional burden on air conditioning when needed. The author has participated in the development of one load simulator and has used a number of others. Overall, while the concept of "bootstrap" end-use simulation seems to be a sound idea, available computer programs can not live up to that promise. Generally, most bootstrap simulators work well only when applied to a narrow range of appliances and climate, usually that on which they were tested during development. For example, one popular simulator which the author has used on several planning studies was developed and first applied to load curve studies in the Boston area. It does quite well whenever applied to situations similar to the Boston area's climate, building, and appliance mix, but it fails miserably elsewhere. When applied to studies of a residential community in west Texas, it

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mis-estimated annual peak load by 15%, and perhaps worse, insisted that peak daily load occurred at 9 o'clock in the morning, an absurdity. No amount of changing of the input data or set-up within reasonable limits could force it to produce reasonable outputs. A different model had to be used, one which incidentally does not give good results when applied to the New England area. Thus, "bootstrap" simulators that estimate electric usage from raw data on buildings, consumers, appliances, and weather are generally very specialized and valid only within a narrow range. Such simulators should always be carefully calibrated against measured load data in the region being studied. If it cannot accurately "forecast" existing load behavior including the magnitude and time of the peak and daily minimum, and if it cannot predict both peak and off-peak day curve shapes when fed reasonable data on existing building types, appliances, weather, occupancy and activity patterns, etc., then what expectation is there that it can accurately forecast future loads? Whenever possible, the author prefers to produce forecasts using the "curve adder" type of end-use models which use measured load curve data by consumer class and appliance subcategory.

4.4 APPLICATION OF END-USE MODELS End-use models, at least those applied to system-wide modeling of electric load growth, are widely used in the electric utility industry. In the author's experience, most electric utilities in North America have an "end-use model" that "explains" their annual sales, breaking them among rate (consumer) classes and end-uses, and quite often utilizing appropriate appliance subcategory models. The majority of these models have peak day or annual load curve features, usually a combination of hourly models for certain peak days and an inferred annual energy (area under the curve) based on these curves. Computed load curves are used primarily for planning purposes and for assessment of the potential of peak shaving DSM methods (e.g., direct control of water heaters) to reduce system peak. End-use and appliance subcategory load curve data are obtained by actual measurement of (usually randomly selected) consumers in each class. Quality of these end-use models varies widely. Unfortunately, many have flaws that impact the accuracy of their results for system-wide, DSM, and spatial applications. Recommended procedure to avoid the most common pitfalls is: 1. Load curves should be measured with sufficient sampling rate and recorded using a statistically significant set of consumers. The number of consumers or appliances sampled must be sufficient that the coincidence plot (for the


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end-use or the appliance itself, not the consumer class as a whole) is well into the part of the curve where the slope is close to horizontal. This will depend on the coincidence behavior of the appliances, but in almost all cases requires at least 25 and perhaps as many as 50 actual recorded samples. Figure 4.9 shows the actual 24-hour data used to represent "highefficiency" heat pumps in the residential class. Note that this has the tell-tale pattern caused by "frequency folding" or "aliasing" that occurs when sampling rate is too slow to track the on-off cycling of the appliance being sampled (see Chapter 3). In this case, the utility sampled a total of 140 residences with "multi-channel" load recorders which recorded the whole house load and the load of four selected appliances inside, on an hourly basis. Out of these 140 homes, only nine had high-efficiency heat pumps. Thus, the load curve used shown in Figure 4.9 is based upon data sampled on only nine homes, a small enough number that coincident load curve shape will be a real problem. The data were sampled at an hourly rate. Heat pumps cycle on and off in less than one-half hour, meaning that even 15-minute sampling would be a bit too long to completely capture the data. Note that the actual data clearly have frequency folding effects in them, even though they were obtained with demand metered (period integrated data). Demand metered data integrated on a periodic window basis (every

(0

o>

Q.

Mid.

Noon Time

Mid.

Figure 4.9 High-efficiency heat pump load curve used by a utility was based upon too few consumers and sampled at too slow a sampling rate. It clearly shows effects of "frequency folding" (aliasing) yet was used nonetheless. Aliasing was caused primarily by the sampling period (hourly, when it should have been at no more than ten minutes) but augmented by the fact that only nine appliances were actually sampled.

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hour on the hour) are not always completely free of aliasing effects. The data shown in Figure 4.9 are simply not valid. As pointed out in Chapter 3, these data are useless for any load-curve related applications (the only valid measurement is the total energy used, or area under the curve). Lesson: Above all else, load curve data should always be examined to determine if they look reasonable and appropriate. The number of consumers metered in any study should be large enough to be out of the "high slope" area of coincidence curves, and sampling period should be no more than half the length of the average cycle period of the appliances being sampled. 2. The end-use model should "add up" to the observed system load. This may seem like an obvious point (the author thinks it is), but a surprising number of end-use models employed within the electric utility industry do not. In one case, a utility had developed a full end-use model of its system, which broke out all classes, all end-uses, and, where needed, all appliance subgroups. It was used for DSM studies and had been applied to determine the effectiveness of specific DSM programs — how much reduction per consumer was being obtained ~ in order to provide study results to the state utility commission. But the model overestimated most loads, and thus overestimated DSM impacts. For example, the model estimated peak water heater demand at 1.1 kW per household, and it was a reduction of this much (1.1 kW/household) upon which the efficiency of the utility's water heater load control program had been based and upon which it was justified to the utility commission. The particular water heater load curves used had not been sampled locally, but taken from a technical paper published by another utility.2 The data were not valid as applied ~ actual peak/household at this utility was only 600 W. Thus, each load controller was reducing peak by only a little more than half of what had been estimated. This particular end-use model was wildly overestimating water heater contribution to peak load in this utility's consumer base, and in fact it was overestimating a number of other end-uses in a similar manner. These errors would have been obvious had the model's loads ever been summed together 2

Water heater load curves sampled and tested prior to implementation of a water heater load control program at Detroit Edison. In the Detroit study, load curves were sampled correctly and the average water heater peak load was 1.1 kW per household in the Detroit area. The problem was that the data were not valid for the utility which had copied it for use on their system. Ground water there was much warmer (hence less energy was needed to raise it to proper temperature) and per capita hot water usage was less (hence less enduse demand).


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to obtain a "proper" weighted sum for the whole system, so they could be compared to actual observed system load behavior. In fact, that had never been done. When it was, it turned out that the model overestimated system peak by 40% and annual energy by 50%. This particular mistake ended up costing the utility dearly. Its load control program, justified on the basis of 1.1 kW reduction per water heater load controller, was delivering only a little over .6 kW/controller, at which level it was not cost-effective. Worse, when the errors were finally discovered, the bad publicity, as well as the financial penalties levied by the utility commission, were quite costly. Lesson: An end-use load model should always be compared to all actual measured peak and load curve data available. At the very least it must explain system peak, system minimum load, and their times of day and season of year. Ideally, it should accurately reflect load curve shape for all hours, too, not just those peak and minimum times.

End-Use Application to Spatial Forecasting End-use models can prove a useful tool in spatial load forecasting studies. When a land-use based simulation spatial model is used, application is fairly straightforward and there is little reason not to merge an end-use model with the spatial forecast model. The benefits far outweigh any slight additional effort required. (Land use simulation methods forecast spatial load growth on a consumer class by consumer class basis, and will be covered in much more detail in Chapters 10-16.) In order to merge spatial simulation and end-use analysis, the consumer class definitions used in both must be identical. This often adds some complexity to both the spatial and the end-use models, for the one set of consumer class definitions must cover all the distinctions needed in both spatial and end-use analysis. Thus, while a spatial model might be able to get by with twelve consumer classes for spatial forecasting, and an end-use model with nine classes for its application, the joint application requires them to work in concert with a total of sixteen classes. (An example will be given later in this section.) End-use models merged with a spatial model can associate end-uses and class behavior with location. This approach has a number of important applications, beginning with the ability to now apply the benefits of the end-use forecasting to spatial (distribution) forecasting, as well as more esoteric applications such as that shown in Figure 4.10.

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22-QCT-93 12-12-13 4 flnother ft»p 9BCL otherwise

(5.3)

where C(h) = the number of cooling degrees in hour h, T is the temperature in hour h, and CL is the optimum ambient outdoor comfort level for interior building temperature

Cooling degrees are given in degrees Fahrenheit or Centigrade - whichever is being used to measure T and CL. The number of cooling degree hours in any one day is calculated as the sum of the hourly differences: 24

CDH = Cooling degree hours (day) =

ZC(h)

(5.4)

h= 1

The number of cooling degree days (CDD) in a season or period of time is simply the number of cooling degree hours in the period divided by 24. Not surprisingly, cooling degree hours correlate reasonably well with weather-sensitive demand in most power systems. Air conditioner and chiller energy use is a fairly linear function of the temperature differential desired (see Chapter 3). For this reason, cooling degree hours and days are often used to measure the overall intensity of weather during a day, season, or year for electric demand purposes. Heating degree hours and days Heating degrees are those displacements of ambient temperature below, rather than above, 65° F. Heating degree hours (HDH) or days (HDD) are computed and applied identically to cooling degrees, except of course that heating degrees are computed as: H(h) =T(h)-CL ifT(h)

T3 r* (D T

(J1

Weather and Electric Load

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Beyond these longer-term cycles, there is no reason to believe that there are not longer-term cycles in the weather, cycles that mankind has not yet confirmed due to the relatively short duration of its quantitative observation of earth's weather (only two centuries at best). Cycles of variation of up to 4000 years in duration among oceanic flows and chemical/biological content (PH, plankton count, etc.), along with significant interactions with weather, have been the basis of some theories of how the planetary meteorological system functions (see Hsu, 1988). Unpredictable Elements of Weather Beyond the predictable daily, seasonal, and longer-term cycles in weather, there is the "unpredictable" element to variation over time. Unpredictable as used here signifies that it cannot be forecast far (e.g., a year and often far less) in advance with any precision characterizing that forecast by its most expected value. Figures 5.3 and 5.4 show typical behavior ~ summer temperatures at locations in Texas and California, along with a fitted Weibulll distributions. Weibull distributions often fit observed weather variation patterns well.2 Although T, H, and I at every location will have an apparently random, unpredictable element to them, the amount of unpredictability or randomness varies depending on location and season. Temperature and humidity vary less from their expected or average day-to-day patterns in Los Gatos, CA than they do in Moline, WI. In many locales, temperature, humidity, wind, and precipitation are much more variable in spring (tornado, or storm, season) than they are in summer or winter. The amount of variation also varies from year to year, with some years being more "volatile" than others. Often, "extremely hot weather" occurs during a summer in which the average temperature is a bit higher than normal, when there may also be more variation than in a normal year. "Heat storms" occur as very high excursions from an already slightly higher than normal mean. However, in this same summer there may be "cool periods" that match those expected in normal years, even though the mean temperature is slightly higher than average. Is Weather a Stationary Process? (Is Climate Changing?) A subject of debate in many environmental and meteorological circles is if climate is stationary (unchanging over time) or if climate and weather are changing. Is global warming real?

2

See, for example, Distributed Power Generation - Planning and Evaluation, by H. Lee Willis and Walter J. Scott (Marcel Dekker, New York, 2001) for a discussion of Wiebul distributions as a model of ambient air speed at any location, and their application in the planning wind-powered electric generation systems.

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Clearly, over long periods of time, climate does change. There is indisputable geological evidence that several ice ages, and their reciprocals — "tropical ages" ~ have occurred just in the last 100 million years. But geological time scales are so far removed from practical day-to-day time scales that their associated climate changes seem irrelevant to a person's life and job, or an electric system expansion plan. Perhaps more relevant are the climate changes recorded in the human record. The Arabian peninsula, which today has a very dry desert climate, had in biblical times considerably milder weather, and far less desert. Then, the land of Sheba (today, western Oman) had a climate somewhat like modem-day southern California (Clapp, 1998). In even earlier times, rhinoceros and other "African" animals thrived there in a climate much like modern-day Kenya. Climate does change significantly over centuries and millennia. In fact, there is no geological evidence to support the view that climate doesn't change and that it isn't currently changing. In fact, there is reasonably convincing evidence that average temperatures over the entire planet have been rising very slightly for several decades. Most of the debate over global warming centers not over if global warming exists, but over its cause and its future: Will it get worse? Is mankind to blame? There are several different theories or explanations for global warming, some backed by computer models or scientific explanations and forecasts of global warming. Some lay the blame for the warming trend directly and entirely at mankind's door: A somewhat convincing correlation between the increase in fossil emissions and other man-made pollutants, and global warming, can be found in the record of the last century. However, numerous opinions exist as to meaning, if any, and the interpretation of this correlation.3 However, other scientific viewpoints raise doubt about whether mankind has anything at all to do with global warming - the planet Earth experienced much wider deviations in climate long before man existed, and this latest trend might be only a part of another such oscillation of climate. The author's opinion is that mankind alone has not caused global warming, but hydrocarbon emissions and overuse of land in areas might have triggered or accelerated a nascent trend or changing local patterns. Regardless, for both power system planners and demand forecasters the important points are: • Global warming is real. Based on historical record, and having no reason to expect a change, one can expect average temperatures at any location to go up, rather than down, in the future.

A similar correlation can be made between the number of human beings residing on the planet, and global warming; or the number of artificial satellites put in orbit, and global warming. Again, correlation does not imply cause.

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Weather and Electric Load 90.0 88.0 -

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82.0

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80.0

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c

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78.0 76.0 74.0

Year

Figure 5.5 Thirty year's of average highest daily one-hour temperatures, June 1 to Aug. 31, measured at a point in the Midwest US. Mean value is 82.587 °F. However, a trend based on a starting value of 82.25 degrees in 1971 plus .166 degree added per year (for a total one half degree rise over the 30 years) fits the data slightly better than just the mean value alone. The fact that this trend fits 30 years of data is not too significant, but it is representative of more comprehensive studies that show a definite trend of warming.

• Often, particularly in and around large cities, there is a local "warming effect" due to the city's impact on local climate, amounting to as much as one degree Fahrenheit over three to four decades. • Within the planning period used in most utilities (5 - 20 years) both global and local warming effects are minor. • A serious challenge is analysis of historical average expected temperature? One obtains a different estimate of next year's most expected temperature from such analysis depending on whether one assumes weather has been stable, or that temperature is slightly increasing (Figure 5.5). Weather Is Less Predictable Over Longer Forecast Periods Weather takes time to change. The reason, which is linked to its physical manifestation, means that temperature, humidity, illumination, precipitation, and wind have a higher short-term than long-term correlation with their own values. If temperatures in early afternoon are extremely high, then temperatures are more likely to be extremely high three hours later, in late afternoon.

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12

°

8

a 4

1

2

3

7

10

30 Days Ahead

1Year

Figure 5.6 Standard deviation of differences in peak daily one-hour temperature for various lead periods (today's peak one-hour temperature - yesterday's peak one hour temperature) and for other different lengths of separation.

But while a period of an hour or two will produce fairly good correlation, even a single day ahead is a long period from the standpoint of forecasting and correlation, or lack of it, in weather. Figure 5.6 shows the standard deviation of daily peak temperatures of days separated by from one to 365 days, averaged over three sites (all in the mid-west US), during a 30-year period 1970 - 1999. Data used is only for mid-year (June 1 through Sept. 15). The difference in peak daily temperature measured only a day apart has a standard deviation of 6.9 degrees, while one week's separation gives a difference of 10.3 degrees, not much less than what a full year provides (11.2 degrees). There is no substantial increase in correlation for periods more than one year ahead. For this reason, weather is more predictable a few hours or days ahead than weeks, months, or years ahead. In some cases, auto-regressive models (which predict weather based on the present and recent past values of weather) are used for short-term (operational) electric demand forecasting purposes. Such models do not work well more than a few hours or days in advance, indicating they are no more accurate than merely characterizing the expected probability distributions based on historical observation alone. The opinion among many mathematicians and weather forecasters is that weather is a chaotic process, meaning that it can not be forecast in detail no more than a few days or perhaps a week ahead, no matter how much effort is expended.


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5.4 WEATHER AND ITS IMPACT ON ELECTRIC DEMAND In nearly every area of the world, ambient temperature varies by time of day and time of year, reaching its highest point each day in the afternoon or early evening, and its highest levels of the year during those times of day in the summer. Qualitatively, the electric demand in many utility systems varies in a similar manner, daily hitting its highest value in late afternoon, and (in most utility systems) hitting its highest annual values during the summer. Very clearly, a correlation, if not a direct cause-and-effect relationship, exists between weather and demand for electric power. Figure 5.7 shows the relationship between temperature and electric demand in a large metropolitan area of the U.S. Quantitative investigation and analysis of this relationship between demand and weather is useful to a utility demand forecaster for three reasons: Weather adjustment. Records of peak loads may have been made under different weather conditions. "Correction" to standard weather conditions makes them more useful for tracking trends. End-use analysis. Study of the variability of demand can provide indications of the composition of consumer demand. Weather normalization of forecasts - formulae are needed to adjust forecasts to extreme weather, so the system will be reliable.

5000

4000

Q 3000

3 O

re

2000

1000

0)

QL

0

10

20 30 40 50 60 70 80 90 100 Maximum Average Three- Hour Temperature During Day

Figure 5.7 Relationship used to represent peak daily system demand during summer as a function of weather in a large metropolitan power system.

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Correlation versus Cause Some elements of electric demand are clearly directly attributable to temperature, humidity, and illumination. Air conditioning and heating are the best examples. If temperature, humidity and illumination all happen to be at extreme values at 3 PM on a given Tuesday afternoon, electric demand will be very high, too, compared to an identical Tuesday where all three are closer to average. The reason is that air conditioning and heating demand is directly attributable to temperature and humidity and sunlight. There is a causal relationship. By contrast, demand varies in many other electric end-use categories in a way that correlates with weather, particularly temperature and illumination, without having a direct causal relationship. It is important to keep in mind that a correlation between weather and demand does not necessarily imply a causal relationship. Temperature, humidity, and illumination cause some of the observed variations in demand. But other elements of demand's variation over time may correlate with diurnal or seasonal weather variations without being caused by that change in weather. Commercial lighting is a good example of a daily cycle that correlates with weather and yet is not directly caused by it. In most office buildings, interior lighting is on only during the day, when temperature is also highest. Therefore a temporal correlation exists. But there is no cause: High temperatures do not cause commercial lighting demand. Commercial office and professional building lighting demand appears to correlate with temperature because both are linked to the same driving cause, but theirs is not a causal relationship Resort and tourist loads are an example of a seasonally varying load that similarly correlates with weather without having a direct causal relationship. For example, in Bar Harbor, Maine, demand is higher in summer than in winter. But the higher demand in summer is not due mainly to temperature, at least not in a direct sense. The resort hotels, restaurants and shops in Bar Harbor are open only during the "season" (roughly May through October). Nearly the whole industry closes during the winter, and many of the seasonal workers leave the region (thus even residential demand drops). In this case, demand and temperature have a common cause to their variation (the season), but one is not a direct function of the other. Very often, seasonal demand patterns contain a juxtaposition of both causal and non-causal correlated variations. As an example, daily household lighting demand curves are significantly different in winter than in summer, particularly at higher latitudes. This seasonal difference in demand pattern is partly due to seasonal changes in ambient sunlight (the sun rises later and sets earlier in winter, thus creating a longer period each day of lighting demand). But part of it


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is due to changes in daily activity patterns for both residences and businesses from season to season. Winter sees far less outdoor activity, and more school activity, and thus different household occupancy patterns, and different electric usage patterns, as a result. Thus, the change in electric demand for the end-use "illumination" is a function of both the actual changes in ambient (outside) illumination and the different seasonal activity patterns. Interpretation of "Causal" In some cases, the identification of "weather sensitivity" of demand as a causal function of weather is a matter of interpretation and preference, as with seasonal differences in water heating demand. Water heaters have to work harder (run for longer periods, using more energy) to heat cold water (e.g., 40°F) versus cool water (65°F). In cities and towns where public water is drawn from lakes or rivers, water heating demand will be noticeably higher in winter than in summer. The reason is that from winter to summer, temperature of lake or river water will increase as long-term changes in air temperature and solar illumination have their effect. As average daily temperatures increase in summer, and as the period of sunlight received each day increases, water gradually warms, until by early summer it is much warmer than it was in mid-winter. Consequently, water temperature is higher in summer, and heating demand is far less than it was in winter, for the same households.4 This seasonal variation can be viewed as either a seasonal variation that simply happens ("Water temperature goes up in summer and down in winter, period. Who cares why?") or as a weather-related variation, a function of THI, but with a very long time constant (moving average period). It is a legitimate weather-sensitive load variation. However a majority of utilities model it as a seasonal variation.5'6 An advantage is that perspective also easily encompasses non-weather-related changes in water heater demand due to such causes as different seasonal human activity patterns.

4

By contrast, in cities and towns where well water is used, particularly deep-well water of the type that high-volume municipal pumps usually draw, seasonal variation in water heater demand is far less. Ground water temperature fifty feet or more below ground varies only slightly throughout the year in most locations. 5 And frankly, a lot of utilities ignore the issue, electric water heater demands, or the variation in them, being below the level of detail in their demand analysis. 6 In addition, activity patterns involving water heating are different from one season to another - people bathe and wash clothes, etc., in different amounts and at different times of day in different seasons. However these changes are usually minor compared to the possible changes in seasonal demand due to water temperature variations.

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Humidity and Demand Temperature is only one of three weather factors that significantly impact electric demand. As mentioned earlier, the other two are humidity and solar illumination. In particular, humidity can have a significant effect on electric demand. Air conditioners and heaters must remove or add heat to the air being conditioned, and if the air is humid it has considerably more mass, and hence heat, to give or take heat energy, and thus requires more work on the part of heating and cooling equipment. Humidity can make a large difference in electric demand in the summer, but little in the winter. Cold air cannot hold nearly as much moisture as warm air, so in winter there is much less increase in thermal storage capacity of air as humidity increases. Figure 5.8 compares residential daily demand profiles for areas whose major difference is the humidity on the day of measurement. Note the per unit curve shapes. Humidity not only causes a higher overall burden (note the difference in the actual demand curves, in kW), but it also causes a fatter curve. Humid air retains a great deal more heat than dry air. Thus, temperature does not plunge as rapidly after sundown, and the air retains considerable heat all night long, requiring more work of AC units at night.

100% humidity

100% humidity

i \ 52% humidity \

(0 0)

a.

52% humidity

,.5 .* re 0)

a.

Mid.

Noon Time of Day

Mid.

Mid.

Noon Time of Day

Mid.

Figure 5.8 Daily coincident demand curves for homes in a "dry climate" (dashed line, 52% humidity and 100°F, southern Arizona on the day measured) and in a very "muggy climate" (solid line, 100% humidity and 100°F, Texas Gulf Coast on the day measured). At left, per household coincident demand curves in kW; at right, the same curves on a per unit basis - normalized to the same peak value — to best illustrate the difference in curve shape attributable mostly to humidity.


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The two areas represented in Figure 5.8 lie at nearly the same latitude and thus see the same diurnal solar inclination patterns. Sample data sets used to prepare the demand curves for each area were prepared from whole-house metered demand data recorded on a 15-minute basis, for 64 and 72 single-family homes, respectively, and were recorded on the same day in August 1990. Homes in each sample area are single story, roughly 2,200 square feet each, and built in 1982-1986 ~ as similar as practicably possible. Cloud cover in the two areas was similar (20-30%) on the days measured. For these reasons, this is one of the author's favorite data sets. Despite the impact that humidity has on demand, analysis of demand-weather interaction, as done at many utilities, sometimes does not include humidity. The reason is that humidity is rather constant (at least during seasonal extremes), or else it is quite highly correlated with temperature. For example, in the Texas Gulf Coast region plotted in Figure 5.8 humidity almost never dips below 90% during summer, and in the Arizona region plotted in the same figure, it seldom rises above 55% during warm periods. Therefore, demand analysis can disregard humidity as an independent variable, by assuming that it will remain at its maximum regional value, at least during those extreme situations which define design conditions. However, one should note that humidity varies regionally so that demand data from one region or microclimate cannot be imported to another for use there. Figure 5.8 amply demonstrates this point. Normalized End-Use Load Curve Weather Models Given care in collecting and analyzing data, weather analysis, adjustment and modeling can be done on an end-use basis, as illustrated in Figure 5.9. Here, data such as that shown in Figure 5.8 — metered demand curves for selected subsets of consumer classes — have been collected and analyzed both for peak and offpeak days. Something similar to the analysis in Figure 5.8 has been done from recorded data to establish AC demand behavior as a function of weather. This has been used to prepare AC appliance demand curves normalized to standard conditions (a "design day" with a peak temperature of 94°F). An additional curve has also been developed which represents "one degree Fahrenheit" ~ the additional increment of demand that would occur in a day where the peak temperature reached one degree higher. This permits the end-use model to add higher than design demands into the forecast so that contingency studies can be done if desired. Such end-use models are not difficult to develop. Basically, the same type of analysis is used on each class as on the system as a whole to determine weather sensitivity.

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1. A useful first step is to perform the type of analysis shown in Figure 5.8 on a class basis, using demand data for selected feeders or substations known to serve single classes of consumer. For example, peak day demands from five substations all serving only residential areas can be recorded, then added to form a "residential demand" for residential weather adjustment studies. 2. Not all seasonal variation is due to weather. During summer, parking lot lights in commercial areas (which consume a noticeable amount of power) are not turned on until as late as 9 PM in some latitudes. In winter those same lights may be on before 6 PM. Interior lighting patterns also vary from winter to summer. As mentioned earlier, ground water temperature varies from winter to summer by several degrees, so water heaters have farther to raise temperature, and water heater demand is thus higher in winter. 3. Appliance characteristics and market penetrations can be used to allocate weather sensitivity to the various appliance types within each class. Usually, for engineering purposes, one can assume that the per unit shape of the temperature sensitivity curve is the same as the per unit shape of the base appliance daily demand curve. Doubts about this can be resolved by applying a bootstrap building simulator to test hypothetical curve shapes. However, in general, if data are properly used, building simulators do not need to be used to determine historical weather sensitivities, because thermal loss factors for buildings are not weather sensitive. On the other hand, appliance output is a function of ambient temperature,7 and demand saturates at high temperatures because duty cycles reach 100% -- two factors modeled by the duty-cycle simulation that should be part of any good bootstrap simulator. The author recommends that weather normalization be a part of all demand analysis. Standard weather design conditions should be defined and applied to engineering, and demand data and base forecast values should be normalized to such conditions. Weather normalized end-use models (Figure 5.9) require a good deal of work in both data gathering and construction/verification but are worthwhile if detailed forecasts, DSM studies, or combined "integrated resource" planning are involved. 7

For example, the demand of a typical heat pump, when acting as a cooling unit, increases by 10% from 85°F to 100°F due to increases in the compressor back pressure caused by the worsening Carnot cycle of the heat pump loop.


153

1 i 2

Mid.

Noon Time of Day

Mid.

Mid.

Noon Time of Day

Mid.

Figure 5.9 The coincident per-consumer appliance end-use curve at the left is for a normal AC appliance demand for a typical single-family home in the Texas Gulf Coast area plotted in Figure 5.8. This is normalized to a "standard design day" (94°F THI here). Effect of higher or lower temperatures can be simulated by adding or subtracting one on more copies of the "one degree more" demand curve at the right, as needed, up to the point where AC demands begin to saturate (thought to be 97°F THI in this system).

The Relationship Between Weather and Peak Demand All manner of weather adjustment and normalization methods have been developed, applied, and refined by the power industry. Most rely on regression analysis and allied statistical methods to establish a functional correlation between data variables, then use this function to determine demand as a function of weather for adjustment and prediction. Many weather-demand models use a "jackknife function" analysis, shown in Figure 5.10. This function has a minimum point, usually at a temperature slightly lower than comfortable room temperature (e.g., 65°F or 17°C). From that point, demand increases as temperature increases or decreases. Since in most systems recorded data give no indication that the slope in both directions is not linear, a straight line is used, as shown. Since in many utility systems recorded data give no strong indication that the slope of the function in both directions is not straight, a straight line is often used for both increasing and decreasing temperature, as shown in Figure 5.11. However, at extreme high temperatures the slope of demand versus temperature usually decreases at high temperatures, as shown in Figure 5.10. This is due to saturation of air conditioner duty cycles (see Chapter 3). Usually, residential and

Chapter 5

154

1000

800 ro Q O)

600

3 Q

400

c •c

•o ro o

200

It

ro 0) O. 0

10

20 30 40 50 60 70 80 90 Maximum One Hour Temperature During Day

100

Figure 5.10 The simple weather-demand jackknife function. Shown are peak daily demand versus peak daily temperature for all Tuesdays in a year (several Tuesdays thought non-representative because they were holidays or similar special events were left out of the analysis). Only Tuesdays are used in order to reduce the effects that different weekday activity patterns may have on demand variation.

1000 800 (Q Q O)

600

Q

400

c

re o -J 200 ro Q)

a.

« 10


100

Figure 5.11 A refinement often used is to represent the high-temperature relationship as "bending" at some point in the high 90°F range, due to duty-cycle saturation of air conditioners. The transition in slope is almost certainly gradual, not sharp as shown here, but that is often not modeled.


155

commercial air conditioners are sized to handle a 25 to 30°F inside-outside differential when working at full (100% duty cycle) output. As a result, at somewhere around 95 to 100°F, the installed base of AC units in most utility systems begins to reach 100% duty cycle and AC demand does not increase as ambient temperature increases beyond that point. Of course, AC units vary in condition and precise fit to their application (a few will be oversized because only a choice of larger- or smaller-than-needed was available among standard sizes). As a result, the actual transition to completely "saturated" AC demand temperature is gradual, the slope decreasing as temperature climbs above some point. In addition, one would expect that at some point all weather sensitive demand would become completely saturated and the demand versus temperature curve would be flat.8 A similar reduction in slope would be expected at low temperatures, but is rarely seen. There are at least two reasons. First, the heating equipment installed in commercial and residential buildings (both apartments and single-family homes) is usually greatly oversized (i.e., installed capacity is greater than needed for expected temperature differentials), so that it seldom "runs out of capacity" no matter how cold the weather becomes. In addition, in extremely cold weather people use portable heaters (similar "portable AC units" do not exist or are much more expensive), turn on electric ovens and leave their doors open, and activate other devices to generate extra heat in their homes, further increasing demand. Figure 5.12 shows a simple improvement that can be made over Figure 5.11 by using simple end-use analysis concepts, in this case the recognition that winter and summer peak times are different and thus the "weather demand" falls on top of different "base" (non-weather related) demand levels. In the system illustrated in these examples, daily peak demand occurs in early morning, in winter, and in early evening in the summer. Rather than use a true "jackknife function" where the two whiter and summer slopes meet at a common point, this analysis realizes that they in fact "meet" at different demand levels because the non-weather sensitive demand is different at their respective peak times. The analysis in Figure 5.12 results in a better understanding of weather behavior and a superior identification of weather variability with temperature.

1

Records in some parts of a few systems in extremely hot climes show that residential demand actually begins to drop when temperature reaches very extreme levels — 105°F. Opinion among utility analysts in these areas is that in such extreme conditions, residential AC units may fall 5 - 10°F behind minimum comfort levels. As a result homeowners "give up" and head to a mall (which generally have both over-designed chiller units and higher thermal inertia so its takes longer to heat up) or the beach, etc. This explanation has not been proven but seems possible.

156

Chapter 5

1000 800 600 400

V

200

0

10


100

Hour of the Day

Figure 5.12 Improved weather adjustment based on end-use analysis recognizes that weather-sensitive demand builds on top of different levels of "base" daily activity in winter and summer. Thus, the "starting point" for peak demand varies seasonally. Daily curves shown for winter (solid) and summer (dashed) at right indicate the slight difference in the base curves due to solar inclination or other seasonal changes.

Statistical Analysis to Identify Weather's Impact on Peak Demand Analysis of weather and demand data for demand forecasting purposes generally has one ultimate goal: to adjust historical demand readings (recorded peak demand values for each year) to standard weather conditions. This is done by: a. Developing a relationship (equation) between weather and peak demand, for each past year, seeking the best statistical fit possible for each year b. Using that equation to determine what the peak demand would have been in each year had the weather been the same in each year Nearly anywhere in the world, a determined utility demand forecaster can obtain the basic weather information needed to perform a basic weather-demand interaction analysis. Availability of raw data is seldom a problem. Hourly records of temperature and humidity, and in some cases illumination, are available in a majority of locations, in some cases going back more than ten decades. If there is any one general lament among forecasters about available weather data, it is that usually it does not have sufficient spatial resolution - in a very large metropolitan area there may be only four sites (downtown, two airports, one weather bureau location) with decades-long records available.


157

General characteristics of the analysis This type of study is always historical, in that it involves only analysis of past weather, demands, and their characteristics and interactions. No prediction or extrapolation is involved in the analysis, nor will the results be used for prediction in the normal sense of the word. The analysis works on weather and demand data for some past period of time, be it a day, year, or usually several decades. For T&D planning purposes, the analytical focus is nearly always on explaining the peak hourly demand that occurred during a period (day, season, or year). Usually, annual peak is the key focus. The historical analysis will do so by looking at all peaks (daily peaks) in the record and trying to explain each as a function of weather at that time. The relationship, once identified, can then be used to explain extreme peaks as they relate to extreme weather and determine load duration curve shapes as will be discussed in Chapter 6. Almost invariably, only a causal mathematical relationship between weather and demand is sought — a relationship that represents demand as a function of weather up to and including the time of the peak demand, but not after it. In other words, demand can not be a function of weather that has yet to occur. This restriction to a causal relationship is made because a working assumption is that weather causes demand variations, and therefore weather that has not yet occurred cannot possibly affect present demand levels. Interestingly, a purely statistical analysis does not always bear this out. As an example, in one utility system in the western United States the highest correlation of weather, on the day of the peak, with daily peak demand was temperature at 10 PM that night. But peak occurred between 6 and 7 PM, hours earlier than that time, although analysis proved temperature at 10 PM had a slightly superior correlation to peak demand than temperature at 6 PM or any hourly temperature leading up to it.9 This situation and its not-too-convincing explanation illustrate one problem with weather analysis — correlation of weather data with itself. 9

The explanation developed for this vexing incongruity was only partly convincing although it passed muster from a statistical standpoint. The area in question often received mild winds during the day, which blew milder air in from the coast, miles away. These winds tended to die down at sunset. Air temperature at 6 PM was therefore often not a function of the amount of illumination received locally throughout the day. But the temperature at 10 PM in the evening (well after sundown) was a function of air temperature at sunset, when the winds tended to abate, and illumination throughout the day (long periods of sunlight heated the earth, which then kept the ambient air warmer at night). Demand at 6 PM was also partly a function of total illumination over the course of the day: By 6 PM the cumulative effect on demand was noticeable. Thus, demand at 6 PM correlated best with temperature four hours later.

158

Chapters

Regardless, planners should agree among themselves up front if they are going to restrict their analysis to developing only a causal relationship, or if a non-causal relationship will be acceptable. They should understand that their purpose and goals will be equally well served by either a causal or non-causal relationship, whichever has the best statistical fit. The weather-demand relationship, once identified, will be used to adjust past peak demand levels to normalized weather. Since this is a purely historical analysis, in every case, they will always know the weather on the peak day and thereafter, and can apply a non-causal relationship in their application. Weather at 10 PM on the day of the peak will be available to them even if the peak always occurred at 6 PM. This is the nature of a purely historical analysis. A multitude of possible weather variables Section 5.2 on weather focused on examples, graphs, and discussions that centered around peak one-hour temperatures. This variable was selected as illustrative of weather and its variation and behavior, in general. However, system planners and demand analysts should realize that there are many possible weather variables, besides just peak one-hour temperature, that should be explored during the process of finding a good relationship between weather and demand. In particular, often multi-hour averages, such as peak three-hour temperature, THI instead of temperature, and lagged temperature or THI readings (i.e., those taken a number of hours prior to peak) are most useful in explaining demand as a function of weather. Collinearity of data A challenge in the analysis of many types of time series, and weather in particular, is that there are so many choices for the variables to be used in a statistical analysis, and many of these are highly collinear (correlated among themselves). This is because weather variables interact with one another (e.g., intense illumination raises the temperature of air) and because one can choose "variables" that are merely lagged versions of one another (temperature at this hour, temperature an hour earlier). Table 5.4 shows the correlation of annual peak demand for a system in Texas, with a number of weather variables for the day of peak. Peak occurred at 6 PM. Four variables have correlations greater than 85% with peak demand: THI at the peak hour, wind chill at the peak hour, cooling degrees in the peak hour, and the temperature at 7 AM that morning.


159

Table 5.4 Correlation of Selected Weather Variables with Peak Daily Electric Demand and With Each Other Weather Variable

Peak THI@ Demand MAX

Wind Chill

Dew CD Temp Temp Max Sky CD Pt. Hr @Max 7AM 3hr Cvr 7AM

THI@Max

0.88

1.00

Wind Chill

0.85

0.94

1.00

DewPt

0.78

0.88

0.74

1.00

CD pk hr

0.90

0.87

0.88

0.83

1.00

Max Temp

0.82

0.89

0.94

0.72

0.92

1.00

Temp 7AM

0.86

0.90

0.86

0.83

0.88

0.83

1.00

Max 3 hr °F

0.83

0.95

0.97

0.76

0.90

0.96

0.87

1.00

-0.13

-0.15

-0.24

0.06

-0.16

-0.28

-0.12

-0.25

0.83

0.71

0.92

0.70

-0.08

1.00

0.88

0.91

0.83

0.90

-0.29

0.73

Sky Cover 7 AM CD

0.81

0.82

0.77

0.84

CD 12-14

0.79

0.90

0.91

0.69

1.00

Solely on the basis of having four variables with correlation above 85%, an analyst might assume that a model based on all four would provide an outstandingly high accuracy in relating demand to weather. But that is not the case due to the collinearity — correlation among the variables. The highest correlation between any two variables in Table 5.4 is between THI at peak hour and wind chill at peak hour. The 94% correlation between the two means that either one provides just about all the information contained in both: use of both doesn't tell the forecaster much more than use of either one. In fact, nearly all of the weather variables, except overcast, correlate fairly well with one another: there is not a lot of information in this set of variables, even if it contains a lot of data. Regardless, a correlation analysis such as shown in Table 5.4 provides a good start for demand-weather analysis and is recommended. It determines what is the preferred variable for analysis (CD in this case) and warns the analyst of collinearity in the data if they exist. Usually, they do. Statistical modeling of peak demand as a function of weather Many utilities expend considerable effort on statistical analysis of weatherdemand interaction, leading to development of functional relationships (equations) that explain or "predict" peak demand based on weather variables.

160

Chapters

This is all based on analysis of weather and demand records of a multi-year period - usually the last five to ten years. The equations, once developed, are then used to adjust demand readings to normalize weather conditions (for comparison purposes) and to adjust forecasts to the standard weather design criteria. Regardless of the method selected, however, the use of only valid statistical analysis methods and the application of formulae developed from such rigorous analysis, is highly recommended. Shortcuts and heuristic rules tend to be too approximate to be useful in this type of application. There are pitfalls. The most common is to spend a great deal of time on very advanced statistical analysis, which produces little benefit over more basic approaches. It is possible to perform a statistical study of weather and demand far beyond what is necessary for planning, a study that provides intellectual challenge and exercises advanced techniques. But its results bring nothing to the planning process. It is best to keep one's eye on the ball, so to speak, and always ask: "Is this work improving the results of the planning process?" Analytical method This book is not a treatise on statistical analysis. A number of excellent references on statistical analysis and its applications are available (see References). Most studies apply step-wise regression to develop the numerical relationship between weather variables and peak demand. This method is preferred over other approaches due to the myriad of variables with high collinearity involved in the analysis. The equation(s) selected are those that provide the best (narrowest range) of error in explaining demand as a function of weather. An example is the formula developed from statistical weather-demand analysis for a small utility system in the Midwest: 4PM

Peak MW = a(y) + b(y) (Td(7AM)) - 7 + c(y)(Z(THI(h)-70)) 1PM

where Td(7AM) is the temperature in °F at 7AM for day d THI (h) is the THI (equation 5.1) at hour h and a(y), b(y), and c(y) are the coefficients sought for year y among the historical period being used, y e [first year, . . . , last year]

(5.6)


161

This formula was developed for a utility system in the central United States, fit to the past ten years of weather and load history, based on 2 months of detailed data collection and statistical analysis by the utility's demand analysts. To obtain the best fit, they developed a formula using the same parameters, but different coefficients (values for a, b, and c) for each of the past ten years of data. One specific set of coefficients is determined by fitting the equation to 1992 data, another, for 1993, and so forth. Different coefficients are used for each year because number of consumers and economic/demographic factors vary from year to year. For example, different values of a, the y-axis intercept, essentially account for different amounts of base, non-weather sensitive load, due to such factors as consumer count. This equation with its year-specific coefficients was then used to adjust each year's peak demands to standard conditions. For each year, that year's coefficients are used as the equation is applied to solve for the peak demand that would have been seen under standard weather conditions. These standard conditions are the design criteria weather conditions - those conditions of temperature and THI, etc., which have been identified for use as "standard design conditions." Determining those values will be discussed in Chapter 6. 4PM

Adjusted Peak MW = a(y) + b(y) (TCd(7AM)) - 7 + c(y)(I(THIC(h)-70)) (5.7) 1PM

where TCd(7AM) is the design criteria temperature in °F at 7AM THIC(h) is the design criteria THI for hour h Note that the same parameter values (standard weather conditions) are used for every historical year, while the coefficients vary from year to year. The result is a set of weather-adjusted historical demands for each of the years used in the historical analysis. All are now on the same standard weather basis, as much as this process produces. Caveat. The author has seen some applications in which different equations are used, not just the same equation with different coefficients in each year. "Different equation" means that different parameters (base variables or their exponent) are used. As an example, the equation for 1998 might relate demand to temperature at 7 AM and cooling degree hours from 3 PM to 6 PM squared, while that for 1999 relates demand to cooling degrees from 6AM to 9AM and cooling degree hours from 3 PM to 6 PM (not squared). Mathematically and functionally, as far as the process of fitting equations and applying them to historical data is concerned, there is no real reason why different equations cannot be used.

162

Chapters

However, the author prefers to see the same equation used (with different coefficients) for each year. This is due to the need for consistency in defining extreme weather for the needs that will be described in Chapter 6. Once the equation(s) and coefficients are determined through the process discussed above, they are applied to the standard set of weather variables. Usually, these consist or two or three parameters such as temperature at 7 AM, cooling degree hours from 3 PM to 6 PM, etc. The specific values used (temperature at 7AM = 83 °F) are selected to weather "extreme enough" that it will be used as the target for design (Chapter 6 will discuss this in detail). Usually, the selection of the exact parameter values is based on a detailed statistical analysis of each: what value of temperature at 7AM is really one-year-in-ten? It is difficult to determine a set of only two or three parameter values that represent the same degree of "extreme" weather. To identify temperature at 7 AM, cooling degree hours from 3 PM to 6 PM, and perhaps one other variable such as solar illumination, so that they each represent the same one-in-ten year pattern takes care in coordinating them - do they all represent the same type of once-in-ten-year weather or is each a representation of one-in-ten year data that never occur together? Using different equations with different variables for different years increases the number of variables required to correct the data history. This larger set of variables creates more room for error. The requirement for different parameters among different years' equations means more must be studied and their extreme values selected. The potential for error (inconsistency) is increased. Worse, the sensitivity of the entire process is increased. If inconsistency exists among the values selected for a set of parameters used in the same way in every one of the yearly equations, then the error is consistently applied in each year. However, if some years use some parameters and others other parameters, the potential for inconsistency increases. Ruling day model In fact, the problem of consistency in identifying extreme weather is serious enough to create problems even when only two or three variables are involved. There are rare locations where one-in-ten temperature and one-in-ten humidity have never occurred simultaneously. Apparently the conditions that create each extreme are mutually exclusive. In many locations, extreme illumination and high humidity are partly exclusive (high humidity creates haze or comes from light cloud cover). For this reason, it is often best to abandon a purely statistical study of individual variables because they may produce an "over-extreme" set of weather variables. Instead, it is best to identify specific days - ruling days which will be used to represent each "one-in-N" weather scenario - all variables being taken from that scenario's day.


163

Example Application The particular equation used in 5.6 and 5.7 was selected by a utility over several other candidate formulae purely because it rated better in terms of two statistical tests in relating peak demand to weather variables on a daily basis for the summer periods (June 1 - Aug. 31) of 1990 - 1999.10'11 Regardless of that statistical fit, one questionable aspect of that formula is the physical explanation for its terms. Certainly the meaning of the first term is clear and seems legitimate: temperature early in the day (7 AM) is an indicator of pre-existing conditions (i.e., a high value indicates this is the second day of a 'heat storm'). However, the squared term (THI(h)-70) is more difficult to relate to a physical basis. But why should a term like THI reduction hours that is normally linearly related to demand, be squared?12 The formula given below proved better in actual use: 6PM

Peak MW = a(y) + b(y) (Td(7AM)) - 72) + c(y) (2(THI(h) - 70)) (5.8) 3PM

This equation had a slightly worse overall fitting accuracy in seven of the ten historical years examined, with respect to its accuracy in explaining peak daily demand level based on daily weather for the entire set of days from June 1 to Aug. 31. However, it had two advantages. First, its physical explanation is made easier due its lack of a squared term, and to a lesser extent by the fact that it includes THI up to the peak time. (This equation's summation of THI cooling hours includes hours up to 6 PM. Typically, system peak occurs at 6 PM.) But second and more important, equation 5.8 proved more accurate in explaining the highest 15% of peak daily demands in each year during that period (R2 of .908 versus .867). While equation 5.8 is less accurate than 5.6 in

Specifically, peak day demand versus weather prior to and including that day, for weekdays from June 1 through September 15 of each year. 1 ' The measures used were R2, the coefficient of determination, which approximates the amount of variability in the data that is explained by the model. An R2 value of 0.90 means that the formulae being used "explain" 90% of the variation seen in the time series being "predicted" by the model (in this case MW). Also used is CP) a statistic which indicates the total mean square error for the regression model's fit to the data. 1 Based on detailed assessment of the equipment and processes involved, the relationship between temperature and energy usage, or THI and energy usage, can be established as roughly linear between temperature and demand is essentially a straight line (See Figures 5.10-5.12).

Chapter 5

164

Table 5.5 Peak Demand Weather Adjustment Equation Coefficients, Parameters, and Results for Application During the Sample Historical Period Study Year

1987 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

Coefficients

A

B

C

4767 4767 4810 4863 4921 4978 5050 5117 5180 5239 5297 5360

65.0 65.0 65.6 66.3 67.1 67.9 68.9 69.8 70.6 71.4 72.2 73.1

25.0 25.0 25.2 25.5 25.8 26.1 26.5 26.8 27.2 27.5 27.8 28.1

Weather 7AM PMA

77.1

5.2 9.1 7.0 9.1 3.5 8.3 9.6 6.9 4.8 1.5 8.4

Demand - MW Actual By eq. Adjust 7234 7351 7622 7410 7876 7328 7947 8240 8162 7901 7957 8545

374.5 92.0 82.0 84.2 86.3 83.2 87.5 91.8 93.9 86.9 90.6 91.1

7258 7402 7474 7476 7758 7390 7940 8247 8218 7967 7920 8535

7747 7747 7817 7903 7998 8090 8207 8316 8417 8514 8608 8711

9000 -a 8500 c CO

E D O

x

8000 7500

CO CD

D- 7000 "co c c

87

88

89

90

91

92 Year

93

94

95

96

97

Figure 5.13 Actual and fitted demand histories, and adjusted (to ten year weather) peak demands for 1987 - 1997 (top, solid line). That adjusted trend shows a slightly lower actual demand growth rate than would have been extrapolated by trending the average of the unadjusted data (dashed line).


165

relating peak daily demand to weather for all 92 days of each summer, it is more accurate when evaluated on only the 14 days which had the highest peak demands. Since the desired application is to apply the formula to adjust peak annual demands to extreme-weather peak conditions, it seemed better to use equation 5.8. Applying the Example Formula to Normalize Data Normalizing historical demand data means adjusting all of the peak and off-peak data to be used in the analysis to some appropriate, standard set of weather conditions. Table 5.5 shows ten years of data including: • Coefficients fitted to equation 5.8 for each year: A, B, and C • Actual (historical) parameter values for each year's peak day, as "Weather" » THI at 7AM, and the sum of the THI for the four hours 3 through 6 PM on the peak day • Actual (historical) peak one-hour demand for that year, in MW. • The equation's estimate of peak demand for that year, at the conditions shown for the year under "Weather" (difference with actual is fitting error for that point) • Adjusted peak demand (equation's estimate) of the peak demand that would have resulted at standard weather conditions Figure 5.13 shows the historical, fitted, and adjusted demand values plotted by year.

REFERENCES Nicholas Clapp, The Road to Ubar, Houghton Mifflin, New York, 1998. Howard Hsu, The Great Dying, Ballantine Books, New York, 1988. R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall, New York, 1998. S. Kachigan, Multivariate Statistical Analysis: A Conceptual Introduction, Radius Press, New York, 1991.

166

Chapters

L. Ott and M. Longnecker, An Introduction to Statistical Methods and Data Analysis, Wadsworth, New York, 1999 H. L. Willis and W. G. Scott, Distributed Power Generation: Planning and Evaluation, Marcel Dekker, New York, 2001.

6 Weather-Design Criteria and Forecast Normalization 6.1 INTRODUCTION This is the second of two chapters on how weather influences demand and the analytical and planning issues associated with that relationship. It could also be characterized as "advanced weather-demand analysis" building on the "basic" material covered in Chapter 5. That chapter examined how weather influences electric load and looked at analytical methods to identify the relationship between weather and load, and use it to improve planning. This chapter looks at one aspect of weather in more detail - extreme weather, those conditions when it creates very high loads. It also examines weather's influence on electric demand from two other perspectives — that of the annual load duration curve and daily load curves. Both are necessary to build toward using the knowledge of weather and load in planning. Finally, the chapter addresses weather design criteria how utility planners use their knowledge of weather in setting target loading levels for the system and in assuring the system can handle the loads created by extremely hot and cold weather. It begins in section 6.2 with a look at "extreme weather" - what it is, how often it occurs, and how planners can study and access its impact. Section 6.3 then looks at weather design criteria and summarizes the basic approaches

167

Chapter 6

168

104 102

ow U.

100

«

98

$ *~ 3O

s-i

%

94 92 90 88 86 1971

1980

1990

2000

1990

2000

Year

1400 1200 . 1000 Q '

800 600 400 200

0 1971

1980

Year

Figure 6.1 Top, hottest peak daily temperature seen in each year from 1971 to 2000. Extremes in this measure often create peak demand conditions. Bottom, total cooling degree days from June 1 to Aug. 1 for the same years. What concerns the utility most are the extremes of both. Very high temperatures (above the dotted line in the top diagram) usually create record peak demand levels and high system stress. Very cool summers (below the dotted line in the diagram at the bottom) mean low revenues and a possible financial shortfall.

Weather-Design Criteria and Forecast Normalization

169

utilities have used to set design criteria. Section 6.4 then looks at a particularly effective analytical method to select an appropriate weather design target and presents an example. Section 6.5 provides a summary of both Chapters 5 and 6, along with some guidelines on weather, weather adjustment, extreme weather, and design weather criteria. 6.2 EXTREME WEATHER What matters to the T&D planner are the extreme situations that might be created by future weather and the likelihood of seeing those extreme weather conditions. How often can the planner expect weather conditions that lead to really high, stressful levels of peak demand on the power system? How often can the utility expect really mild summers, with the extremely low revenues they bring? Figure 6.1 shows actual data from a system in the Midwest U.S., addressing both questions. Extreme values occur rarely, but over a long enough period of time they are essentially certain to occur. From any practical standpoint of T&D planning, it is impossible to "forecast" accurately the time and severity of extreme weather. The "forecasting period" of interest to T&D and financial planners is a function of the lead times for system equipment and additions, and is measured in years. While predicting weather a few hours or a day ahead is feasible, short lead times like that are important only to bulk power schedulers and T&D system dispatchers at a utility. As mentioned in Chapter 5, over a period of even one year ahead, it is impossible to forecast future weather beyond being able to project the probabilistic distribution of possible outcomes. As a result, "forecasting" extreme weather for T&D planning basically boils down to determining the expected characteristics of future weather based on analysis of historical weather variation Frequency of extreme weather One of the most useful ways to characterize weather for T&D planning is to look at how often the utility can expect to see weather conditions extreme enough to lead to really high demand levels. This "frequency analysis" will be used later in this chapter to derive risk-based definitions of weather design criteria for power systems. On many power systems, annual peak demands occur in summer, near the end of multi-day periods of very high temperature - what are often called "heat storms." In winter, a peak demand period can occur due to an intense cold snap, often brought on by the movement from the polar region of an area of intensely cold air (called, in the U.S., a "blue norther"). The more extreme the weather, the less often it occurs and the less often it can be expected in the future. As an example, Figure 6.2 shows frequency of

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occurrence of temperatures at a location in the Midwest United States. This is called a weather severity diagram. As shown, in this case a temperature of at least 91°F can be expected to be reached or exceeded every summer, while it is quite common to see temperature reach or exceed 96°F - on average every other year (one out of every two summers). A temperature of 99°F is reached only one out of every five summers, and 102°F is reached only on average once every ten years. This type of "once every X years" weather-severity plotting is a useful way of characterizing weather for forecasting and planning purposes, as will be discussed later in this chapter. As mentioned above, not just the peak temperature for an hour, but the entire seasonal weather behavior tends to vary in "extremeness" too (Figure 6.3). Simply put, some summers average out hotter than others, all summer long. During those, average temperatures are higher and temperature reaches any particular high level (e.g., 96°F) more often and/or for longer periods than in cooler summers. Figure 6.4 shows how extremes of temperature and cooling degree days were related (same data as used for Figures 6.1 through 6.3).

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287

The coefficients an, bn, cn, dn and am, bm, cm, dm are identical to those that would have been obtained had the regression been solved separately for each area. The only change is that the simultaneous solution required roughly twice as much computer time as it would have taken to solve them separately. This is because a major part of the computation involves inverting and multiplying the P matrix, which in the simultaneous case is 18 by 8, four times as large as the 9 by 4 individual case. Thus, this simultaneous calculation for two cases takes roughly four times as long as the calculation of each of the individual cases, which means a net doubling of solution time. At twice the effort overall, it hardly makes sense to solve the two areas simultaneously, except that this new method permits the trending of the two load histories to be coupled, which permits the acknowledgment of transfers within the load histories of both and removal of some of the adverse forecast aspect of the transfers. An additional matrix, R, the coupling matrix, is required to implement this. R is square, and has dimensions 2 x (number of years of load history and horizon loads). R is inserted into equation 9.6, as C

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If R is equal to the identity matrix, i.e., if all its off-diagonal terms are zero and it has ones along its upper left to lower right diagonal, then it makes no impact on the resulting solution, and the coefficients obtained are identical to those found by equation 8.6. However, suppose that the off-diagonal terms at (7, 16) and (16, 7) are set to 1, as shown in Figure 9.15 (top). These two positions correspond to the "meeting places" of the column representing the seventh year of substation n's history and the row representing the seventh year of substation m's history, and vice versa. If this matrix, with its off-diagonal ones, is used in equation 9.7 the resulting projections of the two substation load histories are much less sensitive to the load transfer between the two substations. No longer is the computation's goal to pass the extrapolated curve through all the data points as closely as possible. For the first six years of load history, that remains the goal. But, for the seventh year, the curve fit's goal is to end up with residuals for substation n and m of equal magnitude, but opposite sign. Since the load transfer caused load changes of equal magnitude but opposite sign to the two load histories, this "trick" tends to make the trending ignore the load transfer, yet take the load growth occurring in the 7th year (that with the load transfer) into account. In practical applications, forecast error is reduced, with error caused by load transfers being reduced by as much as 90%.

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In ignoring the load transfer in this way, the forecast accuracy is improved. Technically speaking, the service area definitions that are being forecast are actually those for years 1-6, not the final year. The resulting forecast would be great if the planner wanted forecasts of load for those substation areas, as would be the case if this load transfer (and its consequent change in substation boundaries) were temporary. That may be the case. If so, matrix R above is the proper one to use. But if the transfer is permanent and the planner wants to forecast the load within the latest service area definitions, then R must be modified as shown in the bottom of Figure 9.15. The curve fit now treats the load transfer as having occurred on a "temporary basis" for years 1 through 6, and produces a forecast that represents a projection by service areas as they were in year 7. With so many off-diagonal terms, the curve fit is not as tight as it was with only two offdiagonal terms, but the forecast is still substantially improved. LTC is a practical forecasting method because the user does need the amount or the direction of the load transfer (information that often is either unavailable or difficult to obtain). The technique can be implemented on more than two substations, to handle simultaneous transfers between three or more substations, by extending the P, L, and C matrices to represent three or more substations at once. Load transfers are then handled by filling in the R matrix with Is everywhere a load transfer between substations or feeders is suspected, possibly representing complicated load transfer scenarios between substations. Care must be taken not to put too many Is into the R matrix. If it becomes too full of Is, the regression accuracy tends to degrade. However, used with judgment and common sense, this method in combination with horizon year loads and fitting cubic or cubic log equations as described above is the author's recommendation as a basic trending method for feeder and substation load forecasting.2 A thorough discussion of how and why the LTC adjusts the trending to ignore the load transfers is beyond the scope of this chapter, but is available in a technical publication (see Willis et al., 1984). Making the LTC algorithm 'automatic" Load transfer coupling trending methods can substantially reduce error in practical forecasts — usually providing a reduction of 20% to 35% in error level as compared to the basic regression-based curve fit. To be of practical use, the 2

This does not mean it is the recommended forecasting method overall. Short- or longrange simulation methods are much more accurate, but also more expensive. Given that only a trending approach is considered affordable, LTC is the recommended method.

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method must be able to handle a large number of small areas at once. Implementation for large numbers of small areas brings two difficulties, one involving computerization, the other data set-up and user effort requirements. With respect to computer implementation, while potentially the P and L matrices can be dimensioned for as many small areas as needed, both matrix size and computation time increase as the square of the number of small areas analyzed simultaneously. The author has experienced computational difficulties (severe round-off error, overflows, etc.) with LTC when handling as few as forty small areas, and such problems occur frequently if LTC is set up to deal directly with 100 or more areas simultaneously. Sparse matrix methods can partly correct this situation, but they increase programming complexity considerably. Beyond the computerization problems, the user has to set up the R matrix ~ even if the computer program has been written to make entry of the data easy, the user must identify any and all transfers manually. This takes time, data, and judgment. Despite these objections, when first developed (1984) LTC was widely used in this manner. Usually limited to a computer program capacity of 25 small areas, it would be applied numerous times to cover a large system. However, the basic LTC method can be included in a procedure that automatically applies it to all small areas serially (i.e., one small area at a time), and that can potentially handle thousands of small areas if necessary. In order to function, this procedure needs one additional item of information about each small area ~ an X-Y location. Such geographic data on the location of small areas are a common requirement of many newer trending methods developed in the 1990s, as will be discussed later in this chapter. For substation area based forecasting this location can be the X-Y coordinates of the substation. For feeder area forecasts this can be an X-Y location at or near the center of the feeder service area.3 These locations can be estimated if precise data are not available - the automatic LTC procedure is quite insensitive to error in these locations, and errors of 1/4 to 1/2 mile make little if any difference in results. The automatic LTC procedure is quite simple, with a computer program to implement it operating in five steps: 1. The user inputs seven years of historical load data, plus a horizon year load, and the X-Y location for each small area. The procedure also requires two arbitrary threshold values, Q and Z, which will be described later.

3

Often this can be done automatically. If a computerized feeder graphic data base is available, a routine can be written to extract all nodes in each feeder and average their XY locations, producing a "center of nodes" for the feeder.

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2. For each small area, multiple regression (without LTC) is used to fit a cubic polynomial to the small area's historical peak data and horizon year load. The residuals (mismatch between historical data point and curve value) for each of the seven historical years are computed and stored. 3. The program proceeds through the list of small areas (the order is not important). For each small area, it will perform an LTC regression analysis on the small area and four of its neighbors, setting up the R matrix itself. a. It uses the X-Y data to find the four nearest neighbors to this small area. b. It compares the stored residuals for this small area to those of each of its neighbors. If the residual for a particular year in the small area and a neighbor: i. are of opposite sign, ii. have magnitude within Q of one another, and iii. exceed a threshold value, Z, then a "1" is entered in the appropriate location in the fivearea R matrix. c. The LTC regression is solved, obtaining a curve fit for each of the five small areas. The curve fitted to the small area is used to forecast its future load. Those computed for its neighbors are ignored. d. The procedure repeats a through c above until it has exhausted the list of small areas. The residual detection threshold values Q and Z must be determined by hindsight testing for the particular system being forecast. Generally, this procedure works well when Q is between 20% to 35% and Z is in the range of 7% to 20%. Although this procedure should not do quite as good a job at "finding" load transfers and setting up the R matrix as an experienced forecaster working with good data, few forecasters have the time, data, or tolerance to work through the

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Figure 9.16 "Automatic" LTC reduces forecast error significantly compared to the basic regression curve fit method. Solid lines compare forecast results in a test on a rapidly growing metropolitan system in the southern United States (700 feeders, average annual system peak growth, 2.7%) using 1979-1985 data. Dashed lines compare basic and LTC for a slowly growing system in the northeastern United States (200 feeders, average annual system peak growth, .3%), using data from 1981-1987.

very tedious and error-prone task of setting up the R matrix well. As a result, in practice this method outperforms manual LTC in terms of accuracy, as well as convenience. Figure 9.16 compares the forecast accuracy of this method with respect to the basic cubic polynomial curve fitting to historical data. Computation time for this method is about fifty times that required for the basic curve fit method — this procedure performs the basic curve fit for each small area (step 2) and then repeats its analysis for each small area using an LTC method covering five substations (using matrices 25 times as large). This increase must be kept in perspective however ~ it is fifty times a very small amount. Computation time is still reasonable: solution time when implemented on a 1500 MHz PC is less than five seconds for a three hundred feeder forecast problem.

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9.4 OTHER TRENDING METHODS Multiple regression is not the only method of curve fitting that can be applied to small area load forecasting. Many other mathematical approaches have been applied, often with results as good as those provided by multiple regression curve fitting. Most techniques that try to improve on standard multiple regression curve fitting attempt to gain an improvement through one of two approaches, either by forcing all extrapolated curves to have something close to an S curve shape, or by using some other trend beyond just load history to help the extrapolation. One method of each type will be discussed here. Template Matching (TM) - A Pattern Recognition Method It is possible to restrict curve fitting methods to only functions (such as the Gompertz function) that have an S curve characteristic using various algebraic means in the curve-fitting steps. However, the "template matching" method is a unique S curve extrapolation method that functions without curve fitting or regression. It will serve as an example of a non-regression based method. Template matching is a pattern recognition based method, rather than regression based. It is very simple mathematically (in fact it can be implemented as a computer program with only 16 bit arithmetic using no multiplication or division operations) but it requires much longer load histories than regression based curve fitting methods ~ the longer the better. Rather than extrapolate load histories, TM tries to forecast a small area's load by comparing its recent load history to "long-past" histories of other small areas. Figure 9.17 illustrates the concept ~ small area A's recent load history (last 6 years) is similar to the six years of load history that occurred sixteen years ago in small area B. Therefore, it is assumed that small area A's load will grow during the next ten years in the same pattern that B's did over the past ten years. Small area B's load history is used as a growth trend "template." In actual operation, template matching is slightly more complicated than illustrated in Figure 9.17 (see Willis and Northcote-Green, 1984). As a first step in finding load histories that match, the method compares each small area's load history to those of all other small areas in order to find several sets of small areas (clusters) that are similar in growth character, all small areas in a particular cluster having similar load history shapes. Typically, the method identifies from six to nine clusters. Small areas in each cluster are not necessarily geographically close to one another, nor do they necessarily experience their sharp growth ramps of S curve growth at the same time, but they have similar S growth characteristics (ramp rate, height, statistical variance from a pure S curve shape), whether their growth ramp occurred recently or long ago.

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AREA B

AREA A

Past | Future YEAR

Figure 9.17 The template matching concept. Small area A's recent load history is found to match small area B's load history of many years ago. Small area B's load history is then used as the "template" for area A's forecast.

For each cluster, the method next develops an average curve shape, which is called the group template. By overlapping and comparing load histories, this group template can be extended in length to cover a longer period than the available load history — twenty-five-year-long templates are extracted from just twelve to fifteen years of load history. These are sufficient to extrapolate all small area load histories far into the future. Overall, accuracy is comparable to that of the best multiple regression methods, but not substantially better. Template matching works best when applied to small areas defined by a uniform grid ~ like many pattern recognition methods, in practice it works best when all the items being studied have a common factor that is identical, in this case area size. With some modification and complication, the method has been applied in the VAI concept, proving slightly more accurate than regression based VAI methods at forecasting future growth in vacant areas of the grid. Template matching gives forecast error that is comparable to regression based curve fitting. Compared to curve fit methods, it requires a much longer period of load history in order to be effective ~ ten to fifteen years at least, versus a maximum of seven for curve fitting. In exchange for this greater need, it

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has only two advantages over curve-fit methods. It is very robust — quite insensitive to missing or erroneous data values -- and it requires minimal computational resources. Its numerical simplicity made it quite popular beginning in the mid 1980s, when it was ideally suited to the limited capabilities of the first generation of personal computers, such as the original Apple. While it continues to be widely used on small PCs by many smaller utilities worldwide, the capability of modern PCs of even modest cost to handle large floating point matrix calculations makes its computational advantages of negligible advantage. However, TM provides a ripe area for use in hybrid algorithms, as will be discussed in Chapter 15. Multivariate Trending Many trending methods attempt to improve the forecasting of electric demand by extrapolating it in company with some other factors, such as consumer count, gas usage, or economic growth. The concept behind such multivariate trending, applied to two variates such as electric load and number of consumers, would be: a. Establish a meaningful relation between the variables being trended (e.g., electric load is related to number of consumers). b. Trend both variates subject to a mathematical constraint that links them using the relationship established. In this way the trend in each variate affects the trend in the other, and vice versa. Hopefully, the larger base of information (one has two historical trends and an identified relationship between the two variables, so the base of information is more than doubled) leads to a better forecast of each variate. Many electric forecasting methods have been developed with this approach. Hybrid Methods Some multivariate trending methods go much farther in using the multiple variates than just "trending," working with the data to the point that they employ some aspects of simulation. Two such examples are extended template matching (ETM) and land-use based multivariate trending (LUMT). These will be discussed in Chapter 15, which covers hybrid forecast methods. Geometric and Cluster-Based Curve-Fit Methods Multivariate methods notwithstanding, the chief advantage of most trending methods has traditionally been economy of usage: data, computer, and user requirements are all minimal. They work on an equipment rather than grid basis, which minimizes time spent on mapping. They require only historical peak data, which are nearly always easy to obtain. Their computational requirements

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(usually multiple regression curve fitting) can be satisfied by small computers and occasionally even hand-held calculators. They are automatic ~ put the data in, run the program, and get the results — requiring only the most limited involvement from the user. Trending's major disadvantage is performance. Forecast accuracy of traditional and multivariate trending methods is marginal, and becomes unacceptably poor when predicting load growth more than four years ahead, particularly if at a small area size small enough for substation and feeder planning (160 acres of smaller). Beyond that, they are unsuited to multi-scenario forecasting. Beginning in the mid to late 1980s, research and development on trending methods turned away from multivariate methods and concentrated on developing improved methods that preserved the traditional simplicity and economy of trending, while improving forecast accuracy, particularly in the short-range T&D planning period (1-5 years ahead), where multi-scenario capability is not a big priority. This led to a number of trending methods with substantially better forecast accuracy than regression-based curve fit. In fact, the best of these methods can match the forecasting accuracy of multivariate methods, but retain most if not all of the simplicity and economy of operation expected of trending methods. In general, these methods were developed with three goals in mind: •

Forecast load on an equipment-oriented small area basis.

•

Use only data that are universally available and easy to obtain.

• Keep computerization simple and the application "automatic." There have been a number of improved trending methods developed, many quite successful in attaining some or all of these goals and improving accuracy. Most of these seek to improve forecast ability by: Combining successful characteristics of existing trending methods such as using both regression curve fit and template matching. All of the standard trending methods — curve fit, LTC regression, clustering, template matching -- have computational needs well within the capability of even low-end modern PCs. Using all of these trending methods simultaneously imposes no practical problem. Using additional data sources which are easy to obtain. An example is information on the location of substations used by several "geometric" methods ~ which is universally available and quite easy to obtain.

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/ \ Figure 9.18 Based only on the location of substations, a method such as LTCCT trending will estimate service area sizes, which helps it distinguish different types (clusters) of small areas it has been asked to forecast. Shown here are a set of substation locations (squares) and the estimated service area sizes (thin lines) interpreted by simply assigning all points to the nearest substation.

Simple geometric analysis of equipment areas as shown in Figure 9.18 has become a common analytical building block in many trending methods, because it is easy to implement, requires readily available data, and provides improvement in forecast accuracy. The required input data are limited simply to the locations of all substations, in any convenient X-Y coordinate system. Analysis consists of estimating the relative sizes of the substation service areas, based only on these data. These estimated service area sizes are only approximate, and they are not useful as a forecast parameter in regression analysis or template matching. However, they do prove useful in helping various clustering algorithms group small areas into meaningful sets with different growth characteristics, and thus can lead to unproved forecasts. Typical of these newer trending methods is the LTCCT forecast method developed by the author and two colleagues (see Willis, Rackliffe, and Tram, 1992). LTCCT (load transfer coupled classified trending) combines geometric analysis, LTC regression, template matching, clustering, horizon year loads, and regression-based curve fitting in one algorithm. As computerized by the author, the method performs forecasts on a feeder basis, in six steps:

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1. Input data. These include: for each feeder, seven years of historical peak load data, and the name (or index number) and X-Y location of the substation to which the feeder is assigned. Based on the feeders assigned to each substation, the method computes the total number of feeders for each substation. Horizon year loads for feeders or substations are not input. 2. Estimate feeder area sizes. Substation area sizes are computed using a simple geometric analysis (Figure 9.19), and the area is allocated to all feeders assigned to the substation on an equal basis. If the service area for Eastside substation is estimated to be 15 square miles and it has six feeders assigned to it, then each is assigned 2.5 square miles of service area. 3. Remove load transfers. LTC regression is used to fit a cubic log polynomial to each feeder in the manner described earlier in section 6.3. The polynomial is then solved for its value in each of the historical years, and those values are substituted for the actual historical data values. The point here is to use LTC only to remove the effects of load transfers from the historical data. The curve fitted by LTC regression ignores most of the load transfer effects. Thus, its values are used as the "true" history of load growth for each feeder. 4. Cluster into sets based on characteristics. The feeders are clustered into sets using a K-means clustering algorithm. In the original algorithm on this method, the clustering was programmed to group feeders into exactly six sets. Since then, the author has determined that it is better to let the clustering algorithm determine the number of clusters itself. Clustering is done based on the amount of load growth from year 1 to 7 in the "corrected" historical load data, the estimated feeder area size, the amount of load transfers removed in step 3 (difference between history and corrected trend), and the number of feeders in the substation to which the feeder is attached. 5. Compare clusters based on the average characteristics of feeders in each. Among clusters whose feeders have higher than average load density (i.e., among those clusters whose average of [kVA load for the feeder in year 7]/[estimate feeder's area] is above the average for all feeders), the cluster with the lowest average growth rate is picked. The average load density (i.e., [kVA load for the feeder in year 7]/[estimate feeder's area]) in this cluster is defined as the "horizon year load density," H. Horizon year loads for all feeders (in all clusters) are now computed as Horizon year load for feeder n = H x (estimated area for feeder n) (9.8)

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The rationale for this step is that the cluster picked to compute H is composed of feeders with above average loadings, but little growth. Hence it probably consists of feeders near the final part of the S curve and close to a stable horizon year load. Thus, those feeders are used to compute H, which is then applied to all feeders. 6. Forecast future load for all feeders by fitting a cubic log polynomial to each feeder's "corrected" load history and horizon year load using multiple regression (LTC regression need not be used since the load histories have already been corrected for load transfers). The fitted curve to each feeder is extrapolated five years into the future to form a shortrange forecast. This LTCCT method requires only data that are easily available. It is fairly automatic in its use, and has substantially improved accuracy when compared to other trending methods, including the multivariate approach, as shown in Figure 9.19. Several other trending methods have been developed with similar "combinations" of regression, clustering, and template matching, and give similar performance improvements.

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1-1

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LTCCT method Multivariate Simulation

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Figure 9.19 Relative accuracy of various trending methods in forecasting feeder-level capacity needs, as a function of years into the future, evaluated in a hindsight test case using data from a utility in the southern United States, 1985-1995. All methods suffer from exponentially increasing levels of forecast error as the forecast is extended further into the future, but the rate of error increase with time varies considerably.

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9.5 SUMMARY "Trending" encompasses those small area forecasting methods that forecast future load growth by extrapolating trends among historical data. The simplest trending methods work only with historical peak data, which gives them both advantages and disadvantages over other forecasting methods. Foremost among their advantages is a wonderful compatibility with equipment-oriented small area data and a great economy of application. A feeder-by-feeder projection of future loadings using trending requires only the records of each feeder's past annual load peaks, and these data are nearly always available. Beyond their simple data needs, typically the computation methods required to apply trending are simple to understand, straightforward to implement, and quite easy to use. Many are totally automatic ~ the user does nothing beyond preparing the input data, running the program, and reviewing the output. On the downside, the severest problem associated with trending methods is poor forecast accuracy when applied to high resolution small area studies. The improvement to polynomial curve fitting made possible with the VAI and LTC methods should not blind the user to the basic handicap of trending methods. Trending methods have a difficult time handling the sharp S curve behavior common at the distribution level, which leads to forecast inaccuracy. When applying trending for distribution forecasting, recommended procedures include: 1. For grid-based small area definitions, multiple regression curve fitting of a cubic or cubic log polynomial, constrained with the VAI analysis. 2. For equipment-oriented area definitions, multiple regression curve fitting a cubic or cubic log polynomial using the LTC approach. 3. Where system peak load growth is less than three percent annually, six years of historical data (the six most recent years) and one horizon year (fifteen years in the future) should be used. 4. Where system peak load growth is more than three percent annually, seven years of historical data (the seven most recent years) and two horizon years (fifteen years and seventeen years in the future) should be used.

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5. Trending should be applied to forecast no more than five years into the future and to areas no smaller than feeders, whether using the cells of a square grid or as determined by equipment.

REFERENCES Electric Power Research Institute, Research into Load Forecasting and Distribution Planning, Electric Power Research Institute, Palo Alto, CA, 1979, EPRI Rep, EL1198. J. R. Meinke, "Sensitivity Analysis of Small Area Load Forecasting Models," in Proc. 10th Annual Pittsburgh Modeling and Simulation Conf. (Instrument Society of America, Pittsburgh, PA, Apr. 1979). E. E. Menge et al., "Electrical Loads Can Be Forecasted for Distribution Planning," in Proc. American Power Conf. (University of Illinois, Chicago, IL, Apr. 1977). H. L. Willis and J. E. D. Northcote-Green, "A Hierarchical Recursive Method for Substantially Improving Trending of Small Area Load Forecasts," IEEE Transactions on Power Apparatus and Systems, June 1982, p. 1776. H. L. Willis and J. E. D. Northcote-Green, "Distribution Load Forecasting Based on Cluster Template Matching," IEEE Transactions on Power Apparatus and Systems, June 1984, p. 3082. H. L. Willis, R. W. Power, and H. N. Tram., "Load Transfer Coupling Regression Curve Fitting for Distribution Load Forecasting," IEEE Transactions on Power Apparatus and Systems, May 1984, p. 1070. H. L. Willis, G. B. Rackliffe, and H. N. Tram, "Short Range Load Forecasting for Distribution System Planning—An Improved Method for Extrapolating Feeder Load Growth," IEEE Transactions on Power Delivery, August 1992, p. 2008 V. F. Wilreker et al., "Spatially Regressive Small Area Electric Load Forecasting," in Proc. IEEE Joint Automatic Control Conference 1977 (San Francisco, CA).

10 Simulation Method: Basic Concepts 10.1 INTRODUCTION Simulation-based distribution load forecasting attempts to reproduce, or model, the process of load growth itself in order to forecast where, when, and how load will develop, as well as to identify some of the reasons behind its growth. In contrast to trending methods, simulation uses a completely different philosophy, requires more data, and works best in a very different context. It is best suited to high spatial resolution, long-range forecasting, and ideally matched to the needs of multi-scenario planning. Most important, when applied properly, it can be much more accurate than the best trending techniques. For these reasons, simulation has become the neplus ultra of T&D load forecasting. One of the most important qualities of simulation is that it works well when applied at high spatial resolution — when the study region is divided into very small small areas. Its accuracy as a function of spatial resolution is the exact opposite of trending's. While trending is most suited to "large area" forecasting and becomes quite inaccurate when applied to smaller and smaller areas, many simulation techniques will not function well or at all if the small areas are larger than a square mile and provide improved accuracy as spatial resolution is further increased (i.e., as small area size is further decreased) to 160 acres or 40 acres or even less. Surprisingly, some of the best simulation methods also become easier to apply and less sensitive to data error as small area size is reduced.

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One point about which there is no arguing, however, is that simulation methods require more data and more user involvement than trending methods. Their data base needs can seem voluminous, difficult to obtain, and more difficult still to verify and maintain, particularly as described in the step-by-step forecast narrative given in Chapter 11, in which all data are collected and processed manually. Actually, data collection is seldom a burden given the capabilities of modern computing systems. Land-use data bases, geo-coded population and business demographic data bases, satellite imagery, a host of other sources, and utility consumer information systems make assembling data for a computerized simulation method straightforward. In addition, a considerable body of research on both accuracy requirements and man-machine time and effort requirements have led to the development of data collection methods that are remarkably effective and accurate (Tram et al., 1983). In this chapter, the first of five on simulation, the basic concepts behind simulation and the way it is implemented are introduced. Chapter 11 will then "walk through" a simulation forecast step by step, looking in detail at the data collection, analysis, and interpretation of a realistic forecast problem. Chapters 1 2 - 1 4 discuss variations on the simulation method and computerization of simulation and simulation data bases.

10.2 SIMULATION OF ELECTRIC LOAD GROWTH De-Coupled Analysis of the Two Causes of Load Growth Simulation addresses the two causes of load growth by trying to model or duplicate their process, directly but separately. As was described in Chapter 7, section 3, electric load will grow (or decrease) for only two reasons: •

Change in the number of consumers buying electric power

•

Change in per capita consumption among consumers

If the electric demand in a power system increases from one year to the next, it is due to one or both of these causes. Simulation forecast methods model possible changes in consumers and possible changes in per capita consumption using separate but coordinated models of each. This is quite a contrast to the trending techniques discussed in

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Simulation Method: Basic Concepts

Consumer Growth

Spatial Analysis Temporal Analysis

Per Capita Growth

Handled by spatial land- I not done use model not done

I Handled by | temporal | end-use model

Figure 10.1 Simulation applies separate models to the analysis of consumer count as a function of location and consumer per capita consumption of electricity as a function of time. It thus "de-couples" load growth analysis in two ways, by cause (consumers, per capita), and by dimension (spatial, temporal).

Chapter 9, which treated all changes in load as of the same cause (just a change in load, without explanation beyond the fact that it happened). There is a further distinction in almost all simulation methods. One part of the model handles all the spatial analysis, the other part all the temporal (hourly, seasonal) analysis. Consumer modeling is done on a spatial basis, tracking where and what types and how many consumers are located in each small area. The per capita analysis does not consider location, only variation in usage as a function of consumer type, end-use, and time of day, week, and year. Thus, simulation decouples not only the causes of load growth, but also the dimensions of the forecast, as shown in Figure 10.1. Land-Use Consumer Classes Simulation methods distinguish consumers by class. Both the modeling of consumers and the modeling of per capita usage are done on a consumer class basis using definitions of various residential, commercial, and industrial classes and subclasses, as was introduced in Chapter 3's discussion of end-use models. The consumer and per capita models are coordinated by using the same consumer classes in each.

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Classification of consumers by type allows both the consumer and per capita models to distinguish consumers by different types of behavior. Three basic important types of behavior can be distinguished: Rate classes. Classification of consumers into residential, commercial, large commercial, industrial, etc., is closely related to the rate classes used in the definition of consumer billing and buying data in utility consumer information systems. Thus, it is easy to interface simulation methods with utility consumer, rate, and load research data and to interpret the results directly to the Rate, Revenue, and Market Research functions in the utility. Per capita consumption classes. As presented in Chapter 3, residential, commercial, and industrial consumers differ in how much and why they buy electric power, in daily and seasonal load shapes, and in growth characteristics and patterns. Spatial location classes. Residential, commercial, and industrial consumers seek different types of locations within a city, town, or rural area, in patterns based on distinct differences in needs, values, and behavior, patterns which are predictable. The term "land-use" is often applied to simulation methods because this locational aspect of distinction is the most visible and striking of the three types of distinctions made by the class definitions. This last application is the basis for the spatial forecasting ability of simulation methods. Within any city or region, land is devoted to residential use (homes, apartments), commercial use (retail strip centers, shopping malls, professional buildings, office buildings and skyscrapers), industrial use (factories, warehouses, refineries, port and rail facilities), and municipal use (city buildings, waste treatment, utilities, parks, roads). Each of these classes has predictable patterns of what type of land they need in order to accomplish their function and explainable values about why they locate in certain places and not in others. Their locational needs and preferences can be utilized to forecast where new consumers will most likely locate in the future. For example, industrial development is very likely to develop alongside existing railroads (93% of all industrial development in the United States occurs within 1/4 mile of an existing railroad right-of-way). In addition, new industrial development usually occurs in close proximity to other existing industrial land use. These two criteria ~ a need to be near a railroad and near other industrial


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development — identify a very small portion of all land within a utility region as much more likely than average to see industrial growth. Addition of a number of other, sometimes subtler factors can further refine the analysis to where it is usually possible to forecast the locations of future industrial development with reasonable accuracy. By contrast to industrial, residential development has a tendency to stay away from railroads (noise, pollution) and industrial areas (poor esthetics, pollution). Residential development has its own list of preferences ~ close to good schools, convenient but not too close to a highway, near other residential areas — that can be used to further identify areas where residential development is most likely. In fact, every land-use class has identifiable, and different, locational requirements. No American would be terribly surprised to find tall buildings in the core of a large city, or a shopping mall at the suburban intersection of a highway and a major road. On the other hand, most people would be surprised to find low-density housing, large executive homes on one to two acre lots, being built at either location. It wouldn't make sense. Modern simulation methods project future consumer locations by utilizing quantitative, spatial models of such locational preference patterns on a class basis to forecast where different land uses will develop (Willis and Tram, 1992). They use a separate forecast of per capita electric usage, done on the same class definitions, to convert that projected geography of future land use to electric load on a small area basis. In so doing, they employ algorithms which are at times quite complex and even exotic in terms of their mathematics. But the overall concept is simple, as shown in Figure 10.2: forecast where future consumers will be, by class, based on land-use patterns, forecast how much electricity consumers will use and when by class, then combine the two forecasts to obtain a forecast of the where, what, and when of future electric load. Overall Framework of a Simulation Method The land-use based simulation concept can be applied on a grid or a polygon (irregularly shaped and sized small areas) basis. Usually, it is applied on a grid basis, which the author generally recommends. A grid basis assures uniform resolution of analysis everywhere. In addition, some of the high-speed algorithms used to reduce the computation time of the spatial analysis work only if applied to a uniform grid of small square areas. Simulation methods are generally iterative, in that they extend the forecast in a series of computational passes, transforming maps of consumers and models of per capita consumption from one year to another as shown in Figure 10.3. Forecasts covering many future years are done by repeating the process several times. An iteration may analyze growth over only a single year (i.e., 1997 to 1998) or may project the forecast ahead several years (1998 to 2001).

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Base year locations of customers

Base year per capita consumption

1. GLOBAL FORECAST Determine the increase in number of customers that will occur somewhere in the service territory.

S 2. SPATIAL FORECAST Determine where the new customers will locate (allocate them to small areas).

Hour

X 3. LOAD CURVE FORECAST Determine changes in per capita use of electricity by time of day.

Combine locations of customers and changes in usage to obtain the small area load forecast

Figure 10.2 Simulation methods project consumer locations and per capita consumption separately, then combine the two to produce the final small area forecast.

309


Year 1 database

SIMULATION METHOD Translates year 1 database into year 2 representation

Year 2 database

Figure 10.3 Simulation is applied as shown, transforming its data base from a picture of consumers and consumption in year t to a projection of consumers and consumption in year t + p.

Step-function models Simulation methods have proven quite good at forecasting vacant area growth — in fact that is among their greatest strengths compared to other spatial forecasting methods. However, they are not particularly good at forecasting incremental growth on a small area basis — as in representing that development in a particular area will go from vacant to only 20% developed hi the first year of growth, then continue to reach 50% developed by the following year, reach 80% the year after that, and only achieve 100% in the fourth year. Instead, unless considerable effort is put into modification of the basic approach, simulation methods tend to forecast a transition from "vacant" to "fully developed" in one jump, or from one type of land-use (for example, old homes) to another (high rises replacing them) in that same period. Essentially, simulation methods forecast step functions in development, the ultimate S curve — with a single year transition, as shown in Figure 10.4 (Brooks and Northcote-Green, 1978).

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100

Q

Q.

0

1

2

3

4

5

6

7 8 9 10 11 12 13 14 15 YEAR

Figure 10.4 Most simulation methods forecast growth as step functions in development (solid line). This matches small area growth characteristics as seen at very high spatial resolutions, but not growth characteristics as they appear at lower spatial resolutions (on a large area basis). Dashed line shows the average historical land-use development curve for the sixty-four 10 acre small areas that comprise the square mile around the author's home in the Raleigh-Durham-Cary region of North Carolina. Dotted line shows the development of the square mile as a whole, being smoother because not all of the 10 acre small areas within it go through their rapid growth ramp in the same year.

This is the reason why simulation works better at high spatial resolutions. Its forecast characteristics are compatible with the growth characteristics of electric load as they appear when modeled at high spatial resolution. As described in Chapter 7, the smaller the areas used in forecast analysis, the shorter the transition period of growth, and the sharper the S curve shape. Thus, many simulation methods are much more accurate when applied at very high resolution, where their characteristic of forecasting a vacant small area as growing from zero to 100% growth in one to two years is a good match for what actually occurs. S curve behavior this sharp occurs in the range of 2 to 10 acre spatial resolution. The way to accurately forecast the growth of a 640 acre (square mile) area with simulation of this type is to divide it into 256 small areas of 2.5 acres each and explicitly forecast each. By using high-speed algorithms that can only forecast transitions, it is possible to keep run time reasonable, so that such a high resolution forecast takes only about 5 to 10 times as long. This will be discussed further in section 14.3.


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Structure of Simulation Methods More than fifty different computerized methods, many quite different from one another, have been developed to apply the simulation approach. These vary from simple and approximate to quite involved numerical approaches. However, there are some common steps which must be accomplished one way or another in the forecasting of spatial consumer location: Global consumer counts. A spatial model must account for the overall total of each consumer class within the utility service territory as a whole. Generally, these "global totals" are an input to the simulation method. Most utilities have a forecasting group separate from T&D planning — usually called Rate and Revenue Forecasting or Corporate Forecasting ~ that studies and projects total system-wide consumer counts (see Chapter 1 section 3). The author strongly recommends that all electric forecasts used for electric system planning be based upon, driven by, or corrected to agree with this forecast in an appropriate manner, as described in section 1.3 (see Lazzari et al., 1965 and Willis and Gregg, 1979). Interaction of classes. The developments of the various land-use classes within a region are interrelated with respect to magnitude and location. The amount of housing in a region matches the amount of industrial activity ~ if there are more jobs there are more workers and hence more homes. Likewise, the amount and type of retail commercial development will match the amount and type of residential population, and so forth. These interrelationships have a limited amount of spatial interaction, too. If industrial employment on the east side of a large city is growing, residential growth will tend to be biased heavily toward the east side, too. In general, these locational aspects of land use impact development only on a broad scale — urban model concepts help locate growth to within three to five miles where it occurs, but no closer (this will be discussed in section 10.4). There are myriad methods to model the economic, regional, and demographic interactions of land-use classes. All are lumped into a category called "urban models." Many urban models are appropriate only for analyzing non-utility or non-spatial aspects of land-use development, but some work very well in the context of spatial load forecasting. One such method is the Lowry model, which represents all land-use development as a direct consequence of development of what is termed "basic industry" — industry that markets outside the region being studied

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Chapter 10 (Lowry, 1964). In the Lowry model concept, growth starts through the development of "basic industry" such as refineries, factories, manufacturing, and tourism, which create jobs, which create a need for housing (residential development), which creates a local market demand for the services and goods sold by retail commercial, and so forth.

Locational land-use patterns. Ultimately the spatial forecast must assign land-use development to specific small areas. While overall system-wide totals and urban model interactions establish a good basis for the forecast, it remains for the spatial details to be done based on the distinction of land-use class locational preference as described earlier in this chapter. The determination of exactly where each class is forecast to develop has been handled with a wide variety of approaches. These range from simple methods utilizing little more than the planner's judgement (as in the manual method discussed in Chapter 11) to highly specialized, computerized pattern recognition techniques that forecast land-use location automatically (as will be discussed in Chapters 12-16). But one way or another, a spatial forecast must ultimately assign consumer class growth to small areas. Whether judgment-based or automatic, it must acknowledge and try to reproduce the types of locational requirements and priorities, and model how they vary from one class to another, as discussed earlier in this chapter. Most simulation methods accomplish the three tasks described above with an identifiable step to accommodate each, with the steps usually arranged in a top down manner. A top down structure can be interpreted as starting with the overall total (whether input directly or forecast) and gradually adding spatial detail to the forecast until it is allocated to small areas. The forecast is done first as overall total(s) by class, then each total sum of growth for the service area is assigned broadly on a "large area basis" using urban model concepts, and finally the growth on a broad basis is assigned more specifically to discrete small areas using some sort of land-use analysis on a small area basis Alternatively, a bottom up approach can be used in which the growth of each small area is analyzed and the forecast works upward to an overall total, usually in a manner where it can adjust its calculations so it can reach a previously input global total (i.e., the "corporate" forecast). Chapter 12 discusses the variety of approaches used and their algorithms in more detail. Per capita consumption is usually forecast with some form of consumer-class daily load curve model, very often one employing end-use analysis as described in Chapter 4. Regardless of approach, the per capita consumption analysis must accommodate two aspects of load development on a consumer class basis:


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Forecast of demand per consumer and its coincidence with other classes. Both the actual peak per consumer as well as the contribution to peak (the two may occur at different times) need to be forecast. Future changes in the market share of various appliances, appliance efficiency, and usage patterns from year to year need to be included in the forecast. This can be done by "inputting" the class curves as previously output from a separate end-use analysis effort, or by end-use analysis within the simulation method itself (Canadian Electric Association, 1986). Thus, the structure of most simulation methods is something like that shown in Figure 10.5. Again, Chapter 12 will provide a more detailed look at the techniques applied in each of the steps, but in one manner or another, a simulation forecast method must address the type of analysis shown in each module in Figure 10.5.

Figure 10.5 Overall structure of a simulation method. In some manner every simulation method accomplishes the steps shown above, most using a process framework very much like this one.

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10.3 LAND-USE GROWTH: CAUSE AND EFFECT This section presents the central tenet of the Lowry model with a hypothetical example of industrially driven growth. A city, town, or rural region can be viewed as a "socio-economic machine," built by man to provide places for housing, places to work, places to shop for food, clothing, and services, and facilities that allow movement from one place to another. Cities and towns may differ dramatically in appearance and structure, but all provide for their population's residential, commercial, industrial, and transportation needs in functional proportion to one another. Urban models are representations of how these different functions interact. There are literally dozens of different approaches to modeling how a city, town, or agricultural region functions. Some are simple enough that they do not need to be computerized to be useful, whereas others involve quite complicated numerical analysis. To illustrate the concepts of urban growth and urban modeling, consider what would happen if a major automobile company were to decide to build a pickup truck factory in an isolated location, for example in the middle of Kansas one hundred miles from any city. Having decided for whatever strategic reasons to locate the factory in rural Kansas, the auto maker would probably start with a search for a specific site on which to build the factory. The site must have the attributes necessary for the factory: located on a road and near a major interstate highway so it is accessible, adjacent to a railroad so that raw materials can be shipped in and finished trucks can be shipped out, near a river if possible, so that cooling and process water is accessible and also permitting barge shipment of materials and finished trucks. Figure 10.6 shows the 350 acre site selected by the auto maker, which has all these attributes. In order to function, the pickup truck factory will need workers. Table 10.1 shows the number by category as a function of time, beginning with the first years of plant construction and start up, through to full operation. Once this factory is up to full production, it will employ a total of 4,470 employees. Since there are no nearby cities or towns, the workers will have to come from other regions, presumably attracted by the prospect of solid employment at this new factory. Assume for the sake of this example that the auto maker arranges to advertise the jobs and to help workers re-locate. These workers will need housing near the factory — remember the nearest city or town is a considerable commute away. Using averages based on nationwide statistics for manufacturing industries, the people in each of the employment categories are likely to want different types of housing in roughly the proportions shown in Table 10.2.

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Figure 10.6 The hypothetical factory site at an isolated location (shaded area).

Table 10.1 Expected Pickup Truck Factory Employment by Year Year

Employment Category Management Professional Skilled labor Unskilled labor TOTALS

8

8 40 250 300 598

20 180 1400 900 2500

30 240 3100 1100 4470

20

25

30 240 3100 1100 4470

30 240 3100 1100 4470

12

30 240 3100 1100 4470

Table 10.2 Percent of Housing by Employment Category Employment Category Management Professional Skilled labor Unskilled labor

Large Houses 50 10 2 0

Med-Sm. M.F.H. Houses (apts, twnhs) 45 82 70 25

30 75

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Using the data in Table 10.2, one predicts that the 4,470 workers listed in Table 10.2 will want a total of 39 executive homes, 2,656 medium sized homes, and 1,775 apartments. Using typical densities of housing type (this will be explained in detail in Chapter 11) this equates to 57 acres of executive homes, 830 acres of normal single family homes, and 291 acres of apartments, for a total of 1,178 acres (nearly two square miles) of housing. Assume for the sake of this example that a road network is built around the site, and that the 1,178 acres of homes are scattered about the factory at locations that both match "residential land-use needs and preferences" and are in reasonable proximity to the factory, as shown in Figure 10.7. Based on nationwide statistics for this type of industry, there will be 2.8 people in each worker's family (there are spouses and children, and the occasional parent or sibling living with the worker). This yields a total of 12,516 people living in these 1.173 acres of housing. These people need to eat, buy clothing, get their shoes repaired, and obtain the other necessities of life, as well as have access to entertainment and a few luxuries. They will not want to

Single family homes Mufti-famity homes

Figure 10.7 Map showing the location of the pickup truck factory, to which has been added 1,173 acres of housing, representing the residential land use associated with the homes that will be needed to house the 4,470 workers for the factory and their families.


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drive 100 miles or more to do so. They create a local market, and a cross section of stores, movie theaters, banks, restaurants, bars, TV repair shops, doctors offices, and other facilities will develop to serve them, as shown in Figure 10.8. There will also need to be schools to educate the children, and police and fire departments to protect all those homes, and a city hall to provide the necessary infrastructure to run these facilities. And of course, there will have to be an electric utility, too, to provide the electricity for the factory, as well as all those homes and stores. These stores, shops, entertainment places, schools, and municipal facilities create more jobs, but there is a ready supply of additional workers. Based on typical statistics for factories in North American, one can assume that of the 4,470 factory workers, 3,665 (82%) will be married, and of those 3,665 spouses, about 2,443 (two-thirds) will seek employment. However, the stores, shops, schools, and other municipal requirements just outlined create a total of nearly 5,000 jobs (more than one for every one of the original factory jobs!) for a net surplus of 1,400 jobs unfilled.

N

One Mile ttS* Single family homes Multi-family homes «n Commercial

Figure 10.8 Map showing the factory, the houses for the factory workers, and the locations of the commercial areas that develop in response to the market demand for goods and services created by the factory workers.

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This rapidly growing little community will need even more workers to fill these jobs. Again, using typical values: At 1.55 employees per family (Remember, both husband and wife work in some families), these 1,400 additional jobs mean a further 1,400/1.55 = 903 families to house, for an additional population of 2.8 x 903 families = 2,529 more people. Assuming these 903 families have a cross section of housing demands like the original 4,470 factory workers,1 one can calculate a need for a further 8 executive homes (12 acres), 537 normal homes (168 acres), and 358 apartments (59 acres). These additional 2,529 people create a further need for shops and entertainment, police, fire, and municipal services, generating yet another 868 jobs, requiring housing for yet another 560 families (1,569 people), requiring a further 7 acres of executive homes, 104 of normal homes, and 37 acres of apartments. And of course those people require even more stores and services, creating jobs for yet another 347 families. This cycle eventually converges to a total population (counting the original factory workers and their families) of slightly more than 19,000 people, requiring a total of 87 acres of executive homes, 1,270 acres of medium size homes, and 547 acres of apartments. Assuming that all the commercial needs and densities are similar to those in many small towns, the commercial and industrial services sector needed to support this community of 19,000 people will require 396 acres, making the total land use needs of this small community very nearly four square miles, not counting any parks, playgrounds, or other restricted land. Essentially, the pickup truck factory, if built in the middle of Kansas, would bring forth a small community around it, a "factory town" which would eventually look something like the community mapped in Figure 10.9. 1 Actually, this isn't a really good assumption. The cross section of incomes, and hence housing demands, for employees in these categories is likely to be far different than for the factory workers. However, to simplify things here, it is assumed the same net percentage of housing per person as with the factory.


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*1& Single family homes •~5&- Multi-family homes Commercial

Figure 10.9 The ultimate result of the factory, a small "factory town" with housing, commercial retail, commercial offices, schools, utilities, and everything else needed to support a population approaching 20,000. This community "earns its living" building pickup trucks — the factory is the driving force behind the entire community's economy.

Table 10.3 Total Land Use Generated by the Pickup Truck Factory in Acres Year Land Use Class Residential 1 Residential 2 Apartments/twnhses. Retail commercial Offices Hi-rise Industry Warehouses Municipal Factory Peak Community MVA

2

4

12 170 73 22 8 3 12 6 1 350

49 710 305 94 34 12 50 27 5 350

87 1,270 547 168 61 22 89 48 8 350

87 1,270 2547 168 61 22 89 48 8 350

16

34

73

77

8

12

20

25

87 87 1,270 1,270 547 547 168 168 61 61 22 22 89 89 48 48 8 8 350 350 77

77

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In a manner similar to the analysis carried out here, the intermediate years of factory startup, before all 4,470 workers were employed, could be analyzed to determine if and how fast the community would grow. The employment numbers from Table 10.1 on a year by year basis can be analyzed to yield the year by year land use totals shown in Table 10.3. Working with projections of electric demand provided by the factory (doubtless, the auto maker's architects and plant engineers can estimate the electric demand required by the factory quite accurately), and using typical per capita electric demand data from mid-North America, this information leads to the forecast of total load given in the last line of the table. A common shortcut often used in electric load forecasting is to convert a Lowry type model as discussed above to a geographic area basis, in which all factors are measured in acres (or hectares, or square kilometers) instead of in terms of population count. Taking the numbers from the example above, a factory of 350 acres caused a demand for: 57 acres of executive homes, or .163 acres per factory acre 830 acres of single family homes, or 2.37 acres per factory acre 291 acres of multi-family housing, or .83 acres per factory acre This means that on a per acre basis, the factory causes . 163 acres of executive homes per factory acre 2.37 acres of single family housing per factory acre .83 acres of multi-family housing per factory acre Similar ratios can be established by studying the relationship between other land use classes, as for example, the ratios of the land-use development listed in Table 10.3. The Lowry model can then be applied on an area basis, without application of direct population statistics. Little loss of accuracy or generality occurs with this simplification. Techniques to apply this concept will be covered in Chapters 13-15. A Workable Method for Consumer Forecasting The example given above showed the interaction of industrial, residential, and commercial development in a community and illustrated how their relationships can be related quantitatively and in a cause-and-effect manner. This example was realistic, but slightly simplified in the interests of keeping it short while still making the major points. In an actual forecasting situation, whether done on a population count or a geographic area basis, the ratios used (such as 2.8 persons per household, 1.55 workers per household, etc.) could be based on local, industry-specific data (usually available from state, county, or municipal planning departments), rather than generic data as used here.


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In addition, the actual analysis should be slightly more detailed. For example, in this example it was assumed that the employees in the retail commercial and industrial areas had the same per capita housing demands and market demands as the new factory workers. In fact, this is unlikely. Salaries at the factory are probably much higher than the average retail or service job, and the skilled workers for the factory will tend to have a different distribution of ages, family types, and spending habits than workers in retail and service industries. Thus, the additional housing and market demand created by the retail and services sector of the local community would have slightly different housing and market impacts per worker than the factory. To account for this local variation, industry-specific data for commercial and services businesses could be obtained to refine such an analysis, or generic data based on nationwide statistics could be used in the absence of anything better. However, the basic method presented above is sound, only the data and level of detail used would need to be refined to make it work well in actual forecasting situations. Locational Forecasting While not highlighted during the example above, it is worth noting that Figures 10.6-10.9 showed where the growth of residential, commercial, and industrial development was expected. The locational aspects of land use fall naturally out of such an analysis. Land-use locational preference patterns of the type discussed earlier can be used to derive realistic patterns of development. As discussed earlier, industrial development seeks sites with certain attributes, while residential development occurs in locations with quite different local attributes. In the real world, a forecaster would use knowledge of these needs and locational patterns to help determine where growth of each of the consumer/landuse types was most likely to occur. Application of such "pattern recognition" is a key factor in most simulation methods, as will be discussed in both Chapters 12 and 13. 10.4 QUANTITATIVE MODELS OF LAND-USE INTERACTION To a very great extent, the factory's impact on development around it would be independent of where it was built. Dropped into a vacant, isolated region such as used in the example above, its effects are easy to identify. However, if that factory were added to Atlanta, Syracuse, Denver, Calgary, San Juan, or any other city, the net result would be similar ~ 4,470 new jobs would be added to the local economy. This increase in the local economy would ripple through a chain of cause and effect similar to that outlined above, leading to a net growth of about 19,000 population, along with all the demands for new housing, commercial services, and other employment that go along with it.

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The development caused by the factory would be more difficult to discern against the backdrop of a city of several million people, a city with other factories and industries, and with other changes in employment occurring simultaneously. But it would still be there. A complicating factor would be that the new housing and commercial development caused by the factory would not necessarily happen in areas immediately adjacent to it, but would have to seek sites where appropriate vacant space was available, which might be farther away than in the example given. A further complication would be that if local unemployment was high, or if other mitigating circumstances intervened, some of the 4,470 jobs created by the factory might be absorbed by local unemployment. Determination of the net development impact would be more complicated, requiring a comprehensive analysis of those factors. However, in principle the train of development given in section 10.3 is a valid perspective on how and why land-use development occurs and one that works well for forecasting. The small community of 19,000 pictured above earned its living building pickup trucks. Only that segment of the local economy brought money into the community. The jobs in grocery stores, gasoline stations, shoe repair shops, bars, doctors offices, and other businesses that served the local community did nothing to add to that. Thus, the fortunes of this small town can be charted by tracking how it fares at its "occupation." If pickup trucks sell well and the factory expands, then the city will do well and expand. If the opposite occurs, then its fortunes will diminish. So it is with nearly any town or city. Its local economy is fueled by only a portion of the actual employment in the region, and a key factor in forecasting its future development is to understand what might happen to this basis of its economy. Large cities generally are a composite of many different basic industries ~ a city like Edmonton, or Boston, or St. Louis, or Buenos Aires "earns its living" from a host of local basic industries. By definition, these basic industries are any activity that produces items marketed outside the region ~ in other words an activity that brings money in from outside the region. In section 10.3's example, only the truck factory is basic industry. Double the factory's employment, the Lowry concept states, and the entire town will eventually double in size. Double the number of grocery stores, shops and movie theaters (none of which "market" outside the local economy) and nothing much would happen, except that eventually a few grocery stores, shops and theaters would go out of business - the town "earns its living" from the factory and its local population will only support so many stores. Moreover, whether a city is growing, shrinking, or just changing, its total structure will remain in proportion to the local "basic industrial" economy, in a manner qualitatively similar to that outlined in section 10.3, whatever the causes


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of that change. If a city is going to grow, all of its parts will grow in an interrelated, at least partially predictable, manner related to the local basic industry. Urban Models The concepts of urban modeling covered here can be applied to predict how a city will grow, and in what proportions its various classes of land use will respond to such stimuli as the construction of a new pickup truck factory or a large government research facility, or for that matter, how the loss of a steel mill or the closing of a military base will reduce the local community. This particular concept is useful, and has wide application, but there are situations where it needs modification or interpretation. This is particularly true of cities or regions where the major source of local "employment" is retirement or pension income. In communities like Sun City, Arizona, or Sarasota, Florida, a significant portion of the local economy is driven by the residential class (or a portion of it, anyway) which has in effect become a basic industry, bringing income in from outside the region via pensions, social security, and investment income. There are many different types of urban models. All work with a structure of relationships among different demographic segments in a community ~ rules and equations similar in spirit to those used in the example above even if different in detail. Not all urban models are useful for electric load forecasting. Many have been designed to study quite different aspects of urban growth and structure than those given here. But a good number have been applied to utility forecasting with good success, and the most advanced simulation-based small area forecasting programs utilize urban models or urban modeling concepts to control the land-use inventory totals. For example, the multivariate model used in EPRJ project RP-570 worked in conjunction with an urban model called EMPIRIC. Many modern simulation methods work with either Lowry models or modified forms of the Lowry industrial-growth-causes-residential-growth-causescommercial-growth model. These will be discussed in more detail in Chapters 12 to 14. Although some of these ideas can be applied manually, most urban models require a detailed, formally structured set of equations implemented by a computer ~ a computerized urban model. This is true of the Lowry model outlined above. When properly computerized and coupled with pattern recognition of land-use development to help locate the growth to specific small areas, it serves as a very powerful tool for forecasting the what, when, and particularly the where of future electric consumer growth.

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10.5 SUMMARY OF KEY CONCEPT A city, whether a megaopolis, a metropolitan area, or just a big town, is a mechanism mankind has developed for his use. All are composed of parts residential, commercial and industrial, along with transportation and support infrastructures - that are all mutually supporting and interrelated. If a city is going to grow, all of its parts will grow in an interrelated, at least partially predictable, manner related to the local basic industry. This applies to all cities, regions, and towns without any exception. Chapters 18 - 20 will explore this concept, in detail and with regard to several specific directions of analysis useful for planners. But overall, forecasters can use this concept, embodied in a quantitative analysis of its various implications, to forecast a new city's future, including spatial electric load. This is the central concept behind simulation forecasting methods, but more fundamentally, this is the most useful perspective from which to view growth of electric load. Forecasters who use this concept as a guide and who grow themselves to understand its mechanism will be better forecasters. REFERENCES C. L. Brooks and J. E. D. Northcote-Green, "A Stochastic Preference Technique for Allocation of Consumer Growth Using Small Area Modeling," in Proceedings of the American Power Conference, University of Illinois, Chicago, IL, 1978. Canadian Electric Association, Urban Distribution Load Forecasting, final report on CEA Project 079D186, Canadian Electric Association, 1986 A. Lazzari et al., "Computer Speeds Accurate Load Forecast at APS," Electric Light and Power, Feb. 1965, pp. 31-40. I. A. Lowry, A Model of Metropolis, The Rand Corporation, Santa Monica, CA, 1964. H. N. Tram et al., "Load Forecasting Data and Database Development for Distribution Planning," IEEE Trans, on PAS, November 1983, p. 3660. H. L. Willis and J. Gregg, "Computerized Spatial Load Forecasting," Transmission and Distribution, May 1979, p. 48. H. L. Willis and H. N. Tram, "Distribution Load Forecasting," Chapter 2 in IEEE Tutorial on Distribution Planning, Institute of Electrical and Electronics Engineers, Hoes Lane, NJ, February 1992.

11 A Detailed Look at the Simulation Method 11.1 INTRODUCTION This chapter is structured much differently from previous chapters. It presents in narrative form, and almost painful detail, the story of a distribution planner who performs a complete simulation-based distribution load forecast manually. Manual application of the simulation approach is usually impractical, because data volume alone makes non-computerized studies much too time-consuming. Beyond that, a computer is really necessary to properly analyze and balance the myriad factors that a simulation might address while producing a forecast. This narrative is presented because the author knows of no better way to communicate the details of the simulation process, along with the subtleties involved in its application than to "walk through" a forecast, step by step. In addition, this presentation highlights the many dimensions of electric load and consumer growth in a thorough manner, making it a good lesson about how and why T&D grows as it does. While fictional, the example given here is realistic, based upon an actual forecast the author did manually, partly as a training exercise and partly as a production forecast, for a municipal utility in 1984. All simulation, whether manual or computerized, uses the basic approach introduced in Chapter 10 and described here, a deceptively simple concept that can yield absolutely stunning results when used appropriately. Thus, the forecast method described is a viable

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SPRINGFIELD LIGHT & POVER CO. Service Territory and Distribution Substations

Figure 11.1 Map of the fictitious city of Springfield and its electric system substations.

forecasting approach, albeit a very labor-intensive one. When applied to very small towns or cities, the effort required to use the procedure described here is not so great as to preclude practical application, but for large cities, application of simulation is unthinkable without some degree of computerization to speed the process. Thus, this chapter provides a "cookbook" for any planner who wishes to do a manual simulation, as well as a step-by-step explanation of the basic steps through which all computerized methods proceed. Chapters 1 2 - 1 6 discuss computerization and application of computerized methods for implementation of the simulation approach.

11.2 SPRINGFIELD The forecasting problem presented here concerns a small, growing city of slightly more than 125,000 population, shown in Figure 11.1. This fictitious city

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is named Springfield, simply because it is an attractive name, and because in the author's experience there are more Springfields in the United States than cities of any other name.1 Most of these other Springfields are medium-sized, pleasant communities, just as this little Utopian community is supposed to be. The distribution system consists of thirteen substations all serving 12.47 kV primary feeders. In addition, there is some old 4.16 kV distribution still in service out of Downtown and Riverside substations, equipment dating from nearly 60 years earlier, which the local utility, Springfield Light and Power (SL&P), would love to retire but cannot afford to replace. The distribution planner, Susan Gannon, has been with Springfield Light and Power's System Planning Department for ten years. It is her responsibility to update the company's long-range distribution budget and construction plan every three years, and it is time to undertake this project again. The first step in her new plan is a forecast of Springfield's distribution loads. She will do her forecasting for a selected set of future planning years ~ two, four, eight, twelve, twenty, and twenty-five years ahead. The final time period is beyond Susan's twenty-year planning horizon, but she has decided to produce a load projection five years beyond that time frame as a check on her forecast. A small inconsistency or inaccuracy in the twenty-year forecast will often become more readily apparent when viewed as part of a further five years of projection into the future. Growth and Growth Influences Taken as a whole, Springfield's peak electric load, which occurs in the summer, has been growing at an annual rate of nearly 3% over the past decade. Susan's colleagues in her company's Rate and Consumer Studies Department have projected that the summer peak load will continue to grow, but at a lower rate in the next decade. Summer peak load growth is expected to drop to between 2% and 2.5% annually, due to a conservation and load management program that Springfield Power and Light is promoting quite aggressively. During the last decade, the winter peak has been growing at over 4% annually, faster than the summer peak due to a shift away from oil and gas heating to electric heat. This trend has accelerated among the residential and small business classes since Springfield Light and Power began a program of promoting heat pump usage, offering cash incentives to home and store owners

1

The author has seen statistics that show "Greensberg" is the most popular community name in the United States. However, he has personally run into many more Springfields than Greensbergs.

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700

600 Summer 500

400

300

200

L

-10

-5

5

10

15

20

25

Years Ahead

Figure 11.2 Actual (broad lines) and weather-corrected forecasts (thin lines) for winter and summer system peak load of the Springfield Light and Power system.

who install both extra insulation and a new, high efficiency heat pump.2 Rate and Consumer Studies predicts that the winter peak will continue to grow at between 4 and 5%, and will exceed the summer peak in year seven, as shown in Figure 11.2. Of much interest to Susan and her department is a new pickup truck factory that may be built in the Springfield area within the next two years. The newspapers are full of rumors and expectations that this economic plum will be built on a site just north of the city. Susan knows that the Springfield City Council is actively negotiating with Nippon-America Motors, a huge JapaneseAmerican conglomerate, to influence them to locate their new factory in the 2 It is not unusual to see a utility simultaneously taking measures to cut its summer peak while encouraging more usage in winter, or vice versa if it's a winter peaking utility. Springfield's program makes a lot of sense. Cutting the summer peak while increasing winter sales will improve the utilization of the power system — spreading equipment costs over more energy sales, and therefore benefiting both the utility and its customers. Ultimately, SL&P will become winter peaking, but that is preferred, too, because it buys power from Multi-State Omni Utilities, a big, summer-peaking utility downstate, which is quite willing to sell power at a discount during its off-peak season.


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Springfield area. The city government has increased the attractiveness of their town with a package of tax incentives, promises of all the necessary permits and licenses, and a year's worth of meetings and three-martini lunches with NipponAmerica executives — the new factory represents an additional four thousand new jobs for Springfield, and will mean a big boom for local business. At the mayor's urging, Springfield Light and Power has held several meetings with Nippon-America, offering very appealing electric rates for the new factory, rates subsidized by the municipality itself, ever eager to remove any barrier to this economic boon. As near as Susan and her colleagues can determine, Nippon-America hasn't made a decision, but the company seems serious. Rumor has it that they have identified a site, just north of town, that seems particularly suitable for their factory. That site has the required amount of space for the factory and is conveniently close to the rail, highway, and river transportation facilities required to get the materials in and the product out, as well as the various electric, water, and gas utilities necessary to supply a major factory. The factory itself is expected to have a peak load of 19 MVA, and will be served by 69 kV transmission to a substation owned by Nippon-America. But what concerns Susan and her colleagues in System Planning most is the secondary impact of the factory, the type of development covered in Chapter 10, section 3. Four thousand new jobs over the next five years mean a large population boom, for the factory is sure to attract workers from other areas of the nation, bringing about construction of new homes and businesses. Local contractors and businessmen are looking forward to the building boom of new subdivisions, stores, offices, movie theaters, bars, restaurants, and other establishments required to support four thousand families. Police, fire, and school officials are worrying about growth — four thousand new jobs mean several thousand new families to protect and new children to educate, all requiring expanded municipal facilities. This growth, caused directly by the factory, will be in addition to Springfield's normal growth, and will require electric power, much more than the factory's 19 MVA load, all of it the direct responsibility of Springfield Light and Power, to generate and distribute. In addition to the normal distribution expansion plan which would not include the factory (since it is considered less than 50% likely), Susan's management wants a plan showing their system's distribution expansion needs if Nippon-America does build its factory in Springfield. Will additional capacity be needed to serve all the new consumers? If so, what facilities will have to be built, and where and when? How much will they cost? When must construction be started, and when will various expenses be incurred? These are the same

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questions Susan answers in her normal distribution plan, but now she must do it for two scenarios of growth — with and without the factory. The pickup truck factory is not the only causal influence on the location of new load. A major bridge, crossing the Springfield River near the south edge of town, is scheduled for completion in the next two years. Vacant farmland, now twenty minutes from downtown, will soon be only a few minutes away, transformed into highly desirable home sites by the new bridge. This is the planning problem faced by Susan Gannon. Her first step is to produce a base forecast of Springfield's growth that accounts for the influence of the new bridge, then modify it to an alternate scenario that includes the growth influences of the pickup truck factory. Because trending is not ideally suited to handling this type of causal factor analysis, and because Susan believes simulation is inherently more accurate, she decides to use it to develop scenarios of growth for both cases. Having access to nothing more powerful than an electronic spreadsheet on her office PC, she is determined to plunge ahead and do the study manually anyway.

11.3 THE FORECAST Susan decides to use a straightforward manual simulation method, taken directly from this section of the book. Since simple categorizations like residential, commercial, and industrial are insufficient for making distinctions in electric load and growth characteristics to the level of accuracy she needs, she decides to use the land-use classifications given in Table 11.1.

Table 11.1 Land-Use Types for Manual Simulation Class Residential 1 Residential 2 Apartments/townhouses Retail commercial Offices High-rise Industry Warehouses Heavy industry Municipal

Definition homes on large lots, farmhouses single family homes (subdivisions) apartments, duplexes, row houses stores, shopping malls professional buildings tall buildings small shops, fabricating plants, etc. warehouses primary or transmission consumers city hall, schools, police, churches


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These, she knows, are the author's recommended simulation classifications for typical small and medium-sized cities in North America. The simulation approach Susan will take is called the "coloring book" method, because during the course of her study she will keep track of the locations of different classes of land use by marking on a map with various colored pencils. The steps in her forecast are: • •

• •

Forecast future load by class on a per consumer basis. Develop a forecast of how the total amount of each landuse class within her service area will increase from year to year. Determine where the increases in each class will locate, producing a map of future land use. Use her map of future land uses and her data on future per consumer class load to calculate the load increases in various areas of the system, as needed.

As summarized above, the "coloring book" approach may seem simpleminded and prone to error. And although it has several shortcomings when compared to computerized simulation, it can give very good results when applied in a careful, well-prepared manner, particularly on a small planning problem like Springfield's. Its only drawback is that it represents a tremendous amount of very tedious work, as Susan is about to discover. Step 1: Base Year Land Use Map Before forecasting where future consumers will be, Susan first needs to determine where her existing consumers are. The goal of this step is to produce a small area land use map of her system for the base year. It is quite simple, but laborious. Base map. Susan begins with a map of Springfield. This particular map happens to be Springfield Light and Power's transmission system map, 42 by 31 inches in size, with a scale of one inch equals half a mile. Susan is fortunate, for this map has all the qualities needed in a small area base map: 1. The map is to scale. 2. It is clear and easy to read. 3. It shows the major highways, roads, railroads and other landmarks in and around Springfield.

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4. It shows a good deal of the land outside her service area, allowing her to study the areas several miles outside her service territory, should that become important. 5. The study region (her entire service territory) is completely covered by this one map. 6. It shows the locations of all distribution substations in her system. 7. The scale of two inches to the mile means the 1/4 mile wide grid cells she plans to use will be exactly 1/2 inch wide. That is a very nice, convenient size for her analysis, and easy to mark on her map. 8. Replacement copies are and will no doubt continue to be cheap and easy to obtain. 9. It will fit on her desktop. Small area grid. Susan visits a local drafting supplies store and buys a sheet of clear plastic film, 36 inches wide and fifty inches long. In addition, she purchases a roll of tracing paper, thirty-six inches wide by two hundred feet long. She also gets two four-foot metal straight edges, a very good marking pen, and three sets of colored pencils.

Figure 11.3 The master small area map, a clear plastic overlay grid defining the small areas to be used in all subsequent forecast steps.


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Carefully, taking an entire afternoon, Susan inks a 1/2 inch grid across the plastic acetate film with a straight edge and permanent marking pen. This will be her master grid map, and she wants it to be accurate. She numbers the grid columns and rows along the top and bottom, and both sides, as shown in Figure 11.3. Once done, she tapes the map of Springfield to her desktop and lays the clear plastic grid down on top of it, taping it down firmly along the edges so that it, too, is secured to both her system map and the desktop ~ neither the system map nor the grid (or the desktop for that matter) is going to move for a long time to come. With the small area grid overlaid on the map, the exact location of all small areas as used in the forthcoming study is defined. The one-half inch wide small areas at a scale of one inch equals half a mile means her small areas are 40 acres each (1/4 mile wide). Susan marks the location of several key landmarks on the plastic overlay, so that if it somehow comes loose, she can fit it back again, exactly as before. Susan now cuts off fifty inches from the roll of tracing paper and lays it atop the plastic overlay, overlapping it on all sides and firmly taping each of its corners down to the desktop. She traces several key landmarks and all four corners of the grid onto the paper, so that if it is removed she can reposition it exactly if need be. Base land use coding. Susan now has the base map, the clear plastic overlay, and the tracing paper laid out on her desk. She intends to color the tracing paper overlay with the colored pencils, using a different color to indicate each of the land-use classes, completely filling in each small area according to its current types of land use. She will use the colors shown in Table 11.2 to indicate her land-use classes: Table 11.2 Colors Used to Indicate Land Use Class Residential 1 Residential 2 Apartments/twnhses Retail commercial Offices High-rise Light & medium industry Warehouses Municipal Heavy industry Vacant-restricted Water

Color light green green dark green yellow orange

red gray brown orange black purple blue

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The alert reader will notice that Susan has added two classes of "land use" that were not in Table 11.1 to the classes she will color: vacant-restricted and water. Vacant-restricted land includes parks, wildlife preserves, military firing ranges, ball fields, airport runways, and other land on which load will never develop. Both research and experience have proved that the vacant restricted class is the single most important class for distribution planning ~ this is land where load can never develop, and thus of great impact to the distribution plan (Tram et al., 1983). It is also the first class Susan colors onto her map -- she wants to have the vacant land that never can develop load identified and marked first. She then intends to color in other land uses until she has a map of the present land use in Springfield. To do this step well, Susan must first establish some definitions that she will use throughout her work, definitions of exactly what she will define as the land that belongs to a shopping center, an individual home, or a factory. Among her rules are: In residential areas, the entire lot, the portion of any streets alongside the residential lot (from the property line to the center of the street) and any alleyway bordering the property will be considered part of the residential land use. Similarly, grassy areas around apartments and office buildings, plazas, sidewalks, and courtyards are part of the area of the adjoining land use. Parking lots associated with stores, apartments, offices, and other buildings are included in their building's land use. A shopping mall that covers five acres, surrounded by a twenty-seven acre parking lot, will be defined as thirty-two acres of retail commercial land use. Outdoor storage areas, such as those used to inventory concrete pipe outside a concrete conduit factory, or wrecked automobiles at a wrecking yard, will be considered vacant-restricted land with no load, and identified as such. Any minor load in those areas (lighting, etc.) will be assumed to be part of the load in the factory or wrecking yard office, respectively. Susan could have defined land use differently, for example classifying all parking lots, etc., as "parking lots" and coloring them a different color. The important point is that she is consistent with whatever rules she applies. Using the rules listed above (rather than separate "parking lot" colors, etc.) makes her job a bit easier, and what matters is that she is absolutely consistent.

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Diagram of land uses

As colored

Residential 2 Residential 1

Vacant (nothing)

Figure 11.4 Map showing the land use in a forty-acre small area (a square area 1/4 mile to a side), and the colors Susan puts on her map.

For each small area on her map, Susan intends to use the appropriate colors in the correct amounts, indicating the present land use in that small area. For example, she would color the cell shown in Figure 11.4 as indicated. Where will she get the information on the land use in each small area? She plans to use a combination of sources: 1. Her own knowledge. She's grown up in Springfield, and worked for ten years as a distribution planner for Springfield Light and Power. She feels that she knows the town just about as well as anyone could. 2. A set of aerial photographs covering the Springfield Light and Power service area. She borrowed these from her company's Land and Right of Way Department, which keeps a photobook containing 30 by 24 inch black and white aerial photos, scale of one inch equals one thousand feet, for studies of potential easement and property purchases. It is quite simple for her to find landmarks on her system map — streets, creeks, and major roads, etc. ~ to help identify the location of any particular small area on these aerial photos. 3. Zoning and land-use maps produced by the City of Springfield's Municipal Planning Department, showing the land uses and zoning for

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various blocks within the city. (Zoning data are the least useful of her three sources, because as in most American cities, so many variances are granted in Springfield that it is unreliable as an indicator of future growth. In addition, Susan has seen the zoning in her city change at the whim of City Council, which generally responds to the wishes of developers and those pushing for economic development). Originally, Susan had planned to use only her own knowledge of Springfield and its development when coloring the land use onto her map. She felt confident that she would know the city accurately, since Springfield was fairly small, she had lived there a number of years and she got around the city a good deal in the course of her work. However, even a cursory look at the aerial photos convinces her that her "expert knowledge" of the Springfield area is incomplete. She decides that she will need to depend on the photos and zoning data more than she had expected. She spends several hours studying the photographs, concentrating on areas of the city that she knows well. This preliminary work allows her to learn what various land uses look like in the photos. She learns a number of "tricks." Among them: Parking lots around a building are the biggest clue to its land use. Retail shopping will always have a big parking lot between the building and the street. Parking for offices tends to be smaller in proportion to the building, and located around or even behind the building. Factories generally have a relatively tiny parking lot located behind or to the side of the building. Warehouses are normally along railroad tracks, which are discernible because they appear as very straight, thin lines (much narrower than roads), and always have wide, circular arcs in any turn. Cemeteries are discernible from parks and undeveloped areas because of the network of very narrow roads running throughout. The height of a building can be discerned from the shadows it casts to the side, which she can see in the photos. She learns she must be careful, however. The photobook contains 165 separate photos, and they were taken at different times of day. However, by comparing shadows within any one photo, using something she knows well as a baseline, she can identify building height.

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A Detailed Look at the Simulation Method service Territory Boundary

:: Lo-densRes. iii! Residential HI! Apartments m Retail 19 Offices •

Hi-rise

= Lgt, Ind. = Med.lnd. •i Hvylnd.

SPRINGFIELD LIGHT & POWER CO. Service Territory and Distribution Substations

Figure 11.5 Susan's base year land-use map of the Springfield System. In addition to the land uses shown, Susan colored restricted (non-buildable) land.

The key to Susan's success, however, is that she depends on all the sources of data at her disposal. She first locates each small area in the aerial photos, studies the information in the photo, checks the zoning data, and then colors the cell on her map to correspond to the land uses she sees. By moving across the map in columns and rows, and organizing her effort carefully, she minimizes the time it takes to do this work. The entire map takes her only four and one half days to complete (Figure 11.5). Land use inventory. Once her map in finished, Susan carefully estimates the total number of small areas of each land-use class that she has colored to obtain a "land use inventory" of the Springfield Light and Power service territory. She counts partial small areas, too, trying to estimate accurately the breakdown of small areas with multiple land uses, such as that shown in Figure 11.4. She then multiplies her figures by 40 (there are forty acres in each small area) to convert the counts to acres, obtaining Table 11.3, Susan's base year class inventory.

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Chapter 11 Table 11.3 Base Year Land-Use Inventory Land-Use Class Residential 1 Residential 2 Apartments/twnhses Retail commercial Offices High-rise Industry Warehouses Municipal Transmission level custs.

Areas

389.1 192.4 41.28 27.78 10.05 3.65 14.70

7.9 1.275 2.65

Acres 15564 7695 1651 1111

402 146 588 316 51 106

Step 2: Consumer Class Load Projections The purpose of this second step is to determine a per acre peak kVA for each land-use class, and make a projection of how these values will change over the twenty-five year study period. Susan will do so by first developing a set of per consumer peak loads for her base year, then converting them to per acre values, and finally projecting those values into the future. This is not a difficult process, but it takes care and attention to detail. Step 2a: Per consumer peak load by class. Like almost every utility, Springfield Light and Power meters only energy sales, not peak, for its residential and smaller commercial and industrial consumers. For these smaller consumers, Susan must estimate the load at time of peak. For the larger commercial and industrial users she has reliable metered demand data which she can use to make accurate assessments of their peak load. For the residential and small commercial/industrial peak estimation, she uses four sources of information: 1. Her judgment. Susan's been a distribution planner for a decade and has developed a fairly good sense of the loads and load behavior of her system. 2. Load research data from the Rate and Consumer Studies Department. A selected sample of 62 homes has been metered for demand behavior, as has a set of 37 representative small


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businesses. Although that work was done carefully, Susan knows that such data are often far from accurate for the application she has in mind (see Chapters 3 and 4). 3. Transformer load management (TLM) data. Springfield's TLM program uses an empirical formula to convert consumer billing kWh to peak load kVA estimates, so that it can predict if service transformers are overloaded and when they should be economically switched to a larger size. Susan can use this formula to calculate per consumer peak load from average billing statistics. 4. Feeder load data. Susan obtains readings of last year's summer and winter peak loads for several key feeders she has identified. These are feeders which serve mostly consumers of only one class, where she knows the type and number of consumers, as shown in Table 11.4. Susan plans to use this source of information, but she suspects that the TLM formula's estimates will be low, for several reasons. First, she understands that the TLM program is based on purely empirical "it seems to work well" formula, and she suspects that her TLM department has set their formula to the lowest estimator that will work in order to cut down on false alarms — situations where the TLM predicts a transformer is overloaded, but subsequent investigation shows that it is not. Second, she knows that "peak" to a transformer or a TLM program may mean the maximum average load over a two, three, or even four hour period » long enough to severely overheat the windings. She wants the peak hourly value, which might be higher than the peak over a longer period.

Table 11.4 Load Readings and Consumer Content for Selected Feeders Feeder Name Ridgeway 2 Eastside 4, branch 1 Northwest 2, branch 4 Eastside 3, branch 2 Riverside 4, branch 3

Number of Consumers by Class 8 1 7 class 2 (normal resid.) 240 class 3 (apt) 14 class 2, 32 class 3, 91 c!4 340 class 2, 23 class 5 56 classes 7 and 8

Summer PeakkVA 3,529 717.6 1,087 2,314 687.9

Winter Peak kVA 2,328 597.3 912.7 1,747 642.2

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Table 11.5 Per Consumer Summer Peak Load Data Values by Source in kVA/Consumer Diversified Contribution to System Peak

Class 1 2 3 4 5 6 7 8 9 10

Confidence:

Fdr data .5

Load Rsrch .15

TLM .15

4.41 4.32 2.99 10.23 36.74 15.24 9.33 78.20 -

5.21 4.85 3.17 10.47 37.25 15.60 9.20 83.20 -

4.17 3.99 2.78 8.52 36.80 . 12.93 8.95 75.60 -

Mtrd Dmd 1.0

320.00

5,120.00

Wtd Ave.

4.55 4.37 2.98 9.86 36.88 320.00 14.70 9.20 78.80 5,120.00

Of these four data sources, Susan has the most confidence in the feeder data, because the feeder load readings were metered accurately and she has precise counts of the consumers downstream from those points. She begins with that data, calculating each class's per consumer peak. For example, in Table 11.4, Ridgeway 2 feeder, which is purely residential class 2, yields values of 4.32 and 2.85 kVA respectively for summer and winter peak loads per consumer. Those values allow her to infer from the feeder loading on Eastside 3 that class 5 has summer and winter loadings of 36.74 kVA/consumer and 33.83 kVA/consumer. She writes out her estimates from the feeder data, the load research values, and the TLM program, as shown in Table 11.5. Weighting each source proportional to her confidence in it, she forms an average of the values weighted by her confidence in each source, obtaining the values in the last column of Table 11.5. In a similar manner she produces estimates for the winter loadings. She does not have complete confidence in any of these figures. Forecasting, particularly distribution forecasting, is an inexact science, although Susan is trying to be as exact as she can. Step 2b: Converting to per acre loads. Susan knows that later in her forecast she will be working on a geographic basis when counting consumers, land area, and the other factors involved, as she did with her base year map in Step 1. She wants to use acres as much as possible in her analysis, and therefore must translate her consumer loads into kVA/acre from kVA/consumer. Making this change will not add any error to her forecast, because somewhere in any distribution forecast the planner must convert land area from acres to number of consumers and/or vice versa. Every distribution load forecast contains an


341

explicit or implicit consumers/acre density factor. Susan has just decided that she will do this explicitly, at the beginning of her simulation. There are a number of sound reasons for her decision. First, this is the traditional way distribution engineers and planners think about load — so many megawatts per square mile, or so many kilowatts per acre. Her intuition and judgment, based on a decade of experience, will apply more directly if she puts load data into this framework. More important, she is going to be counting land use on an acre basis while using her land use maps. It will be easier to convert her data to load, and check her figures quickly if she works with load on a per acre basis (remember, she will have a lot of figuring to do, all of it manual). In addition, much of the data she has or will obtain from the City of Springfield Zoning Department, as well as various state agencies are on an acre basis. Translating Between Consumer and Acre Bases Susan already knows the total number of acres of each land-use class (these are the totals on her map). In order to translate her data on a per consumer basis (Table 11.5) to a per acre basis, she needs to know the total number of consumers in each of her land-use classes. The Rate and Consumer Studies Department's Forecast of Electric Sales and Revenues, a thick volume published annually, is her starting place. It lists consumer counts by rate class. She has to break some of the rate classes into several subclasses so that they correspond with her classifications. For example, the single rate class "residential single family home" contains both larger homes on large lots (Residential 1) and medium-sized houses (Residential 2). To some extent Susan

Table 11.6 Consumers per Acre by Class Class Residential 1 Residential 2 Apartments/twnhses Retail commercial Offices High-rise Industry Warehouses Municipal Hvy industry

Consumers + 10584 24524 10072 2444 684 88 824 316 41 5

Acres = Custs./acre 15564 7695 1651 1111 402 146 588 316 51 106

.68 3.2 6.1 2.2 1.7 .6 1.4 1 .8 .047

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has to rely on her own knowledge and that of her colleagues to do this, but she obtains useful statistics from City building permit and zoning data, discussions with builders and developers, and load research statistics. With a little thought and work, Susan obtains the consumer counts shown in Table 11.6, column 1. She uses these numbers, and the land-use acre inventory from the last column in Table 11.3, to determine the consumers/acre for each class ~ the final column in Table 11.6. She then uses those consumer/acre ratios to convert her summer per consumer kVA values (the final column in Table 11.5) to a per acre basis, obtaining the values shown in Table 11.7 (only summer values are shown). She performs an identical analysis for winter peak. With these numbers she can convert consumer-based figures to acre-based, and vice versa. Step 2c: Projecting per consumer peak data into the future. To complete this step, Susan needs to forecast these summer and winter peak kVA/acre figures into the future for the years she plans to study. Once again, she turns to the Rate and Consumer Studies Department's Forecast of Electric Sales and Revenues. The Rate and Consumer Studies department had used a consumer-class, end-use appliance subcategory load model (see Chapter 4) to project peak contribution and energy usage on a per consumer basis by rate class over the next twenty years. From the tables in their forecast report, Susan determines the expected percentage growth in per consumer peak by year, for each rate class. She applies these percentages to project her kVA/acre figures into the future, knowing as she does that this step is a big potential source of error. Rate and Consumer Studies' peak projections focus on contribution to system peak for each class (load at time of peak not necessarily peak daily load). Of course Susan does have an interest in peak contribution and system peak, but her abiding interest is in peak load by class, because many areas of her system (and

Table 11.7 Per Acre Summer Peak Load Density Conversion Class Residential 1 Residential 2 Aparrments/rwnhses Retail commercial Offices High-rise Industry Warehouses Municipal Hvy industry

kVA/cust x Custs./acre 4.55 4.37 2.98 9.86 36.88 320.0 14.7 9.2 78.8 5120.0

0.68 3.2 6.1 2.2 1.7 0.6 1.4 1.0 0.8 0.047

=

kV A/acre 3.09 13.98 18.18 21.69 62.69 192.00 20.58 9.20 63.04 240.64


343

the equipment that will serve them) will see a local peak not at the time of system-coincident peak but at the time of local class peak. What she really needs is peak day hourly load curve shapes for each class. Those would give her both each class's load at time of system peak and peak load, if different. In fact, such data are available to Susan, but she prudently decides not to try to include it in her manual analysis. Rate and Consumer Studies used peak day load curve shape data in their end-use model, but didn't put that level of detail in their report. The raw data are available if Susan wants it. But while she has the resources and time to analyze and work with load curves on a consumer class basis now, she recognizes that without computerization she will not be able to work with composite class curves on a small area by small area basis later in her study. She takes a look at the hourly load curve data to give her a feel for the curve shapes, but decides to stick with her single peak-per-class figures. Thus, she has the figures shown in Tables 11.8 and 8.9. Later in her simulation process, she will have to verify and perhaps change these numbers, but for now they are good estimates. At this point that is all she needs ~ estimates that are starting points for her study. She will refine them later. Before continuing, Susan studies these numbers to make certain that they seem reasonable and that she understands what these future changes in per consumer (per acre) load mean to her plan. What intrigues her most is the relative growth of the summer and winter loads. She has known for several years that Springfield's load was forecast to become winter peaking, but these figures illustrate just what that means to her system. Specifically, she notes that: 1. Many commercial and industrial classes will remain summer peaking throughout most of the twenty-five-year study period, only switching to winter peaking in the last few years. 2. The residential class will become winter peaking in less than four years, and its winter peak will continue to grow much faster than summer's. Susan realizes that this means substations and feeders serving predominantly residential areas of the Springfield Light and Power system will become winter peaking in only two to three years, even though the system, as a whole, may not be winter peaking until another five years later. Residential parts of her system may need to be reinforced for this higher and faster growing winter peak much sooner than had been expected. Reasoning that if she had been unaware of this problem, others in the Engineering Division probably were also, she sends a short memo to her boss, summarizing the problem and suggesting that it be routed to the other department heads.

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Table 11.8 Diversified Summer Peak Load Densities by Class (kVA/acre) Consumer Class Residential 1 Residential 2 Apartments/Twn h ses Retail commercial Offices High-rise Industry Warehouses Municipal Heavy industry

Now

3.09 13.98 18.18 21.69 62.69 192.00 20.58 9.20 63.04 240.64

2

4

Year 8

12

20

25*

2.80 2.75 2.73 3.03 2.87 2.97 13.75 13.52 12.99 12.50 12.02 11.80 17.83 17.59 17.40 17.25 17.10 17.00 20.51 20.00 19.86 19.75 19.65 19.55 61.40 60.50 59.50 59.00 58.80 58.65 189.90 187.20 185.00 183.00 181.20 179.80 20.00 19.50 19.00 18.75 18.69 18.66 9.32 9.21 9.24 9.28 9.30 9.22 61.80 59.35 57.50 56.73 56.73 56.73 240.64 239.00 237.00 235.00 233.00 232.00

* Extrapolated from previous time period growth rates.

Table 11.9 Diversified Winter Peak Load Densities by Class (kVA/acre) Consumer Class Residential 1 Residential 2 Apartments/Twnhses Retail commercial Offices High-rise Industry Warehouses Municipal Heavy industry

Now

2.85 11.86 15.32 18.64 57.39 161.00 19.62 11.30 51.60 237.40

2

4

Year 8

12

20

25*

3.54 3.54 3.54 2.97 3.10 3.33 12.20 12.54 13.15 13.60 13.80 13.80 15.61 15.95 16.42 16.60 16.70 16.75 18.82 20.04 21.00 22.00 23.00 23.50 57.41 57.40 57.30 57.00 56.80 56.80 159.00 160.00 162.0 165.00 170.00 173.00 19.60 19.55 19.00 18.75 18.69 18.64 9.70 9.58 11.00 10.50 10.00 9.90 50.70 49.20 47.80 46.44 46.44 46.44 237.40 237.00 236.00 234.00 234.0 234.00

* Extrapolated from previous time period growth rates.

345

A Detailed Look at the Simulation Method Step 3: Global Forecast of Consumer Growth

The purpose of this third step is to produce a count (in acres) for each land-use class throughout the twenty-five-year study period. For the base scenario, this is easy to obtain from Rate and Consumer Studies' report, which lists consumer counts by rate class by year for the service territory. Those are not on an acre basis, but they give Susan the growth rates she needs to calculate acres. Susan needs similar numbers for the factory scenario. Fortunately, Rate and Consumer Studies had been asked to provide management with a projection of the NipponAmerica factory's impact on revenues and has already completed its study using a method similar to that described in Chapter 10, section 3.

Table 11.10 Projected Number of Consumers by Land-Use class Rate Class

Land-use class

Now

8

12

20

25*

rural 10584 11395 12200 14001 15821 19934 22885 normal 24524 26772 28820 32854 37125 46777 53702 multi-family 10072 10788 11532 13146 14854 18716 21487 Gen. serv. retail 2444 2617 2798 3189 3604 4541 5213 802 1504 offices 684 740 915 1040 1310 Lrg. gn. srv. big, high-rise 88 105 141 204 97 123 178 Industrial small shops, etc. 824 951 1094 1789 890 1236 1558 361 412 674 warehouses 31 6 338 466 587 Municipal city, schools, etc. 41 44 47 54 61 77 88 Spec, contr. transmission custs 5 5.72 6.52 7.4 10.6 5.35 9..3 *Extrapolated from year 1 5 to 20 year trends. Residential

Table 11.11 Projected Consumer Counts Added by the Pickup Truck Factory Scenario Land-Use Class Residential

2

rural 8 normal 544 mult-fam. 445 Commercial retail 46 offices 14 High-rise 2 Industry small 17 warehouses 6 Municipal 1

Years After Construction Begins 4 8 12 20 33 2272 1860 207 58 7 70 27 4

59 4064 3337 369 104 13 125 48 6

59 4064 3337 369 104 13 125 48 6

59 4064 3337 369 104 13 125 48 6

25 59 4064 3337 369 104 13 125 48 6

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Convert consumer counts to acres. Using her statistics on consumer density (Table 11.7), Susan simply converts the consumer counts in the R&CS tables above to acres by dividing each consumer count by its appropriate consumers/acre value, obtaining Tables 11.12 A and B, below. (Note that Table 11.12 B is identical to Table 10.3 in Chapter 10's example.) These are Susan's "global totals," the consumer counts that will "drive" her forecast of spatial load growth. Assuming the R&CS Department's forecast is accurate, and her rate class disaggregations and load density analysis were correct, the SL&P service territory will have the future land use inventories given by the columns shown in Table 11.12. Her job through the rest of the simulation forecast process is to assign that global growth to specific small areas. Table 11.12 A. Projected Consumer Class Acres by Year ~ Base Scenario Land-Use class

12

Now

Residential 1 15564 Residential 2 7695 Apartments/Twnhses 1651 Retail commercial 1111 Offices 402 High rise 146 Industry 588 Warehouses 316 Municipal 51 Transmission level cons. 106

16757 8366 1768 1190 435 162 635 338 55 114

20

17941 20589 23266 29314 9006 10266 11601 14618 1891 2155 2435 3068 1272 1638 2064 1450 611 770 471 538 175 205 235 296 1112 679 781 882 587 361 412 466 76 96 59 68 122 139 157 198

25 33654 16782 3522 2369 884 340 1278 674 110 225

Table 11.12 B. Projected Additional Consumer Class Acres by Year ~ Factory Scenario Land-Use class

Now

Residential 1 Residential 2 Apartments/twnhses Retail commercial Offices High rise Industry Warehouses Municipal Addtnl Transmission level cons.

2

4

8

12 170 73 22 8 3 12 6 1 2

49 710 305 94 34 12 50 27 5 4

12

20

25

87 1,270 547 168 61 22 89 48 8 7

87 1,270 2547 168 61 22 89 48 8 7

87 1,270 547 168 61 22 89 48 8 7


347

Table 11.13 Comparison of System Peak Load* Projections (MVA)

Forecast Year 0 2 4 8 12 20 25

From Susan's Numbers Summer Winter 315 328 343 379 425 525 598

270 299 328 388 452 575 662

From R&CS's Forecast Summer Winter 315 328 344 380 427 528

270 298 325 388 452 574

System peak load with transmission losses removed.

Check by calculating future system loads. Before going on to the next step, Susan combines her peak load per acre data (Table 11.8) with the projected land-use totals (Table 11.12), in order to calculate the system peak load for her base year and for each of her future study years. For each future year, she multiplies the projected number of acres for each land-use class by that class's kVA/acre for that year, to obtain the total load for that class (for example, in year 2 for Class 1, 3.03 kVA/acre times 16757 acres = 50.77 MVA). She then adds all the land use's loads together by year to get the total system loads projected by her figures, as shown in Table 11.13. As a check, she compares these to Rate and Consumer Studies' projections of annual system peak. She is quite pleased with her results, which are within 1% in all cases. These are good results, but not extraordinary. It is advisable not to go on past this step if there is any significant mismatch. This match means that she has the overall totals adjusted correctly — in the rest of her simulation procedure, she will be assigning the correct total amounts of consumer load ("correct" in the sense that it is totally consistent with Rate and Consumer Studies' figures) to the small areas on her map.

Step 4: Base Year Calibration A laborious step now faces Susan — spatial calibration of her base year loads. The data shown in the top line of Table 11.13 indicate that her overall totals match the actual recorded system loads — her combination of land-use totals and

348

Chapter 11

peak KVA/acre sums to the same figures as the most recent actual system peak. But how does she know that her spatial allocation of load (her geographic distribution of land use) is correct? She must verify the accuracy of the spatial or small area base year data by making certain that they correctly explain her existing substation loads. To do so, Susan must convert the land-use colors in each small area of her colored land use map to load per small area using the per acre load density figures developed in Step 2. Then she must sum up the appropriate small areas in each substation area to get a "forecast" base year peak load for each substation, and compare those sums to actual recorded substation peak loads. Given that Susan has only a calculator, pencil, and paper, this will be a laborious step. Determined to complete it as quickly and efficiently as possible, she lays a second sheet of tracing paper atop her colored land use map. She has, from bottom to top: the system map, her plastic overlay grid, her colored land use map, and this new, blank sheet. She can see through this top sheet of tracing paper to the colors on the map below and the small area grid beneath that. For each small area, she examines the colors covering that cell, and converts them to load in the manner shown below, writing the load value in pencil on the top layer of paper, as shown below:

50S y e l l o w < r e t a i l > x 2 0 acres 3 21.69 kUfl/acre = 433.8 40$ green c e [-1.0, 1.0] fj(x,y) is the score for factor j in small area x,y Pj>c is class c's coefficient for factor j and j indexes J and c indexes C. Pc (x,y) is therefore somewhere between -12.0 and +12.0

Computation of these linear combinations for all small areas, for all C classes, resulted in C pattern match score maps, one for each land-use class, which were stored in a multi-map stack for later use. Table 13.8 lists the pj c values for three land-use classes, giving an example of how the weighting for a particular factor may vary from one class to the other. Although based upon the same twelve factors, the spatial pattern maps and their evaluations developed for

Table 13.8 Pattern Match Coefficients for Three Consumer Classes Factor

1 2 3 4 5 6 1 8 9 10 11 12

Description Railroad proximity factor Near highway factor Highway proximity factor Industrial proximity factor Hi-rise proximity factor Water proximity factor Trees - local factor Major intersection proximity Residential surround within 1/2 mile Residential within 3 miles Commercial within 1 mile Industrial within 2 miles

Resid.

Retail

Hvylnd.

-1.0 -.66 1.0 -1.0 -.66 .50 .35 -1.0 1.0 -.33 -.16 -1.0

-.50 1.0 1.0 -.50 .40 0 -.16 1.0 -.2 1.0 .50 -.50

1.0 -.10 .50 1.0 -1.0 1.0 -.20 -.33 -.50 -.16 0 1.0

Analytical Building Blocks for Spatial Simulation

417

Figure 13.6 Maps of preference values computed at a 160-acre resolution for two classes. Major highways (lines) are shown for reference. Shading indicates magnitude of positive values. (Negative values are not displayed.) Top diagram shows industrial preference. Path of trunk rail corridors, a major factor for preferable industrial location, is quite discernable. Bottom diagram shows preference for the retail commercial class.

418

Chapter 13

each of the classes had a far different spatial character and distribution because of the different weighting coefficients. Figure 13.6 shows pattern match maps produced by applying the retail commercial and residential patterns templates in Table 13.8. Shading indicates degree of pattern match. The reader should note that pattern evaluation is only one part of the spatial analysis of consumer growth. Its purpose is not to determine location of change, but merely to identify certain aspects of location. Pattern matches determined by linear combinations as shown in eq. 13.4 provide only a partial answer, but a critical part. When combined with other spatial analysis features in a comprehensive program, they lead to a useful, accurate forecast.

REFERENCES AND BIBLIOGRAPHY

R. J. Bennett, Spatial Time Series Analysis, London, Pion, 1979 C. L. Brooks and J. E. D. Northcote-Green, "A Stochastic-Preference Technique for Allocation of Consumer Growth Using Small Area Modeling," in Proceedings of the American Power Conference, Chicago, Univ. of Illinois, 1978. J. L. Carrington, "A Tri-level Hierarchical Simulation Program for Geographic and Area Utility Forecasting," in Proceedings of the African Electric Congress, Rabot, April 1988. M. V. Engel et al, editors, Tutorial on Distribution Planning, New York, Institute of Electrical and Electronics Engineers, 1992. J. Gregg et al, "Spatial Load Forecasting for System Planning," in Proceedings of the American Power Conference, Chicago, Univ. of Illinois, 1978. A. Lazzari, "Computer Speeds Accurate Load Forecast at APS," Electric Light and Power, Feb. 1965, pp. 31-40. I. S. Lowry, A Model of Metropolis, Santa Monica, The Rand Corp., 1964. R. W. Powell, "Advances in Distribution Planning Techniques," in Proceedings of the Congress on Electric Power Systems International, Bangkok, 1983. C. Ramasamy, "Simulation of Distribution Area Power Demand for the Large Metropolitan Area Including Bombay," in Proceedings of the African Electric Congress, Rabot, April 1988.

Building Blocks for Spatial Simulation

419

Research into Load Forecasting and Distribution Planning, EL-1198, Palo Alto, Electric Power Research Institute, 1979. B. M. Sander, "Forecasting Residential Energy Demand: A Key to Distribution Planning," IEEE PES Summer Meeting, 1977, IEEE Paper A77642-2. W. G. Scott, "Computer Model Offers More Improved Load Forecasting," Energy International, Sept. 1974, p. 18. Urban Distribution Load Forecasting, final report on project 070D186, Canadian Electric Association, 1982. H. L. Willis and J. Aanstoos, "Some Unique Signal Processing Applications in Power Systems Analysis," IEEE Transactions on Acoustics, Speech, and Signal Processing, Dec. 1979, p. 685. H. L. Willis and J. Gregg, "Computerized Spatial Load Forecasting," Transmission and Distribution, p. 48, May 1979. H. L. Willis and T. W. Parks, "Fast Algorithms for Small Area Load Forecasting," IEEE Transactions on Power Apparatus and Systems, October, 1983, p. 342. H. L. Willis and T. D. Vismor, "Spatial Urban and Land-Use Analysis of the Ancient Cities of the Indus Valley", in Proceedings of the Fifteenth Annual Pittsburgh Modeling and Simulation Conference, University of Pittsburgh, 1984. H. L. Willis, M. V. Engel, and M. J. Buri, "Spatial Load Forecasting," IEEE Computer Applications in Power, April, 1995. H. L. Willis, J. Gregg, and Y. Chambers, "Small Area Electric Load Forecasting by Dual Spatial Frequency Modeling," in IEEE Proceedings of the Joint Automatic Control Conference, San Francisco, 1977.

14 Advanced Elements of Computerized Simulation 14.1 INTRODUCTION This chapter is the last of three to look at computerized simulation methodology. Here, the structure and flow of computation for several types of simulation is presented and the use of the various concepts and building blocks discussed in Chapter 12 and 13 is examined in detail. The detailed, overall program and numerical computation structure of a generic simulation program for spatial electric load forecasting is presented. Spatial frequency domain methods and various other advanced techniques to increase computation speed, improve accuracy, automate the forecast calibration process, or otherwise improve the program's usefulness are covered next. A discussion of these and their application concludes the chapter. 14.2 SIMULATION PROGRAM STRUCTURE AND FUNCTION Figure 14.1 shows the overview diagram of simulation program structure from Chapter 12, with the typical locations of various building blocks and elements of program operation covered in Chapters 12 and 13 shown. What might be termed the basic, and certainly the most straightforward, of widely used programming approaches for this method is shown in Figure 14.2. A majority of simulation programs uses this approach or a variation to it. It has been applied in both grid

421

Chapter 14

422

Year N Spatial Data

Year N Load dive Data

onsumer Classes Consumer Count Input-Output Inventory Polycentric Pole Model Pattern Recognizer

YearN+P Spatial

Preference Computation Match to Corp. totals Consumer count -> Load Year Ntp Load Map

Figure 14.1 Simulation programs generally include both spatial and temporal "sides" which work independently, their function coordinated by the use of a common set of classes and a shell program that merges their results into the final spatial forecast.

or polygon spatial basis, and with either a mono-class or multi-map framework. The program goes through one iteration of the logic shown in Figure 14.2 for each study period (year to be forecast) that iteration taking the spatial data description of the consumer amounts and locations at the beginning of the period and transforming it into a description of consumer amounts and locations at the end of the period. The new spatial consumer data is then combined with load curves computed for the end of the period, to form forecast load maps. In this way the program gradually extends the forecast into the future — 1996 to 1997, to 1998, to 2000, to 2002, and so forth. Table 14.1 summarizes typical data requirements. At the start of each iteration, a T matrix (Table 13.3 and equation 13.1) computation is used to determine the expected total change in the amount of each consumer class (alternately the amount of consumer change may simply be input by the user).

Advanced Elements of Computerized Simulation

423

Table 14.1 Input Required by the Basic Simulation Method Shell program • set up data defining the number and indexing range of the small areas, the units to be used • number and names of the consumer classes • future years to be forecast (study periods) Temporal • • •

model Load curves, End-use structure (if using an end-use model) K, q, and p for each class (if using approach like that in Chapter 4, section 5).

Spatial model • spatial land-use data for the base year • geographic data such as highways, railroads, canals, airports, wetlands • global land-use totals for future years, or basic industry totals and a "Lowry" T matrix • polycentric activity model data: locations of the center, height, and radii for all urban poles • definition data for the proximity and surround factor maps, which might include what factors are to be computed (but often this is hard-wired) and the profiles to be used • pattern template coefficients, for each consumer-class coefficients for each factor (similar to Table 13.8) and a coefficient for the polycentric activity center urban pole map

Using a set of urban pole locations, height and radii input by the user, the program computes an urban pole map ~ a polycentric activity center model of the large area effects in the region, similar to that shown Figure 13.3. The program next determines what small areas are available for change. At a minimum, this involves checking the current spatial land-use data to determine how much of each small area is classified as vacant unrestricted land. Additionally, some programs perform an evaluation of all currently developed land to identify where it appears likely that the existing land-use types might be razed and re-built as some other type. Regardless, this step concludes with a computed map of "available space." In a mono-class framework this is a map of small areas that are available to change to a new consumer class type. In a multimap framework this is a small area map that contains for each small area a count of the acres (or hectares, or square kilometers as the case may be) available for

Chapter 14

424

INPUT DATA

SPATIAL MODULE

Global changes or basic industry & T

Accept or compute global change totals by class

Base year customer load maps

Identify or compute available growth space on a small area basis(form map)

TEMPORAL MODULE

Available space map Compute new customer class load curves for this study period

Base year load curves

(

cente Activity center^. _ r~-^^ Form Polycentnc Activity Center "Urban Pole" influence map ocations& datay data 1-u-—-^

/'Factor definitions. l v data&profilesJ' actor & urban pole map coefficients, by class

r

~~~~~--»r 1—L

Compute factor maps

Compute preference map for each class

•oPreference maps

Allocate decline of each class by "removing" land use in small areas with lowest preferences

>=>

Allocate growth of this class to available small areas with highest preferences

Updated customer class maps Merge load curves and customer maps into load map(s) for end of period Load map(s) ;for end of study period

Figure 14.2 Overall flow of the basic simulation method's logic in computing one iteration of the forecast.


425

new development and change. Regardless of pattern match or preference values, consideration for growth is applied only to small areas in this map. Next, the program computes a set of proximity and surround factor maps (usually about a dozen similar to those in Table 13.7) and forms a different linear combination of them into a pattern match map for each class (similar to Figure 13.6), to which it adds the polycentric activity center map, using a different weighting factor for each class, to form the preference map for each class. Change is then allocated based on the computed preference maps for each class. If decline (negative growth) is a concern, forecast of that is done first by removing the forecast amount of land-use (consumer count) decline from the land-use map. For each class, all small areas of that land use are analyzed. Those with the lowest preference map scores are deleted (changed to vacant available) by altering their land-use designation to vacant unrestricted. In a mono-class map framework this is done by changing the small area's class designator. In a multimap framework this is done by setting the small area's count in this particular land-use map to zero, and adding that same value to the vacant restricted map ~ e.g., "subtracting" 10 acres of residential from the residential map and adding it to the value stored for the small area in the vacant restricted map. Decline is computed first so that the "deleted areas" are available for consideration for new development of other classes. ' Finally, growth is allocated. For each land-use class in turn, the amount of global increase forecast to occur is allocated to selected vacant available small areas. Using the preference map for this particular class, the highest scoring vacant available small areas, up to the amount of growth required, are changed to the land-use class. The order of consideration of classes in this step is somewhat important. Classes are done in the order of greatest typical land value (high rise is done first, light industrial last) - land uses that can pay more for land get "first choice." In this way all of the land-use totals are allocated, and the consumer class maps have been transformed. The total amount of land use for each class now equals the amount forecast by the global model. These land-use maps are then

A few simulation programs use an interesting variation in this step. Instead of designating such land as "vacant unrestricted," they use an "abandoned" class designator, a flag set into a separate map in the map stack, which indicates small areas where buildings and houses have been abandoned but not torn down. The program is written so that it computes no load associated with such areas, but the land area is not identified as "vacant." From a purely electric load forecasting standpoint this does little to improve accuracy, at least in a simulation with this level of detail. Dealing with the flag map in this and other steps adds considerably to the program computation time.

426

Chapter 14

combined with the temporal load model's load curves to form a small area map of load. C Small area load (x,y,h) =

(Area(x,y,c)*l(c,h))

(14.1)

c=l where: Area (x,y,c) is the amount of land-use class c in small area x,y L (c,h) is the per area load for class c at hour (or time period) h x = [1,X] and x = [1, Y] index the small area in an X by Y grid or the polygon framework c = [ 1 , C] indexes the land-use class h = hour (or time period) for the load curve Often, several maps are computed, loads for different times of day (system peak, residential peak, commercial peak time) and a final map equal to the maximum of all loads on a small area basis is computed by taking the maximum of these on a small area basis, a map of non-coincident peak loads. Simulation programs taking this approach have used a variety of temporal load models — everything from a class-based model that used only a single hourly peak-day load curve for every class, to a full end-use, appliance subcategory model on a five-minute basis. Examples of programs that have used this basic approach include the CEALUS (Canadian Electrical Association Land Use Simulation) program and the SLF-1 program developed and sold by Westinghouse Advanced Systems Technology in the late 1980s and early 1990s. The chief advantage of this approach compared to other simulation methods is simplicity of programming and relatively low level of computation. Note that here, the urban pole and factor maps are combined in one linear combination to yield the preference maps. In many other simulation approaches determination of the preference maps requires considerably more computation. For this reason, this basic simulation flow shown in Figure 14.2 is the most complicated form of simulation that can be implemented using direct computation of the factor and urban pole maps, which makes it much easier to program and verify. It does not require the high-speed numerical methods to be covered in section 14.3 in order to run within practical time limits (although they certainly can be used to make it very quick). This method has two major disadvantages, which are not serious in all forecasting situations. First, it cannot assess major changes in spatial distribution of influence among the stages of growth. Second, it cannot accurately model simultaneous decline and growth of a class within the service area but in different locations. Very often, one part of a service area will be shrinking,

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another growing, due to different basic industrial influences. This approach allocates only the total change in a consumer class on a global basis. Thus, if 10,000 homes were being abandoned in one part of a state, and 10,000 built in another, this approach would determine that there was no net change and forecast no change in spatial locations. Fuzzy or Numerical Logic? The spatial preference computation and pattern recognition used for the preference engine and class-transition decision making in a simulation model are a near ideal forum for application of fuzzy logic classifiers. A number of such attempts have been quite successful. Fuzzy, or "grade" logic, has been used for the entire surround and proximity classification, determination of the preference scores, and allocation prioritization. In a fuzzy-logic preference engine, the surround and proximity factor "functions" are implemented in fuzzy rather than numerical terms. For example, the "close to railroad" factor (Figure 14.3) might be: Close to railroad factor e [very close, moderately close, moderately far, far] The preference functions then use fuzzy logic to assess the combined weight of the various factors for class by class evaluation. This fuzzy logic preference

Very Sort of Close Close

Sort of Far

.5 .75 Distance - miles

Very Far

Very Sort of Close Close

.25

Sort of Far

Very Far

.5 .75 Distance - miles

Figure 14.3 Fuzzy logic railroad-proximity function as implemented for a fuzzy logic preference engine in a 2-3-2 simulation algorithm. Left, traditional fuzzy logic linear membership functions for each grade. Right, Gaussian membership functions have certain computational advantages.

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allocation can lead to either fuzzy or non-fuzzy outputs. Preference results that are fuzzy grades are something like Residential Preference e [very likely, moderately likely, not likely, none] Their impact is then implemented through allocation of growth to all small areas for all classes in a subsequent step requiring fuzzy logic. Factors are added, subtracted, and multiplied as needed in the course of determining preference function results, using fuzzy logic rather than algebraic rules. The resulting preferences are grade-specific: high-high match to needs, high match, etc., down to no match. These classifications are then used to allocate the global totals with fuzzy allocation rules. But often the fuzzy logic preference factors lead directly to numerical results, as in this equation used in the SLF program developed by Mirando and Monteiro: //"(distance to road is Close) and (distance of urban center is Moderately Close) and terrain slope is Moderate) and domestic saturation nearby is Medium) and (14.2) industrial saturation nearby is Low) then Domestic PfD is 20 consumers per stage and Industrial PfD is 0.1 consumers per stage Here, fuzzy logic leads to numerical results. Fuzzy logic works well in highresolution simulation algorithms. In fact there is every reason to believe, based on results, that there is little difference between numerical or fuzzy logic with respect to terms of accuracy, flexibility, or other characteristics, with the possible exception of the computational speed advantages numerical methods implemented in the space domain can provide. Use of Cellular Automata A simulation method that models growth as transitions among land use and density classifications can be implemented with a cellular automata instead of the type of transition matrices and functions described early in this chapter and used ins such widely used programs as the author's LOADSITE and FORESITE programs. However, there is no difference in concept and little in implementation between the two approaches. Cellular automata are discrete dynamic systems in which each point in a rectangular spatial lattice, or cell set, can transition from one class or state to another depending on a locally applied rule, at times which are the discrete


429

points in a set of transition opportunities over time. The cellular automata is basically a set of rules, applied to each cell in the lattice, that looks at local conditions (those around the cell) and determines if it will change or not depending on the status of its neighborhood and itself. The game of "Life" a popular mathematical game, is a binary cellular automata. "Binary" means each cell has only two states (filled or empty). A game's Automata involves a set of rules that determines back and forth transitions between the two states depending on the number of the cell's immediate neighbors that are filled (Gardner). Binary cellular automata are simple to implement and test, having only two states, and therefore only one decision to make with respect to transition at each iteration. There is no reason that several cannot act simultaneously on a lattice of cells. The land-use class transitions of a simulation program can be implemented using a set of binary cellular automata. The simulation rules become a set of cellular automata computing the development potential for various land use transitions (vacant to residential, vacant to commercial, low-rise to high-rise commercial, etc.) based on surrounding land-use development, present state in the cell and other conditions. Advantages of cellular automata models are that they bring a different, but formal theory to bear on the preference decision process and are simply to implement. Disadvantages are the logic and computation needed to resolve conflicts among the several binary automata functioning side by side (a vacant area can develop to any of several states, requiring a set of automata). But these advantages and disadvantages are small: in truth there is little difference and the standard simulation model is very close to being a cellular automata. Event Iterations Rather than Study Period Iterations Very often there is more than one cause of growth or major addition to the regional basic industry. For example, in Chapter 11 the growth in Springfield was due to two causes. First, there was the "normal" distributed growth of the city - a healthy local economy generates growth that appears to occur as "a little bit everywhere," generally modeled by locating an urban pole in the center of the downtown area and representing growth as "caused" by forces emanating from that location. In addition, Chapter 11 's example had a new factory that would contribute greatly to growth. This was going to be located on the outskirts of town, and it would most influence the side of the city near its location. The basic simulation model can determine the total amount of growth that takes place due to the distributed growth as well as due to the factory, by using the Lowry matrix, T. However, it will model all that growth influence as occurring from one source at one location, and allocate all growth with the same spatial basis, letting the urban pole map represent the "large area effects" as the same on all of the growth.

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The forecast of Springfield would be improved considerably if the forecast instead made two iterations for each study period: a) Apply the basic simulation (Figure 14.2) to allocate the "normal" growth expected in Springfield, what Susan allocated based on Rate and Consumer Studies Department's base forecast, using a polycentric activity center model of Springfield, and leaving out all consideration of the factory and the growth it causes. b) Perform a second iteration to model only the factory and the growth it caused. Begin by assigning the factory's land-use to small areas (typically done by "manually" editing the land-use data with the program's data editor). Using the Lowry matrix, T, compute the total growth impact (DT vector) caused by the factory, and repeat the entire simulation iteration (Figure 14.2) to spatially allocate these amounts of consumer growth, using an urban pole map with a single pole located at the new factory site and a radius of 50 minutes travel time from that site. These two iterations have to be done for each study period in which the factory is expanding. A larger city or region may have more than one large growth event such as a new factory or major employment change occurring simultaneously, in which case additional iterations, one for each of these major events, would have to be done within each study period. It is not necessary to modify the basic simulation approach in order to apply this approach of event-based forecasting. In all programs for the basic simulation approach that the author has used, the amount of growth allocated in each "study period" and the urban pole data can be changed from one "study period interaction" to another. To represent a new factory as a separate event, the user merely has to set up the data driving the basic simulation process so it is instructed to do two or more iterations per study period, calling for the following actual iterations (passes through Figure 14.2): • First forecast year distributed growth using polycentric activity centers • First forecast year's factory growth using urban pole at factory site • Second forecast year distributed growth using polycentric activity centers • Second forecast year's factory growth using urban pole at factory site • Third forecast year distributed growth using polycentric activity centers • Third forecast year's factory growth using urban pole at factory site • Fourth forecast year distributed growth using polycentric activity centers • Fourth forecast year's factory growth using urban pole at factory site


431

This approach has been widely used where needed. Other than the fact that it increases run time by a factor of two or more, depending on the number of discrete iterations done for each study period, it has no disadvantages, and it improves forecast accuracy substantially. Fairly straightforward changes to the program shell and the flow of computation within the spatial module can make the multiple iterations easier on the user and the computation slightly faster. Changes to improve ease of use usually include making it possible for the user to enter all years of data on distributed growth first, then identify the factory scenario and data on all years of its trends as a separate unit, etc. In addition, features are added to allow postforecast reporting to sum results for all iterations on a study period basis. Computational changes include features that calculate the urban pole maps one time and merely swap them in and out, and re-ordering to factor maps are computed only once per study period, not once per iteration. An example of such a program was the "SLF-2" or "advanced urban" version of Westinghouse's SLF1 program, widely used from 1987 into the mid-1990s. Multiple-Pass Direct Lowry Model One of the most popular variations on the basic simulation method (Figure 14.2) is to apply a direct Lowry matrix approach in a series of "passes" rather than a total Lowry matrix approach in one pass. Programming this is not inherently difficult, except that computation time mushrooms to the extent that high speed numerical methods which are difficult to program must be applied to make the approach practical on anything more than a very small study system. The basic simulation method used the Lowry T matrix to determine the total amount of change in all land uses (L vector) and allocated that to small areas in one step. In the modified direct Lowry simulation, growth is allocated in a series of passes corresponding to the stages of the "Lowry model" discussed earlier: basic industrial growth, directly caused growth, secondary growth, tertiary growth, and so forth. This method uses the same computational steps as in the basic approach, slightly re-ordered, and applies the urban pole maps in a slightly different manner. It does the following for each study period iteration: 1. It identifies available land for growth, making a map of "growth space." It computes the factor maps as in the basic simulation. It computes the urban pole map as in basic simulation. 2. The program takes as input the basic industrial growth, for the study period. To review from earlier in this chapter, this is a vector D of land-use changes for each class. Since this is the direct Lowry model, many of these may be zero or near zero.

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3. The program computes the preference maps for all classes based on the factor maps and the urban pole map, using the input coefficients for each class. The program allocates all the direct consumer class changes (those called for in the D vector) exactly as it did in the basic simulation for all the growth (the L vector). 4. The program computes the "center of growth" of the land-use change just allocated. It computes a new urban pole map with a single urban pole at this center of growth. Radius is the average radius of poles used in the previous stage. 5. The program computes the next stage of Lowry direct growth by applying T' to the D vector determined in step 2 above. This produces a new D vector of growth for the next pass. 6. The program goes back to step 3 unless a stopping rule is satisfied. The stopping rules may be that the D vector total is below some minimum threshold, or that six passes have been completed. In some cases, it is necessary to re-compute the factor maps during one or more of these passes — ideally it should be done at the end of every pass, but the computation time is considerable and the improvement in accuracy of some passes is negligible. As a rule, if the growth allocated to any one class since the last re-calculation of factor/pattern maps exceeds 3% of a class, the maps should be re-computed. This approach can track spatial changes in the center of influence of the different stages of Lowry growth. In many cases, such as that shown in Figure 14.3, this change dramatically improves forecasting accuracy with regard to location. In many others, the improvement either is unnecessary (the centers of all stages are similar) or is not sufficient to address fully the complexity of the forecast, as will be discussed below. This approach gives improved spatial forecast ability in about twenty percent of forecast situations, where for a variety of reasons the centers of influence for residential, industrial, and commercial growth may be different. A major disadvantage is that computation time increases by an order of magnitude over that required by the basic simulation method. This becomes much more of a burden if "multiple iterations" per study period are being done to represent the different locations of factories or other growth drivers. Careful attention to program flow, looping, and a variety of "tricks" in program organization can reduce the additional margin in computation time over basic simulation to a certain degree, but without the frequency domain discussed


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in the next section, this approach is simply not practical. Examples of programs using this approach include LOADSITE, a multi-map grid-based program developed and marketed by ABB Power T&D Company Inc. from 1988 to 1994, and FORESITE, its monoclass polygon/grid successor, as well as DIFOS (Distribution Forecasting System), a program similar to LOADSITE, developed and used in northern India (see Willis et al, 1995, and Ramasamy, 1988).

Basic Simulation

Multi-Pass Simulation

Figure 14.3 Examples of how growth influences within the factory scenario are handled by the basic and multi-pass simulations. At the top, the basic model represents all stages of growth stemming from the new factory as caused by spatial influences emanating from that site (black dot). At the bottom, the multi-pass approach recognizes that the secondary growth (predominately growth of the retail and commercial consumer classes) is driven by influences emanating from the center of new residential growth (dotted ellipse), and models them with an urban pole function centered there.

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Hierarchical Allocation As mentioned earlier, an alternative approach to simulation is a hierarchical structure in which the program increases the spatial resolution of the forecast in a series of top-down stages, beginning with a forecast on a system-wide basis (no spatial resolution). The next stage allocates that growth among large regions within the service territory. In the next stage the growth assigned to each large region is allocated among large areas within it, and so forth, until the process ends by allocating the growth to the small areas. A hierarchical program may use input data identical to the basic simulation approach in Table 14.1 and appear very similar to the user. It can be applied in multi-map or monoclass frameworks, and to polygon or small area format. It works with the same preference map concept, and in many cases computes factor maps and polycenrric activity center maps nearly identical to those of the basic simulation method. For an equivalent spatial resolution and level of analytical detail, the hierarchical approach requires up to 100% more computation, depending both on how the hierarchy is defined and on how the program flow is organized. Usually, the motivation for taking this approach is to reduce computer resource requirements, particularly that for active memory, when faced with very large small area data sets. The hierarchical program can be written so that no more than one region is in memory at one time, drastically reducing necessary memory requirements at the expense of run-time. For example, a state-wide utility service area of 250 miles by 250 miles would require a grid of 1,000 by 1,000 small areas at 40-acre spatial resolution. In a multi-map framework (needed at this resolution to obtain good accuracy) this would require roughly 200 Mbytes of memory. A hierarchical structure could be defined as 400 blocks of 50 by 50 small areas each. No more than nine of these blocks (22,500 small areas or about 4 Mbytes) need be retained in memory at one time, reducing memory requirements by nearly two orders of magnitude.2 A similar savings in memory requirements can be effected when using a monoclass framework or polygon framework.

2

Memory management was much more of a concern in the 1980s and 1990s than it is today. However, memory requirement turns out to be a fairly good proxy for the overall computation burden (time, effort) required when simulation methods are implemented within modern GIS and spatial analysis systems. Organization of block size and hierarchy structure to be optimally compatible with surround factor search radii, etc., is an interesting side issue that can make a considerable difference in program speed. See Willis etal, 1977.


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In addition, a hierarchical structure is often used when the small area approach is to be combined with spatial-time series (STS) regression models or urban models developed originally for other purposes — agricultural, trade, or econometric forecasting on a regional or geographic basis (Bennett). Such models often require substantial computer resources in their own right, limiting that available for the small area pattern recognition and factor analysis. In addition, the hierarchical method's "aggregation" of statistics during its bottomup path (see Figure 14.2) has proven a useful point for "translation" from one type of small area modeling approach to another ~ for example, the data required for an STS regression can be computed from the small area, factor, and pattern recognition results and collected on a region basis at this point, to be used by an STS model operating at a regional or macro-regional level. Although called top-down, most hierarchical simulation is circular, actually consisting of a bottom-up collection of data and statistics, followed by a topdown hierarchical allocation, as shown in Figure 14.4, for a three-level (global,

GLOBAL dinuuMi^ui ciiaitye

/2.......

Oil and National Gas Furnaces

0)

a: o

5

10

15 Years

20

25

30

Figure 16.5 Distribution of replacement ages for space-heating appliances.

Figure 16.6 Market penetration of competing electric and gas appliance types over a twenty-year period, as discussed in the text.

Advanced Demand Methods: Multi-Fuel and Reliability Models

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The direct end-use model tracks the two residential subcategories in this scenario separately: old (80% of consumers) and new (20%) residential classes. If the spatial model being used also uses this distinction (and has two different types of residential), then the end-use model's distinctions on usage by those categories can be applied spatially. Differences in Program Structure and Data Flow An important point from the standpoint of program development is that the direct end-use model is not strictly hierarchical, as are the types of models represented in Figures 4.5 and 16.2. It is rather important to recognize this when developing a direct end-use model (spreadsheet or otherwise) or when modifying a curve adder type of end-use model into a direct end-use model. In particular, the different data flow and interactions need to be considered carefully when building automatic checks and balances into the program to detect errors and force consistency and accuracy. There are three points worth careful consideration. Top-down -> bottom-up data flow The market share models in Figure 16.7 are at the "bottom" of the model. Information (if not data) flows "down to them" and then after their contribution to the analysis, "flows back up" to the top. As shown, end-use information on demand first flows "down" toward the appliance models, being repeatedly divided and sub-divided into finer distinctions of class, usage, and sub-usage until it reaches the market models. After it passes through, it is then "added up" into end uses, classes, and finally aggregated up to the top (overall curve) level. This "top-down then bottom up" flow is common to nearly all end-use models, but often not recognized by developers as a required feature. Diagrams like Figures 4.5 and 16.2 look to be one-way (down) and hierarchical. They appear to be one way because their end-use load curves combine both the demand and appliance model (essentially trivial, or from a theoretical standpoint, "null" versions of each). Appliance models can be on either side of the market model Figure 16.7 shows the appliance models "above" the market models. Here, the model basically flows through the following logic, moving down the figure: 1. The structure of the model branches "down" through classes, subclasses, and end uses, to the base end-use demand curves (e.g., residential, subclass 2, ulterior lighting demand). 2. For each end-use demand curve, the appliance model computes what each possible technology (incandescent, fluorescent, etc.) would do with respect to electric, gas, and other energy source demands if each were satisfying all of that demand.

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Washer-Dryer

1

Washer-Dryer

JC

Hour

Hour

Figure 16.7 A direct, multi-fuel end-use model works on a consumer-class basis, as shown here for just one class (only part of its model is shown). The model represents the various end uses in their natural units (i.e., lumens, BTU) of usage (top), converts those to electric, gas, and other energy demand with appliance models (middle) and then accumulates the various demand curves that result into electric, gas and other demand curves for the class. The model is a more effective planning tool than a "curve adder" model, and makes it easier to represent involved market penetration and technology change scenarios among appliances. The additional programming and set up effort required to build such a model is only justifiable if such scenarios will be routinely studied.


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3. These "all of that demand" curves are sent to the market share model where they are each weighted by their appliance types' respective market shares. 4. The resulting curves output from the market share model are added "up " within the structure of the model by end-use category, sub-class, class, and so forth, within their respective electric, gas, energy efficiency, and other venues. The flow of logic above traces the flow shown in Figure 16.7. But a direct enduse energy usage model will work just as well if written with the middle two steps reversed in order: 1. The structure of the model branches "down" through classes, subclasses, and end-uses, to the base end-use demand curves (e.g., residential, subclass 2, interior lighting demand). 2. These base end-use demand curves are sent to the market share model where they are each weighted by their appliance types' respective market shares to output appliance-type weighted demand curves. 3. For each appliance type weighted end-use demand curve, the appliance model computes what that respective technology (incandescent, fluorescent, etc.) would do with respect to electric, gas, and other energy source demands, when satisfying that demand. 4. The resulting curves output from the appliance models are added "up" within the structure of the model by end-use category, sub-class, class, and so forth, within their respective electric, gas, energy efficiency, and other venues. Different program "structure" The structure of end-use curve adders (Figure 4.5 and 16.2) is purely hierarchical — every nth-level element has only one progenitor. They are hierarchical in structure because only one energy source is involved. By contrast, multi-fuel models are not. The have dual paths on the data flow out of the appliance model. Among other points illustrated by Figure 16.7 is the "loop" structure of some parts of the end-use model for such an application. The figure shows that more than one data path flows both in and out of some elements of the model. Note in particular the gas washer-dryer appliance model. That appliance has both electric and gas components (so do gas heaters and AC units). It therefore contributes to "both" sides of this model. Such dual involvement of an appliance model is common and must be accommodated well. In fact, dual involvement is the rule if energy efficiency and conservation is one of the "energy sources" being modeled.

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16.3 SPATIAL VALUE-BASED ANALYSIS Quality of the electric power provided to the energy consumer, particularly reliability, is a major concern to electric utilities and consumers. This section will explore in more detail the concepts introduced in Chapters 2 and 4, that a spatial load forecast model can be used to forecast demand for quality value in somewhat the same manner it forecasts demand quantity of power. The section begins by looking at reliability and its market role with consumers. It then discusses the features needed for and the application of spatially differentiated reliability needs analysis and forecasting. Reliability's Growing Importance Distribution system reliability is driven by several factors including: (1) The increasing sensitivity of consumer loads to poor reliability, driven by both the increasing use of digital equipment and changing lifestyles. (2) The importance of distribution systems to consumer reliability as the final link to the consumer. They, more than anything else, shape service quality. (3) The large costs associated with distribution systems. Distribution is gradually becoming an increasing share of overall power system cost. (4) Regulatory implementation of performance-based rates, and large-consumer contracts that specify rebates for poor reliability, all give the utility a financial interest in improving service quality. Traditionally, particularly in the first few decades of the electric power industry, distribution system reliability was a by-product of standard design practices and largely reactive solutions to operational problems. Reliability was not engineered. It was achieved by standards. During the mid-twentieth century, power delivery reliability was provided by these time-tested standards and criteria - build it to these standards and service will be good enough to satisfy most consumers. However, in the first half of the 21st century, distribution system reliability has become a competitive battlefield for electric delivery utilities in several ways. First, delivery utilities do face stiff competition. Even though there may be no competing wires companies, delivery utilities face competition from alternative technologies and energy sources. Natural gas, distributed generation and energy efficiency measures are competition - if a delivery utility does a poor job, it will lose more of its market share to these competitors.


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Second, utilities also face competition through benchmarking. Regulatory commissions, consumers, and politicians all compare utilities on the basis of performance. Reliability is one of the foremost areas of comparison. And like any goal, reliability is best attained through study and planning - plan your work and work your plan. Toward that end, analysis of reliability needs and desires of the consumer base - where premium reliability may be demanded and where reliability will be needed for societal needs - is a first step. The Maximum Possible Reliability Is Seldom Optimal Planners must keep in mind that while reliability is important to all consumers, so is cost, and that only a portion of the consumer base is willing to pay a premium price for premium levels of reliability. The real challenge for a distribution utility is within tight cost constraints to: • Provide a good basic level of reliability. • Provide roughly equal levels of reliability throughout its system, with no areas falling far below the norm. • Provide the ability to implement means to improve reliability at designated localities or individual consumer sites where greater service quality is needed and justified. Reliability can be engineering into a distribution system in the same way that other performance aspects such as voltage profile, loading, and power factor are engineered. For details on methods that can be used, see Brown, 2002. Consumer Value Is the Key Quantity and quality both have value to the electric consumer. But so does cost. In fact, no factor in the decision about energy source and energy usage is more important to most consumers than cost. The three dimensions of power — quantity, quality, and cost - form a "value volume" that defines the range of the overall benefit consumers see in electric power (Figure 16.8). The author is aware that all consumers want reliability. But what they desire in terms of reliability is very close to irrelevant compared to what they will pay for reliability. The cost they will bear for the service they want measures the value they see in it. Conceptually, it is often useful to visualize where particular types of consumers are within the value volume. Some have a sufficient need for quantity and quality that they will pay a relatively high per-unit cost (integrated chip manufacturer). Others need a lot of power but have no need for reliability to the extent they will pay a lot for it (metal re-processing center). Still others need only relatively modest amounts of power, but will pay a relatively high rate to assure continuity of service (computerized data entry offices).

Chapter 16

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c

(Q 3

o

Cost Quality Figure 16.8 Ultimately, the value electric power consumers see in their electric power supply is based on a "value volume" of three dimensions: quantity, quality, and cost.

Telecom data center

Auto wrecking yard

The author

Cost Quality Figure 16.9 Each consumer has a demand that fits somewhere in this conceptual space of quantity, quality, and willingness to pay. Every individual will have his own values, but in general groups of similar consumers will have similar interests and values they place on electric power and reliability. They form market niches. As an example, petstore owners have unique electric reliability needs (tropical fish last only a few hours without heat and air bubbling), and form one market "niche" among many.


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The Market Comb Traditionally, the price of power in the electric utility industry was based solely upon the quantity used. A consumer would pay more if he or she used more power and less if their use was less. Except in rare and specially treated cases, an electric utility would offer a "one size fits all" level of quality. Consumers got what was available, whether or not they needed and would have paid for higher quality, or whether they would have preferred a discount even if it meant somewhat lower quality. In the wake of de-regulation, many people suggested that utilities needed to provide more reliability, citing statistics that usually showed about 40% of the electric consumer market wanted and was willing to pay for more reliability. But what often went unsaid was that most surveys showed an equal amount, 40%, thought lower prices were as or more important. The remaining 20% in most surveys seemed relatively satisfied with their service/cost. The real point is that electric consumers differ widely in their need for, and their willingness to pay for, reliability of service, as shown by Figure 16.9. They probably differ as much in that regard as they do in the amount of power they want to buy. Consumers also differ in exactly what "reliability," and in a broader sense, "quality," means to them, although availability of service, quick and knowledgeable response on the part of their supplier, and power of a usable nature are always key factors in their evaluation.

Customers who put little value on reliability and are very unwilling to pay for it.

Motivated by Price

Customers reasonably satisfied by the industry's traditional reliability and price combinations.

Customers who require high reliability and are willing to pay a higher price in return.

Motivated by Service Quality

Figure 16.10 The electric marketplace can be likened to a comb: composed of many small niches, each made up of consumers who have a different need for and cost sensitivity to reliability of service. Even those who put a high value on reliability may differ greatly in how they define "good reliability," one reason why there are no broad market segments, only dozens of somewhat similar but different niches.

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As a result, the electric power demand marketplace can be likened to a comb as shown in Figure 16.10. It is a set of many small niches that vary from the few consumers who need very high levels of reliability, to those who do not need reliability, only power, and are motivated purely by lowest cost. Whether or not a utility chooses to address these needs by offering consumers choice in reliability of service, planners and management alike should realize that the market has this characteristic. "One size fits all" service leaves a lot of consumers with unfulfilled needs. Reliability Can Be a Product Traditionally, electric utilities have not offered reliability as a price-able commodity. They selected equipment, and designed and engineered their systems based on engineering standards that were aimed at maintaining high levels of power system equipment reliability. These standards and methods, and the logic behind them, were actually aimed at minimizing utility equipment outages. The prevailing dogma maintained that this led to sufficiently high levels of reliable service for all consumers and that reliable service was good. This cost of the reliability level mandated by the engineering standards was carried into the utility rate base. A utility's consumers basically had no option but to purchase this level of reliability (Figure 16.11). Only large industrial consumers, who could negotiate special arrangements, had any ability to make changes bypassing this "one-size-fits-all" approach to reliability that utilities provided. The traditional way of engineering the system and pricing power has two incompatibilities with a competitive electric power marketplace. First, its price is cost-based: a central tenet of regulated operation, but contrary to the marketdriven paradigm of deregulated competition. Traditional electric utilities don't really have a choice in this. Any "one-size-fits-all" reliability price structure will quickly evaporate in the face of competitive suppliers. Someone among the various suppliers will realize there is a profit to be made and a competitive advantage to be gained by providing high-reliability service to consumers willing to pay for it. While others will realize that there are a substantial number of consumers who will buy low reliability power, as long as it has a suitably low price. The concept that will undoubtedly emerge (in fact, is already evolving at the time this book is being written) in a de-regulated market will be a range of reliability and cost options. The electric utility market will broaden its offerings, as shown in Figure 16.12. For example, distributed generation (DG) interacts with the opportunities that the need for differentiated reliability creates in a de-regulated marketplace in two ways. First, this new marketplace will create an opportunity for DG, which can be tailored to variable reliability needs by virtue of various design tricks (such as installing more/redundant units). Secondly, DG is a "threat" that may force utilities to compete on the basis of reliability, because it offers an alternative,


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Reliability Figure 16.11 Generally, the cost of providing reliability in a power system is a nonlinear function of reliability level: providing higher levels carries a premium cost, as shown by the line above. While reliability varies as a function of location, due to inevitable differences in configuration and equipment throughout the system, the concept utilities used was to design to a single level of reliability for all their consumers.

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Hi

Reliability

Figure 16.12 A competitive market will recognize the demand for various levels of reliability and providers will have to broaden their offerings (left) until the range of options available to consumers covers the entire spectrum of capabilities, as shown at the right. Actual options offered will be even more complicated than shown here because they will vary in the type of reliability (e.g., perhaps frequency or duration is more important to a specific consumer; some may care about service 24 hours a day 365 days a year, while others want "reliability" only during normal business hours).

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which can easily be engineered (tailored), to different reliability-cost combinations. Utilities may have no choice but to compete in pricing reliability. Utilities and energy service companies have an interest in studying consumers by not only how much power they want to buy, but what quality they want to buy. Delivery utilities need to study where these needs are so they can tailor their system to those demands.

End-Use Modeling of Consumer Power Quality Needs The consumer-class, end-use basis for analysis of electric usage, discussed in Chapters 2, 4, 15, and here provides a reasonably good foundation for study of the service reliability and power quality requirements of consumers, just as it provides a firm foundation for analysis of requirements for the amount of power. Reliability and power quality requirements vary among consumers for a number of reasons, but two reasons predominate: End-usage patterns differ: the timing and dependence of consumers' need for lighting, cooling, compressor usage, hot water usage, machinery operation, etc., vary from one to another. Appliance usage differs: the appliances used to provide end uses will vary in their sensitivity to power quality. For example, many fabric and hosiery manufacturing plants have very high interruption costs purely because the machinery used (robotic looms) is quite sensitive to interruption of power. Others (with older mechanical looms) put a much lower cost on interruptions. End-use analysis can provide a very good basis for detailed study of power quality needs in power delivery planning. For example, consider two of the more ubiquitous appliances in use in most consumer classes: the electric water heater and the personal computer. They represent opposite ends of the spectrum from the standpoint of both amount of power required and cost of interruption. A typical 50-gallon storage electric water heater has a connected load of between 3,000 and 6,000 watts, a standard PC a demand of between 50 and 150


501

watts. Although it is among the largest loads in most households, an electric water heater's ability to provide hot water is not impacted in the least by a oneminute interruption of power. In most cases even a one-hour interruption does not reduce its ability to satisfy the end-use demands put on it.3 On the other hand, interruption of power to a computer, for even half a second, results in serious damage to its "product." Often there is little difference between the cost of a one-minute outage and a one-hour outage. It is possible to characterize the sensitivity of end uses in various consumer classes by using this "Two-Q" end-use basis. This is in fact how detailed studies of industrial plants are done in order to establish the cost-of-interruption statistics which they use in value-based planning (VBP) of plant facilities and in negotiations with the utility to provide upgrades in reliability to the plant. Following the recommended approach, this requires distinguishing between the fixed cost (cost of momentary interruption) and variable cost (usually linearized as discussed above) on an end-use basis (see Chapter 2). An end-use load model as covered in Chapter 4 and here can be modified to provide interruption cost sensitivity analysis, which can result in "twodimensional" appliance end-use models as illustrated in Figure 16.13. Generally, this approach works best if interruption costs are assigned to appliances rather than end-use categories. In residential, commercial and industrial classes different types of appliances within one end use can have wildly varying power reliability and service needs. Thus, reliability studies are really only feasible, in good detail, with an "appliance sub-category" type of end-use model, or a direct end-use model (see Section 16.2). Modifications to an end-use simulation program to accommodate this approach are straightforward. Every appliance end-use load curve now has "two dimensions." Each summation of curves keeps two summed load curves — quantity and quality. Used with a spatial simulation approach, they produce analysis of both Qs — quantity and quality - by time and location, as shown in Figures 16.14 and 16.15. A direct appliance-level end-use model is particularly useful in this type of study because it can study the power quality needs of different appliances aimed at the same end use. For example, variable-speed chillers for commercial offices save energy, but their computer control systems are very sensitive to voltage sags and brief interruptions. By contrast, traditional chiller systems have a relatively low demand for power quality but have a slightly higher level of demand for quantity. 3

Utility load control programs offer consumers a rebate in order to allow the utility to interrupt power flow to water heaters at its discretion. This rebate is clearly an acceptable value for the interruption, as the consumers voluntarily take it in exchange for the interruptions. In this and many other cases, economic data obtained from market research for DSM programs can be used as a starting point for value analysis of consumer reliability needs on a value-based planning basis.

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Residential Bectric Water Heater

HotroftheDay

Figure 16.13 The simulation's end-use model is modified to handle "two-dimensional" appliance curves, as shown here for a residential electric water heater. The electric demand curve is the same data used in a standard end-use model of electric demand. Interruption cost varies during the day, generally low prior to and during periods of low usage and highest prior to high periods of use (a sustained outage prior to the evening peak usage period would result in an inability to satisfy end-use demand).


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Figure 16.14 Result of a power quality evaluation using an end-use model. Top: the daily load curve for single-family homes segmented into four interruption-cost categories. High-cost end uses in the home are predominantly digital appliances (alarm clocks, computers) and home entertainment and cooking. Bottom: total interruption cost by hour of the day for a one-hour outage. Compare to Figure 2.17.

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1 Hcrdocpy

22-FEB-95 11-42-32

(TTM i

cirscr to OR Hit tne rutrcer Keg or the required fegtxre.

:

;;::: |:ii;

Darker shading indicates higher reliability demand

Lines indicate highways and roads

Figure 16.15 Map of average reliability needs computed on a 10-acre small area grid basis for a port city of population 130,000, using a combination of an end-use model and a spatial consumer simulation forecast method of the type discussed in Chapter 15. Shading indicates general level of reliability need (based on a willingness-to-pay model of consumer value).

16.4 CONCLUSION With few exceptions, advanced spatial forecasting applications almost exclusively use the simulation method, building upon its spatial and end-use models to apply detailed consumer and appliance-usage modeling concepts which are beyond the scope of traditional T&D planning. Simulation is a tremendously flexible approach that can accommodate a variety of different special needs. Generally, the modeling aspects of simulation are expanded in four ways: Changes in basic structure of the simulation approach, as exemplified by the shift to road-link analysis for rural forecasting (Chapter 18) or the models for redevelopment and three-dimensional growth (Chapter 19), or changes in forecast priorities for electrification (Chapter 20). End-use models are expanded, much more than just growing in the number of classes, as explained here. Generally, more classes of land-use (consumers) will be needed for any special application. Almost invariably, a "special application" is targeting


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electric or energy usage in some way. Whether spatial or otherwise, such targeting is accomplished within a simulation program through the use of more classes - more specifically, through the coordinated use of class definitions that cross over between spatial and temporal models. The multi-fuel and reliability-based planning applications covered here certainly do not exhaust the list of advanced applications for spatial electric forecasting end-use models. Others include expanding value-based planning to include power quality (voltage stability, harmonics) rather than only reliability; planning of automated meter reading (AMR) systems and automation; marketing studies in a competitive market; distributed resource planning; and power shortage (rolling blackout) planning. Regardless, spatial load forecast simulation can be applied to handle all of these and other applications, in addition to its primary role of T&D asset planning, through the use of an artfully modified and expanded end-use model.

REFERENCES J. H. Broehl, "An End-Use Approach to Demand Forecasting," IEEE Transactions on Power Apparatus and Systems, June 1981, p. 271. R. E. Brown, Electric Distribution System Reliability, Marcel Dekker, New York, 2002. G. Dalton et al, "Value Based Reliability Transmission Planning," paper presented at the 1995 IEEE Summer Power Meeting, number 95SM566. Electric Power Research Institute, DSM: Transmission and Distribution Impacts, Volumes 1 and 2, EPRI Report CU-6924, Electric Power Research Institute, Palo Alto, CA, August 1990. R. Orans et al, Targeting DSM for Transmission and Distribution Planning," IEEE Transactions on Power Systems, November 1994, p. 2001. H. L. Willis and G. B. Rackliffe, Introduction to Integrated Resource T&D Planning, ABB Power T&D Company, Raleigh, NC, 1994. H. L. Willis, L. A. Finley, and M. J. Buri, "Forecasting Electric Demand for Distribution Planning in Rural and Sparsely Populated Regions," IEEE Transactions on Power Systems, November, 1995, p. 2008. T. S. Yau et al,"Demand-Side Management Impact on the Transmission and Distribution System," IEEE Transactions on Power Systems, May 1990, p. 506.

17 Comparison and Selection of Spatial Forecast Methods 17.1 INTRODUCTION This chapter focuses on the selection of a forecasting method and its application to spatial electric load forecasting in a practical electric utility environment. The primary purpose of the forecasting discussed here is T&D planning, but targeted marketing, energy conservation and efficiency programs, and competitive planning issues are also addressed. This chapter begins with a categorization of the methods covered in previous chapters including presentation of nomenclature for identification of the key components in simulation forecast methods. A set of comparison tests of nineteen forecast programs, representative of the spectrum of available spatial load forecast methods, is reviewed. All are uniformly applied to two utility test cases - a metropolitan and a rural system. Accuracy, labor, data, computer requirements and operating characteristics, as well as special features of all nineteen methods are evaluated and contrasted. Section 10.4 covers data, data sources, and procedures for collection of the data required to support spatial load forecasting. Section 10.5 covers the selection of the most appropriate forecast method for a particular utility and application. The method presented is applied to a realistic utility problem, and its variation to fit a number of other situations is discussed. The chapter ends with guidelines for spatial forecast application.

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17.2 CLASSIFICATION OF SPATIAL FORECAST METHODS In order to compare and contrast spatial load forecast methods, it is useful to have a system to categorize them on the basis of their most important characteristics. To the author's knowledge, all spatial load forecast methods use the small area technique, in which the territory of interest to the planner is broken into a set of small areas of appropriate size and shape so that the forecasting of all individual areas will provide the required information on where load is located.1 Small areas used in the analysis of load growth can be either irregular polygons (often substation or feeder service areas) or the rectangular areas defined by a uniform grid. Regardless, what is important is spatial resolution the amount of "where" detail the forecast provides, which is related to the size of the small areas - from the standpoint of forecast detail, the smaller, the better. Categorization by Approach and Algorithm Spatial electric load forecasting methods can be grouped into three categories: non-analytic, trending, and simulation. Non-analytic methods include all forecasting techniques, computerized or not, which perform no analysis of historical or base year data during the production of their forecast. Such a forecasting method depends entirely on the judgment of the user, even if it employs a computer program to accept "input data" consisting of the user's forecast trends as well as to output maps and tables derived from that bias. As will be shown later in this chapter, despite the skill and experience of the planners who may be using them, these methods fall far short of the forecast accuracy produced by analytic methods. Trending methods, discussed in Chapter 6, forecast future load growth by extrapolating past and present trends into the future. Most utilize some form of univariate or multivariate interpolation and extrapolation, most notably multiple linear regression, but a wide variety of other methods, some non-algebraic have been applied with varying degrees of success. Trending methods are most often categorized according to the number of variates (e.g., univariate, bivariate, multivariate) and the mathematical technique used to perform the trend extrapolation

1

There are other approaches. For example, presumably one could develop a function F(x, y, t) whose value corresponded to the load at location x, y, in year t, and then through some means "grow" or extrapolate this function through time and space so that it would forecast future load when solved for input values of t greater than the present year. However, as far as the author knows, all attempts to make this approach work have met with so little success that the researchers have never even tried to publish their results.

Comparison and Selection of Spatial Forecast Methods

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(e.g., linear multiple regression, vacant area inference (VAI), load-transfer coupling (LTC)). Computerization of trending methods is most often applied using standardized "horizontal application" software - programs designed for general purpose use such as electronic spreadsheets like Excel or Lotus 1-2-3, mathematical manipulation packages like MathCAD, Mathematica, and Matlab. Simulation methods analyze and attempt to replicate the process by which electric load changes in both location and time, as discussed in Chapters 7, 8, and 9. All use some form of consumer-class or land-use framework to assess location of demand, and most represent usage over time on a per-consumer basis using load curve distinctions defined on that same consumer-class basis. Most are computerized, using software developed specifically or primarily for electric power planning purposes. A number of computer programs for "land-use-based load forecasting" are non-analytic, doing nothing more than outputting (often with beautiful color maps and well-organized tables) numbers and trends input by the user. Despite the manufacturers' claims, they often do little beyond regurgitate the user's judgment-based forecast as input. However, most simulation methods apply something between a limited and a very comprehensive analysis of the local geography, locational economy, land use, population demographics, and electric load consumption. Categorization of simulation methods is difficult because of the great variety of urban models, land-use bases, pattern recognition algorithms, end-use load models which have been developed and applied, and differences in how they are put together into complete programs. However, the nomenclature listed in Table 17.1 has proven useful in distinguishing between one method and another and seems to categorize methods consistently into groups with similar performance. This terminology was first developed in the early 1980s, and a majority of significant publications on simulation spatial load forecast methods make use of it. Simulation methods are classified by a three-digit nomenclature (Table 17.1), as for example 1-2-3, with the three digits representing, respectively, the type of approach used in each of the three major portions of the spatial simulation: • The first position, X-x-x, indicates the type of global/large area model.

• The second position, x-X-x, indicates the type of spatial small area model. • The third position, x-x-X, indicates the type of temporal load model.

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Table 17.1 Categorization of Spatial Load Forecast Simulation Programs Global/large area model 0 - none 1 - input control totals, no urban pole or large area effects analysis 2 - input control totals or Lowry T matrix computation, polycentric activity center model of large-area growth influences including methods with distributed pole computation 3 - input control totals and/or multi-stage Lowry computation and polycentric activity center 4 - urban sub-areas "compression and competition" model (see Chapter 19) Small area/local attribute model 0 - none 1 - small area class changes are assigned based on preference "scores" input by the user, from judgment or a priori analysis 2 - small area class changes are assigned based on preference "scores" determined by analysis of less than six surround or proximity factors 3 - small area class changes assigned based on preference "scores" determined by analysis of more than six surround or proximity factors Temporal load model 0 - none 1 - no load curves, uses a single kW value per consumer or acre for each consumer class 2 - peak day or longer load curves per consumer or acre for each consumer class 3 - peak day or longer load curves using a consumer-class end-use model on a per consumer or acre basis 4 - spatially time-tagged consumer class areas in company with time-indexed enduse curves


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Table 17.2 Spatial Electric Load Forecast Programs Program

Year

Characteristics and Comments

1964

Spatial temporal extrapolation using a single urban pole and a monoclass land-use model to provide load densities (Lazzari, 1965). To the author's knowledge, the first computerized use of any type of "gravity model" for electric utility forecasting. Basically a 2-0-1 model.

CAREFUL

1991

A 3-3-4 monoclass land-use simulation based on Carrington's staged timetagged linear load curve model combined with a time-tag update model of energy efficiency and demographics, marketed and supported by Alerti in the early to mid 1990s (Carrington and Lodi, 1992).

CEALUS

1981

A 2-3-3 type multi-map land-use based simulation program sponsored by the Canadian Electric Association. Based heavily on ELUFANT (see below) with simplified preference function computations. The first really good implementation of the modern simulation concept (CEA, 1982).

DIFOS

1987

A 1-3-2 monoclass simulation based on a variation on the basic land-use theme (Ramasamy, 1988) coupled with a designated redevelopment area feature. It required heavy user interaction but could do quite good forecasts when set up with good inventory data. Superceded by PFUC (see below).

DLF

1980

A 2-1-1 type multi-map land-use simulation developed and marketed by Westinghouse Electric Co., 1978-1982. Widely used by utilities in the US through the late 1990s (Brooks and Northcote-Green, 1978).

ELUFANT

1977

A 2-3-2 multi-map land-use based hierarchical simulation program developed at Houston L & P, 1973-1984. The first modern simulation approach, done by a team that had the right basic idea but overcomplicated the implementation details (Willis et al., 1977).

FORECAST

1994

A 3-3-3 monoclass land-use simulation, PC-based program, developed based on many of the author's papers and marketed by MVEN Technologi from 1994 until 1998. The program ran under a PC-based GIS system called Atlas Map.

FORESITE

1992

A 3-3-3 monoclass land-use simulation, basically a simplified algorithm based on LOADSITE (see above) with improved urban renewal forecasting and a modern GUI. Currently developed and marketed by ABB Power T&D Company, Inc., and widely used by utilities worldwide (Willis et al 1995).

LTCETRA

1985

A LTC trending program developed by several utilities in South America and used from 1985 into the new century (Rodriguez).

LOADSITE

1986

A 3-3-3 capable multi-map land-use simulation that did all spatial computations in the frequency domain and included an appliance-level enduse/DSM model. Developed and marketed by ABB from 1989 through 1993. (Willis, Vogt, and Buri, 1991).

LOADSITE-2 1990

A 4-3-4 simulation multi-map land-use simulation that used urban compression, frequency domain preference functions, and a time-tagged end-use/DSM model. In the author's opinion, still the most powerful spatial forecast simulation developed to date.

MATILDA

The author's forecast and display shell procedure for working with researchlevel forecast algorithms. Used for display and interpretation of examples in various chapters in this book.

APS-1

2001

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Table 17.2 cont. Program MLF

Year 1985

Characteristics and Comments

A 1-1-1 type land-use based simulation method developed and marketed by Scott & Scott (now Stoner) in the last quarter of the 20th century. Basically a simple but useful program to assist with "manual simulation."

MULTIVARIATE 1

Developed in 1977. Historical data extrapolation with cluster analysis, landuse, multivariate regression manifold fit, and a gravity type urban model, 1977-1984. The smarter alternative to Trend developed by EPRI project RP570. (Wilreker, 1977; EPRI, 1979).

PFUC

1999

A probabilistic 2-3-1 simulation using multi-iteration Markov modeling and "majority" selection to predict land-use transitions. A year-2000 derivative improvement of DIFOS, (above) marketed in India and SE Asia by Terncala spa.

PROCFV

1985

A derivative of the method but not the program code from Multivariate, this multivariate curve fitting program used principal components analysis and multiple regression to forecast regions, groups and then individual small areas in a hierarchy of trending (Grovinski, 1986).

PROCAL

1989

A 2-2-2 type multi-map land-use based hierarchical simulation program developed in South Africa, 1987, a simple but effective program that used manual input of urban-pole like priorities and computed preference factors on a local area basis (Carrington, 1988).

SLF

1980

A 2-1-2 type monoclass land-use based simulation method developed and marketed by Westinghouse Electric Co. as a smarter version of DLF (above). Difficult to use and depended too much on the user's savvy. 19801984 (Brooks, 1979).

SLF

2000

Cellular-automata/fuzzy-logic 2-3-1 simulation program. A very slick and innovative algorithm coupled with an apparently easy to use program, developed on an Arclnfo GIS platform and programmed in Avenue (Miranda, 2001).

SLF-2

1983

A 2-3-3 multi-map land-use simulation program developed and marketed by Westinghouse Electric Co. in the late 1980s. Modified in 1984 to use frequency domain computations in its preference model (Willis, 1983 and Willis and Parks, 1983).

SUSAN

1997

A hybrid mono-map land-use simulation/trending method developed at ABB in 1997, which provided simulation-like accuracy and representativeness with 1/3 the user labor (see Chapter 16).

TEMPLATE

1986

Template matching trending developed by a south American utility and used until the late 1990s for forecasting of urban and rural loads (Willis and Northcote-Green, 1984).

TREND

1977

Feeder/substation peak history extrapolation using regression curve fit. The first result of research to take spatial forecasting beyond "dumb" regression and better than most other multiple regression approaches (Menge, 1977; EPRI, 1979).

no name

2001

A research-grade 1-3-1 land-use based simulation method. While it lacks the global model needed for a complete forecast model, it uses an innovative technique to solve for the surround and proximity factor solutions, a major breakthrough (Wan Lin Wun and Land Win Lu, 2000).


513

This nomenclature distinguishes forecast methods by the type of modeling approach and detail used, not by the efficiency or method employed in a particular program's numerical computation and program flow. Thus, whether a computer program uses spatial frequency domain computation in its pattern recognition or not, if it analyzes more than six proximity and surround factors in the course of its preference mapping, it is a x-3-x method. Similarly, a 3-x-x method uses a multi-stage Lowry model to represent global and large-area growth influences, regardless of whether it computes them with urban poles on a distributed basis or not. Therefore, this nomenclature is reasonably good at classifying simulation methods with regard to their data needs and potential forecast accuracy, but not with regard to computer resource requirements and computer run time. Table 17.2 summarizes twenty-four computer programs which have been developed expressly for electric load forecasting, and for which publications are available covering both method and results. (References cited in table.) A majority utilize simulation for two reasons. First, most trending applications use general-purpose software to apply the trend calculations. Second, simulation has proved increasingly popular due to its higher accuracy, easier communicability, and better credibility, in spite of its generally higher resource requirements. In summary, spatial electric load forecasting methods can be grouped into three major categories based on the approach they use - non-analytic, trending, and simulation. Within the last two groups, a particular method can be further categorized by the mathematical or modeling methods employed to compute the forecast. Categorization is useful in order to compare and contrast forecast methods based on their resource needs, capabilities, and proven results. 17.3 COMPARISON TEST OF NINETEEN SPATIAL LOAD FORECAST METHODS This section presents a discussion and comparison of nineteen different spatial forecasting methods, including their application to two utility test cases. They are compared on the basis of accuracy, forecast applicability, data needs, and resource requirements. The research upon which these results is based was performed by the author while at Westinghouse Advanced Systems Technology and ABB Systems Control, from 1982 to 1995. Additional theory and evaluation concerning these comparisons is available in two technical publications (see Willis and Northcote-Green, 1984, and IEEE Tutorial on Distribution Load Forecasting, Chapter 2). However, the results and discussion given here are updated versions of those earlier works. Test Problems and Test Procedure Test case A is a metropolitan area in the southern United States, which includes a large city and the surrounding periphery, and contains both high growth and low/negative growth regions within it. Case B is a rural region in the central

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United States, which includes five small towns, two of which are growing at modest rates. In both tests, data from 1975 to 1981 was used to forecast 1992 loads. Table 17.3 gives more information on each of the test cases. These two cases were selected because they represent two important and different classes of problems, metropolitan areas and small towns/rural areas. The primary reason for selecting these specific cases was that the data necessary to uniformly test and evaluate all methods were available for the period 1965 through 1992. Available data were almost identical in nature and quality for both test utilities and will be discussed later. Forecasting performance was evaluated based on both error measures (AAV, Ux, as discussed in Chapter 5) and on what the author believes is the only true

Table 17.3 Forecast Comparison Test Case Utility Systems Characteristic Territory analyzed, sq. miles Number of consumers 1981 Number of consumers 1 992 1981 peak load* 1992 peak load* Residential, % peak Commercial, % peak Industrial, % peak Municipal, other % peak Agricultural, % peak 1 992 annual load factor Major transmission voltages Distribution voltages Number of distr. substations Average substation peak

Utility A 2,000 446,000 544,000 4409 5241 42 23 28 5 3 67% 345 & 138kV 12.5, 34.5 kV 118 48

* All load values are weather corrected to a standard weather year

Utility B 21,000 65,000 71,700 285 312 55 22 6 4 13 58% 69&115kV 12.5&25kV 24 15


515

criterion - the contribution the forecast method's errors appear to make to the system planning. Figure 17.1 shows the evaluation procedure used with each forecast method. First, the forecasting method was used to project 1991 loads from 1981 and prior data. Its forecast was fed into an automatic T&D expansion planning program, which produced a minimum cost 1981-1991 T&D expansion plan to serve the forecasted loads.2 The actual 1991 loads were fed into this same planning program, producing a 1981-1991 hindsight plan (one based on a "forecast" with zero error). The expansion planning program was then asked to compare the "plan based on the forecast" with the "hindsight plan" and to "fix" any deficiencies in a minimum cost manner. If the forecast was accurate, this cost would be small. If it was poor and had misguided the original plan, it would be large. Regardless, this "fix-up cost" was added to the cost of the original plan as based on the forecast, and compared to the cost of the hindsight plan. Any margin of cost above the hindsight plan's cost was attributed to the forecast error.3 This procedure attempts to reproduce in a consistent, repeatable manner, the "real world" situation in which a forecast is used to guide expansion planning, where any forecast mistakes must be corrected after the fact with addition projects or changes to the system. The use of an automatic expansion planning program as "the planner" in this case was done both to assure bias-free evaluation of all forecasts, and because the author had nowhere near the resources to manually produce nineteen (counting the hindsight plan) expansion plans for each systems. Readers who have used automatic expansion planning programs will realize that they are far from perfect and that usually intervention and fine-tuning by the user can further refine cost. This was not done in this case - the goal here being a consistent, reasonable method of evaluation. Thus, while any of these plans could be slightly improved by further work on the part of an experienced T&D planner, all are of the same relative quality. The author believes that the numbers established by this procedure are representative of relative performance of the methods and nothing more. In terms of predicting absolute rather than just relative performance, this procedure will tend to overestimate error impact, because it does not allow for interim-period discovery and correction of forecast errors, but instead assumes that all plans are fully built through 1991 and can only be "fixed" after the 19811991 expansion has been built (and all the budget spent). In actuality it is not uncommon for errors to be discovered about halfway through a ten-year planning period, and some can be corrected.

2

Actually, a two-stage coupling of expansion planning programs called SUBSITE and FEEDERSITE. (See Willis, Engel and Buri, 1995.) 3 This difference is always positive, i.e., the "as repaired cost" will always exceed that of the hindsight plan. The hindsight plan is, within the framework of the analysis, the optimum (minimum cost) plan. Any other plan must have a higher cost.

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FORECAST METHOD

load growth earsTtoT+10

Comparison finds AAV andU

AUTOMATED

AUTOMATED

AUTOMATED

"Forecastdriven plan" forT + 10

(

load growth years T to T+10

Comparison gives cost impacts

Forecastdriven plan" forT+10

(

Forecast-\ driven plan" y \ forT + 10 /

Figure 17.1 Procedure to compare forecast methods works from a base year, T, with twenty years of load history (years T - 20 to T -1), and forecasts through year T + 10. At the top, actual (left) and forecast (right) loads were compared using load-based error evaluations. The procedure inside the dashed-line box is the actual test procedure, which produced a "hindsight plan" (left side) based on knowledge of the actual load that developed. It represents a plan done with a "perfect" forecast. This was compared to a plan produced from the forecast method being tested, which is updated "at the last minute," as a utility would when forecast errors were detected as time passed (right).


517

However, this procedure also assumes that any desired modifications to minimize the "patch up" cost could be determined and added in time. Often it is "too late" to obtain ROW, order equipment, or install facilities that the planner would prefer to have, and less desirable (i.e., more expensive) modifications must be made because they are the only ones available at that late date. The effects of these two "real-world" inaccuracies in this test procedure somewhat cancel one another, but to what extent is not determinable. The author believes that in actual application, total impact of any method tested would probably be only about two-thirds of that indicated by the tests. Two cost impacts were analyzed for all nineteen forecast-based plans. These were capital expansion cost and losses cost. Very often, when a poor plan is "patched up" capital cost is kept as low as possible by accepting higher losses costs. Thus, the impact of poor forecasting is often to increase long-term losses costs more than capital expansion cost (although often capital cost is increased considerably, regardless). All cost impacts are reported as percentage increases above the hindsight plan's. Accuracy was also evaluated on the basis of two statistical methods, average absolute value (AAV) of the small area errors, and planning impact sensitivities, Ux error measures for the transmission, substation, and feeder levels. These are reported in terms of percentage of average small area growth. As mentioned earlier, the computer program implementation is important in the overall ease-of-use of a program. In this test, all but four methods (numbers 9, 10, 18 and 19 as listed and described later in this chapter) of the nineteen were tested within the same computer program, a research version of a commercial load forecast program called LOADSITE. The research version used could mimic, or copy, any small area forecast algorithm, through its own forecast simulation language. For the test, algorithm instruction sets representing all but methods 9, 10, 18 and 19, were each used in turn. Methods 9, 10, 18, and 19 were tested using the original programs (developed by the author), at a somewhat later date than the other methods. However, results shown in the next few pages have been adjusted to represent as well as possible the relative performance of all 19 methods. Data common to several methods were identical. For example all land-usebased forecast programs ran from the same set of land-use maps and geographic data, gathered once, verified, and used for all in order to assure that differences in the test were due to differences in forecast method, not data. The only difference was in spatial resolution - methods that used one-mile land-use grids had the land-use data accumulated to that resolution, methods that used a polygon basis had the data loaded at that resolution. Similarly, all trending methods drew from a common set of tested historical data and metered load readings.

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The Nineteen Forecast Methods Nineteen different spatial forecast methods were tested, as summarized in Table 17.4. In each case, the forecast method was applied to the data for which it was designed, in the data format (grid, polygon) and spatial resolution for which it was designed as identified in the technical references listed. Methods one through nine are trending methods. By contrast the remaining eight methods use some form of simulation to produce the forecast. The same land-use base and historical load data were used for all nineteen methods, with the format, framework, and spatial resolution varying as appropriate for each individual method. Thus, spatial resolution varied, as did whether they used polygon or grid data formats, and multi-map or monoclass frameworks - that is part of the test. The nineteen methods are: 1

Polynomial curve Jit using linear regression (Meinke, 1979; EPRI, 1979). This was applied on a feeder service area basis; the linear regression is used to fit a cubic log polynomial to the annual small area peak loads. Extrapolation was applied to the most recent seven years of historical load data and horizon values at +20 and +22 years. Data used consisted of weather-corrected historical peak loads for the past ten years for each feeder (only the most recent seven were used) and horizon year loads selected at random (see Chapter 6).

2

Ratio shares curve fit. Also referred to as disaggregation trending (Cody, 1987), this method involves regressing area loads against the system load total rather than in time. This was applied on a feeder service area basis using seven years of historical data, this having proved best among the range of 4-8 years tested for this method. Data used consisted of weather-corrected historical peak loads for each feeder and the past ten years of system peak loads (also weather corrected).

3

Cluster template matching (Willis and Parks, 1983) involves two steps. A clustering algorithm is used to group the small areas into sets. Each set consists of areas whose growth history is similar and all areas within a set are extrapolated using the set's average trend (also discussed in Chapters 9 and 16). This was applied on a feeder service area basis. Data used consisted of weather-corrected historical peak loads for each feeder for the past twenty years and horizon year loads selected at random. This method was popular throughout the 1980s and early 1990s, and is still frequently used, because it is extremely parsimonious of numerical resources. As mentioned in Chapter 6, the method's chief advantage over other methods is that it can be efficiently implemented as a computer


519

program without decimal arithmetic, and with some slight innovation in program flow, without multiply or divide calculations as well. This advantage is of doubtful value in a world where even low-cost personal computers can perform 80-bit floating point artihmetic, but the method is included for completeness' sake. 4

Vacant area inference (VAI) curve fit (Willis and Northcote-Green, 1981) applied on a 160-acre grid basis. The individual small area load histories are trended using polynomial curve fit with linear regression, as well as the total loads for blocks (groups) of areas. Differences are "inferred" as load forecasts for "vacant" areas. Data used consisted of weather-corrected historical peak loads for the past ten years for each feeder (only the most recent seven were used) and horizon year loads selected at random (see Chapter 6).

5

Cluster template in a VAI structure following a procedure of developing templates for individual areas and for blocks of areas was applied on a grid basis. Data used consisted of weather-corrected historical peak loads for the past twenty years for each small area and horizon year loads selected at random.

6

Urban pole centroid method as first developed by Americo Lazzari (Lazzari, 1965) on a grid basis, with one pole center used in every identifiable city or town, fitted to historical loads by varying the center height, the center location, and the function shape and then extrapolated into the future, subject to the constraint that volume (total area under the functions' surface - the total load) equal the projected system total. Data used consisted of weather-corrected historical peak loads for the past ten years for each small area, land-use data for the most recent year, and horizon year loads selected at random.

7

Consumer-class ratio shares (Schauer, 1982; Cody, 1987) using a multivariate regression. This is similar to method 2, except each area is trended against the total system load in separate classes of residential and commercial. Data used consisted of weather-corrected historical peak loads for the past ten years for each feeder, and residential and commercialindustrial consumer counts.

8

Multivariate rotation-clustering extrapolation (Wilreker et al., 1977; EPRI, 1979). Basically, this procedure simultaneously extrapolates small areas based on a number of factors, rather than just load, after first normalizing and statistically adjusting their historical data to maximize significant variance of the data sets.

520

Chapter 17 Data used consisted of weather-corrected historical peak loads and energy sales for the past ten years for each small area, land-use data for the most recent year and ten years prior.

9

LTCCT curve fitting regression, the most accurate trending method the author has discovered (Willis et al, 1994) is a combination of LTC regression and geometric constrained trending (see Chapter 9). Data used consisted of weather-corrected historical peak loads for the past ten years for each feeder, and the X-Y coordinates of the "center" of each feeder (obtained by averaging the X-Y coordinates of all nodes assigned to each feeder in a distribution feeder analysis database).

10 1-0-1 non-computerized land-use based manual approach, a manual forecast dependent only on user judgment. This method was applied, with insignificant variations from the method defined in Chapter 11, by the author or those under his direct supervision, for both test cases, as the first of the cases done in each utility case (thus the judgment was not "contaminated" by knowledge of the results of the other forecasting methods). Spatial resolution was 160-acre grid for case A, and 640-acre (square miles) for case B. Data used and the method applied are given in detail in Chapter 11. 11 7-7-7 computerized method, similar to that first published by Walter Scott (Scott, 1974), operating on nine land-use classes in a multi-map format at 40-acre grid basis. Data used includes land-use distribution by class for the base year (1981), load totals by land-use class, and consumer count forecasts by class for 1975-1981 (historical) and 1982-1992 (projected in Rate and Revenue forecasts). 12 2-2-7 land-use based simulation on a 40-acre grid basis using a multi-map format and nine land-use classes (Brooks and NorthcoteGreen, 1978) and widely used by many electric utilities during the 1980s. Required data are land-use and zoning data on a small area basis, information on the locations of major roads, highways, railroad, canals, and data on "restricted use" areas such as game preserves, cemeteries, and parks. Also required are the historical and projected load by class (Rate and Revenue) and load curve data (Load Research Department). 13 2-3-2 method (Canadian Electric Association, 1982) on a 40-acre grid basis using ten land-use classes in a multi-map format. Required data are land-use and zoning data on a small area basis, information on the locations of major roads, highways, railroad,


521

canals, and data on "restricted use" areas such as game preserves, cemeteries, and parks. Also required are the historical and projected load by class (Rate and Revenue) and load curve data (Load Research Department). 14 Combined land-use/multivariate method, an advanced form of EPRI Multivariate model (EPRI, 1979; Yau et al., 1990) operating on a 40acre grid with a multi-map format. A "Lowry" type urban model is used to provide "control" to the multivariate extrapolation procedure and an end-use model is added to anticipate changes in consumer usage patterns. This procedure is close to a 3-2-1 land-use based method of forecasting but substitutes multivariate regression in the place of pattern recognition. This program is a hybrid algorithm of an early type. Required data are land use and zoning and major roads, highways, railroad, canals, as well as data on "restricted use" areas such as game preserves, cemeteries, and parks, on a small area basis for two years, at least five years apart,. Also required are the historical and projected load by class (from Rate and Revenue Department) load curve data, market penetration, and technology trends by appliance subcategory (from Load Research Department). 15 3-3-3 method (Willis, Engel, and Buri, 1995) operating on a 40-acre grid basis using ten land-use classes in a multi-map format. Required data is land-use and zoning data on small area basis, information on the locations of major roads, highways, railroad, canals, and data on "restricted use" areas such as game preserves, cemeteries, and parks. Also required are the historical and projected load by class (from Rate and Revenue Department) load curve data, market penetration, and technology trends by appliance subcategory (from Load Research Department). 16 3-3-3 method (Willis, Engel, and Buri, 1995) operating on a 1/2 mile road-link small area basis using ten land-use classes in a multi-map format. (Also covered in Chapter 18.) Required data are base year land-use and zoning data on a small area basis, information on the locations of major roads, highways, railroad, canals, and data on "restricted use" areas such as game preserves, cemeteries, and parks. Also required are the historical and projected load by class (from Rate and Revenue Department) load curve data, market penetration, and technology trends by appliance subcategory (from Load Research Department). 17 3-3-3 method (Willis et al., 1995) operating on a 2.5-acre grid basis using twenty land-use classes in a monoclass map format.

522

Chapter 17 Required are base year land-use and zoning data on small area basis, information on the locations of major roads, highways, railroad, canals, and data on "restricted-use" areas such as game preserves, cemeteries, and parks. Also required is the historical and projected load by class (from Rate and Revenue Department) load curve data, market penetration, and technology trends by appliance sub-category (from Load Research Department).

18 Extended Template Matching as described in Chapter 15, applied on a !/2 mile basis. Test results given here reflect application to a typical urban problem area, not the developing nation area. Required are base year land-use and zoning data on small area basis, information on the locations of major roads, highways, railroad, canals, and data on "restricted-use" areas such as game preserves, cemeteries, and parks, and similar data for twenty years earlier. Also required is the historical and projected load by class (from Rate and Revenue Department) load curve data, market penetration, and historical peak feeder of TLM- area loads for the past 25 years. 19 A hybrid 3-3-1/trending program, SUSAN, described in Chapter 15. Required data are "approximate" base year land-use and information on major roads, highways, railroad, canals, as well as limits defined by "restricted use" areas. Also required is the historical load data by feeder for the past five years, and the corporate forecast. Testing of these methods was done by substituting in turn an analysis engine (set of program routines) for each of the nineteen methods into a common shell program which handled the data, controlled program flow, reported results, and computed error. All nineteen subroutine sets were professionally developed, used standardized program design criteria, and utilized the high-speed calculation methods discussed in Chapters 6 and 9, where appropriate. Comparison of Forecast Accuracy Table 17.4 gives the results and Figure 17.2 shows the ACap error costs for all nineteen as evaluated. Note that these numerical error measures bear out the behavior expected of AAV and similar error statistics as discussed in Chapter 5. AAV is not a good predictor of overall forecast value. In fact, in both cases, the worst AAV value is for method 17, which proves to have the least negative impact on planning. AAV is evaluated for any particular method at the method's small area resolution, which in method 17 is 2.5-acre grid (squares 1/16 mile across, much higher than any other method tested). The SFA-based error measures, Ux, generally bore a closer resemblance to actual impact. Despite the approximations in the test procedure discussed earlier, and the fact that only two utilities were used as test cases, the author believes these results are representative of the relative performance of the types of forecast


523

methods tested. The following conclusions can be generalized as applicable to most situations: • Data and set-up needs vary by nearly an order of magnitude. • AAV is a very poor measure of spatial forecast accuracy, as indicated by its measure versus Ux and ACap (see Table 15.4a). Methods 1 and 2, among the worst forecasting methods tested, have roughly the same AAV measure as methods 18 and 19, which actually produce less than one-fourth as much effective T&D error or impact as methods 1 or 2. • Land-use-based simulation methods were uniformly more accurate to trending when forecasting over a ten-year period. The more comprehensive methods were generally more accurate, but more costly to operate. • Trending methods and some hybrid methods (ETM) could not perform multiple scenario studies. • Most trending methods (methods 1, 2, 3, 6, 7, and 8) could not forecast growth in small areas that had no load prior to the base year. The VAI (4) and LTCCT (9) regression could to a limited extent. Methods that could not forecast vacant area growth tended to over-estimate growth everywhere else and underestimate it in vacant areas. The LTCCT method's accuracy advantage over other trending methods is its ability to forecast vacant area growth better than the others. • Multivariate methods (methods 7, 8, 14) did no better than comparable methods using other approaches. • Land-use techniques yielded results roughly proportional to the level of detail included in the spatial analysis used to compute preference functions. The more advanced land-use methods (methods 13, 15, 16, and 17) produced errors with an apparently random spatial distribution. • Hybrid methods (8, 14, 18 and 19) differ greatly in characteristics depending on how the algorithms were designed, but potentially offer advantages in areas where they were designed to excel, at possible additional cost of loss of flexibility in other areas. • There is a slight tendency of simulation to do worse on rural area forecasting if applied at less than very high spatial resolution (more on this in Chapter 18).

Chapter 17

524

Table 17.4a Forecast Accuracy Comparison Utility Test Case A in Percent Ux Method

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Type

Curve fit Trend Ratio shares Trend Template Trend VAI Trend TempI+VAI Trend Urban centr. Trend Cust. ratios Trend Multivariate Hybrid LTCCT Trend Manual land-use Jdgmnt 1-1-1 cust. class Simul. 2-2-1 cust. class Simul. 2-3-2 cust. class Simul. Multiv. cust. clss Hyrbid 3-3-3 multi-map Simul. 3-3-3 road link Simul. 3-3-3 monoclass Simul. Extended template Hybrid SUSAN Hybrid

AAV*

ut

Us

Us

50 51 47 38 36 61 52 44 36 56 39 39 38 45 27 32 79 52 49

22 23 15 14 12 18 14 11 10 17 14 9 7 10 7 7 6 8 7

35 37 25 22 20 27 22 20 18 30 28 14 14 15 11 13 10 13 12

51 47 38 36 46 52 30 26 41 38 27 22 28 15 18 13 19 14

ACap ALosses

50 41 32 28 26 34 28 24 24 32 22 17 14 19 12 15 10 12 11

39 21 19 14 15 23 20 16 14 20 15 13 9 10 7 9 6 8 7

Table 17.4b Forecast Accuracy Comparison Utility Test Case B - % Method

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

Type

Curve fit Trend Ratio Shares Trend Template Trend VAI Trend Templ+VAI Trend Urban centr. Trend Cust. ratios Trend Multivariate Trend LTCCT Trend Manual land-use Jdgmnt 1-1-1 cust. class Simul. 2-2-1 cust. class Simul. 2-3-2 cust. class Simul. Multiv. cust. clss Mixed 3-3-3 multi-map Simul. 3-3-3 road link Simul. 3-3-3 monoclass Simul. Extended template Hybrid SUSAN Hybrid

AAV*

u,

Us

us

45 47 47 34 32 62 43 36 28 40 35 31 29 39 23 30 84 52 49

20 21 20 11 11 17 16 13 12 12 14 10 7 10 9 6 6 8 7

35 37 25 24 26 39 24 27 19 18 28 21 14 15 13 10 10 13 12

50 51 47 38 36 56 43 32 30 28 40 33 27 33 22 20 20 19 14

ACap ALosses

35 38 29 26 26 50 28 34 23 19 22 18 17 22 17 12 12 12 12

23 21 20 16 15 20 22 16 16 24 17 14 9 9 8 7 8 8 7

525


Utility Test Case A - metropolitan electric system Curve fit

< 40 a. 5

I—| Multivariate

E 0

Codified curve fit

S 30

Manual .—|

LTCCT

• —-

Simulation s imple 4— algorithm —^ complex

Hybrid

— O U

1- 10

I

U)

I—I I

I I—

U UJ

te.

6 7

8 9 10 11 12 FORECAST METHOD

13 14

15

16

17

18

19

Utility Test Case B - rural electric system Curve fit § 40 5

Modified curve fi

w 0 30

flM jltivariate

| —|

Manual Simulation LTCCT simple^— algorithm —>• complex

*t

S

Hybrid

| —I

2

°

U U

I—I I—I I—I I—I

tn o UJ a: 6 7

8 9 10 11 12 FORECAST METHOD

13 14

15

16

17

18

19

Figure 17.2 Planning impact as measured in the comparison tests of nineteen forecast methods for utility test cases A and B. Differences in the relative performance of the methods between the two cases (method 2 was worst in case A, method 6 in case B. method 17 was best in case A, method 16 best in case B. Manual forecasting did much better in case B than in A) are due to differences in both relative performance in urban versus rural settings, and responsiveness to growth rate.

Chapter 17

526

Table 17.5 Data Requirements for Forecast Methods Type data Feeder peaks Subst. peaks X-Yofsubst. Lo-res land-use Land-use Hi-rs land-use Land-base Cust. peak k W Load curves End-use data Corp. frcst

Prep hrs. A B 1 thru 5

Method 6 7 8 9 10 1 12 13 14 15 16 17 18 19

24 20 24 20 16 24 60 80 1 6 02 4 0 60 60 40 40 8 8 24 24 60 60 24 24

X X X X X X X

X

X

X

X

X

X X

X

X X X

X

X

X X X X X X X X X X

X

X

X X X X X X X X

X

X

X

X X X

X

X

X

X

X

X

X

X X X

X

X

X

w 600 3 O

—

I 500

g 400 uj 300

1 —1

§200 H 100 < Q

n

™_rir,runn 1

2

3 4

fin

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 FORECAST METHOD

Figure 17.3 Data collection time for the nineteen methods, for cases A (shaded blocks) and B (unshaded).


527

• Methods which worked at a higher spatial resolution produced better forecast accuracy. This does not necessarily mean that greater resolution, per se, provides improved forecast accuracy (although occasionally that is the case with simulation). All nineteen methods were applied at the resolution for which they were designed. In general, methods designed to work at a higher spatial resolution include more comprehensive analysis of other factors besides resolution, and thus they forecast more accurately. • The techniques using clustering and pattern recognition, whether trending or simulation or hybrid, worked slightly better in the metropolitan test case than the rural case. Data and Resource Requirements Table 17.5 lists the data requirements of the various forecast methods, along with the preparation time for each source (Figure 17.3), under normal circumstances, for cases A and B. (Data sources and procedures to collect such data will be discussed in 17.4.) Data listed for the multivariate and simulation methods include the full set of data for which they were designed.4 Data required include: Historical peak load data. Methods 1, 2, 3, 4, 5, and 7, what might be termed "traditional" trending methods, require only peak load measurements on feeders for the past seven to twenty years, depending on method. Such data are certainly available at any utility that practices good record keeping, and take no real preparation time. In all cases these data are used as the basis for extrapolation of past trends as the forecast of future peak loads. In any practical situation, methods 13, 14, 15, 16, and 17 require historical peak load data on substations, used to "calibrate" the simulation base data (see Chapter 11, Step 4). Geographic location of substations. Methods 7, 9, 12, 13, 14, 15,16, and 17 require a geographic location for each substation, information used in quite different ways depending on whether the method is trending, multivariate, or simulation. Trending (7, 9) uses the locational information as the basis for comparison of trends among neighboring 4

Both multivariate and simulation methods are robust, to the point that many can produce quite reasonable results with incomplete data or with data that contains a significant amount of error. However, this test applied each method as designed for best performance, and thus lists all data required. (See Willis, Engel, and Buri, 1995).

528

Chapter 17 substations or feeders. Multivariate methods (and method 9, trending) use the data to estimate load density and attempt to use that information to improve the forecast. Simulation uses substation location to tie landuse data to historical trends for calibration purposes. Low-resolution land-use data. Methods 6, 10, and 11 work with low resolution (square mile or so) land-use data, and also require only basic land-use data at "Anderson level-1" definition.5 Land-use data. Methods 12, 13, 14, 15, and 16 use land-use data, usually on a grid basis at 40-acre resolution, and with considerably more than Anderson level 1 distinction of consumer land-use classes - nine classes is typical. This is used as the basis for the pattern recognition and the forecast itself. High resolution land-use data. Method 17 analyzes land-use data on a grid basis at 2.5-acre resolution, but as a mono-class simulation can accept land-use data on a polygon basis and convert it to grid basis without much approximation error. Like methods 12 through 16, this method uses land use as the basis for the pattern recognition and the forecast itself. Land-base data consisting of general map data, highways, railroads, and similar terrain data are required by methods 10 through 17. Peak per consumer or per acre load values, giving kW usage by future year per consumer unit of each land-use class are required by methods 10, 11, and 12. End-use load model database is required by methods 12 through 17. Methods 12 and 14 require peak and energy usage by class, methods 13, 15, 16, and 17 require load curves, hierarchical framework, and technology and market share trends by class, end use, and appliance subcategory. Consumer and energy usage trends from the corporate forecast are required by methods 10 through 17.

5

Two Anderson levels are distinguished in classification used by the United States Geologic Survey. Level 1 gives good distinction of forest cover, agriculture, etc. Anderson level 1 can be thought of as providing, at best, classification into the following categories: urban, suburban, rural occupied, agricultural, and other.


529

Table 17.6 lists the labor required to set up, run the forecast method, and interpret its results; the data collection labor from Table 17.5; and the computer resources required to support each of these methods as estimated by the author, on both of the cases. Labor includes the total time required to set up, produce, and interpret the forecast results for two forecasts (the author's experience is that forecasts are repeated or scenarios studied to the extent that a single forecast is rarely done). Computer resource requirements are the required RAM when running, total disk storage, and wall-clock minutes for an efficiently written program to complete the forecast running on an Intel Pentium 1000 Mhz processor. Using values of $65 per hour for forecast labor, $50 per hour for data gathering, $50 per Mbyte for RAM, $2 per Mybte of disk space, and assuming a base computer costs $2,000, an overall application cost of use can be developed. The cost computed in this manner for each forecast method is plotted against its accuracy in Figure 17.4. The author does not represent the costs computed as accurate estimates of overall cost in an actual utility application - many other

Table 17.6 Resource Requirements for Forecast Application Utility Test Cases

Method

1, 6 7 8 9 10 11 12 13 14 15 16 17 18 19

2, 3, 4, 5, or 7 Urban centr. Cust. ratios Multivariate LTCCT Manual land-use 1-1-1 cust. class 2-2-1 cust. class 2-3-2 cust. class Multiv. cust. clss 3-3-3 multi-map 3-3-3 road link 3-3-3 monoclass Extended template SUSAN

Type Trend Trend Trend Trend Trend Jdgmnt Simul. Simul. Simul. Mixed Simul. Simul. Simul. Hybrid Hybrid

Hours A B 60 68 72 320 144 480 240 120 200 480 240 300 280 550 71

80 160 60 320 120 240 240 200 280 240 240 240 240 470 65

Mbyte RAM Disk

Min. CPU

1 1 1

4 8 6 16 8

4 10 4 40 4

15 1

4 8 16 32 24 36 48 30 40

10 10 20 40 50 50 120 200 80

1 1 3 15 10 35 25 40 50

Chapter 17

530

2

0-3

0
3000

Dense development with a majority of multi-story structures. Local economy is driven by: regional metropolitan economy Transportation system: omnipresent road network, mass transit Examples: Inner 1/5 of radius of large metropolitan areas like Boston, Houston, Atlanta, exclusive of the downtown core.

Urban core > 10,000

Very dense high-rise development. Growth is three-dimensional. Land-use/consumer classes are mixed (residential, commercial, office in the same small areas, even when viewed at very high spatial resolution). Local economy is driven by: regional metropolitan economy Transportation system: omnipresent road network, mass transit Examples: Core of very large metropolitan areas like Boston, Houston, Atlanta, Chicago and San Francisco. Most of Manhattan.

Agrarian

Development Dimensionality: Urban, Rural and Agrarian Areas 18.2

571

REGIONAL TYPES AND DEVELOPMENT DIMENSION

Table 18.1 shows the spectrum of land-use densities and their major characteristics, from completely undeveloped to the densest urban cores. Although a major distinguishing feature among these categories is population density, there are other significant differences among them, one of those being electric load density. Most significant to the forecaster, however, growth and development act differently in each of these types of region - in the sense that in each type of region the growth and change are defined and limited by different forces and behave in different ways. An understanding of these regional development types, and "dimensionality" of development, can help planners and forecasters improve their ability to predict growth trends and anticipate how to plan for them. Proximity and Its Role One clear area of variation among the regional types shown in Table 18.1 is transportation infrastructure. Largely as a result of the population density variation, road network density also varies from less than .01 road miles per square mile in sparsely populated regions to more than 50 miles/mile2 in the heart of densely populated cities. In suburban, urban, and urban core areas, proximity to roads and to certain types of roads largely determines the value of individual land parcels and the purposes for which each is best suited. This more than any other single factor determines growth potential and the amount of electric load that will develop. The density of the road network in a region is a sort of "chicken or the egg" process: an area with high population density will have a dense road network, and vice versa. One grows as a result of the other, and the other grows as a result of the one. In fact, as most people and not just planners and forecasters understand, they grow up together, in a kind of mutually supporting relationship. Proximity - "being close to" - is important in the development of nearly all types of areas (residential, industrial, etc.) in all types of regions (rural, urban). But the "what" changes as one moves up the developmental spectrum. As one moves across the development spectrum, proximity or access to other locations becomes more and more dominant in determining growth of any one location. Each developed location in a sparsely populated region selected its specific site due to some unique quality of that site. It is the coldest place on earth, and thus a great place for scientific research. It is above a large underground reservoir of petroleum. Development in such areas is attracted to these unique characteristics of each site. They outweigh everything else with respect to the purpose of that site/facility including the isolation of the sites, the distances, and any difficulty that one may encounter in traveling to and from the site.

572

Chapter 18 Sparse

Agrarian

Rural

Suburban

Urban

Core

High

High

=3 f« If

ll *o £ 0 O U (A

c o> a *:

!i

5.2 E

None

None

.01

1 10 100 1000 Population Density - persons/mile2 (log scale)

10000

Figure 18.1 The "what" in [proximity to . . . ] changes as one moves across the development spectrum. Development in sparsely populated regions is due to unique characteristics of the site(s), but that in urban areas is proximity to other development.

As one moves across the developmental spectrum from sparse to urban core, the importance of the special or unique natural features of a site as a factor in its development decrease, and the importance of proximity or access to other development increases (Figure 18.1). The "community" at Prudhoe Bay is located where it is only because it is near a petroleum deposit. By contrast, development in Manhattan is driven by, and a function of, only proximity to other human development. The vast majority of people deciding to invest in a new skyscraper or to make their home in Manhattan do so because it is near to a lot of other development and because it has access to more goods, services, and infrastructure than just about anywhere else they could choose. They probably do not consider what special features the location itself has (a great harbor, good river access to inland areas) even though those are what initially caused Manhattan to become Manhattan. Rural and Agrarian Regions: A Split Personality to "Proximity" People living and using land in both rural and agrarian areas are driven there, because of some natural quality of the land (e.g., fertility for crops). That is what motivates them and is the purpose of their being there. Roads and access to other development play a role in development and site selection within these regions, but natural qualities are the major drivers. In rural areas, this property of the site(s) is sometimes nothing more than its isolation. Second homes, hunting cabins, etc., are often located where they are because those locations are not well settled. Many people who live in rural areas

Development Dimensionality: Urban, Rural and Agrarian Areas

573

but work in cities live where they do just because of the low development density. But usually, rural residential locations were also chosen for some other natural attribute — they are close to beautiful mountains, in a lush forest, or near a coast, or a lake, or have a particularly high wind speed, etc. In those rural areas in danger of transitioning to suburban development (i.e., those near a city), proximity to a city is a reason residents and businessmen in the area chose their location. In agrarian areas the land itself is the sought after quality. Land is this economic function's production line. Some land is much more valuable than other land ("bottom land" with rich soil generally being preferred) but mainly area - measured in square miles or square kilometers or acres - is the property being sought. Ranching can get so many head per mile, farming so many bushels per acre. But access to the regional road network, meager though it may be by comparison with urban systems is also important. Residential, commercial and industrial development within this region all want to be relatively close to this (sparse) road network because it is their primary means of transportation and access to goods and services. Commercial and industrial development will put what facilities it can relatively close to roads for a similar reason: it is efficient and lowers business cost. But relatively is an important word here: in a city, "close" might mean 150 feet. Here, it generally means within a mile. And in these areas, it is proximity to a road that is more important than distance to market and services. Traveling ten miles or thirty miles to town is quite secondary to the fact that one can go to town. To a farmer, having 1000 acres of prime land for raising grain is much more important than its proximity to shopping, services, and neighbors, or to a grain elevator for that matter. Access to a road means that they are close enough: quality of land is paramount. A good deal of the challenge in forecasting electric load growth in agrarian and rural areas revolves around modeling and balancing these two competing qualities as they affect development and electric demand. In suburban and urban areas, proximity to other development - distance to the activity centers, to local amenities - are key factors. In agrarian and rural areas, those are secondary. Access to a road is a key factor in the domain of "spatial interactions," while unique qualities of the land itself are the other key factor in determining siting for residents and businesses. Finally, rural areas near a city often create a big challenge to forecasters because they are in transition to suburban application. Overall, only a tiny fraction of all rural land falls into this category, but because of this transition, it becomes the focus of intense planning interest. It is the rural areas outside cities that make a transition to suburban development. Many of the residents in such areas work in the city or its suburbs but seek the isolation of rural areas. These particular rural areas are in transition to suburban - to a development dimension in which proximity to market, services, and employment other than in the agrarian sector is of primary importance.

574

Chapter 18

In fact, there is a progression in development from agrarian to rural to suburban that is often followed. Traditional farming or ranch land gradually is converted to higher population through spotty development of large-acre home sites and commercial development. Planned development and creation of roads then converts the area to more of a suburban context, and the area "develops." Dimensionality of Development and Growth One can view the dimensionality of spatial development and dispersion as increasing as one moves from sparse to urban core densities. This increase is important both as a concept of use to forecasters, and in choosing and setting up the right forecast tool for a region. It is summarized in Figure 18.2 Dimension refers to the context of, or the degrees of spatial freedom and interaction exhibited by, the land use and interactions in the region. In sparsely populated areas, dimensionality is very close to zero. Only a few specific, unrelated points are inhabited and used, their locations selected for scientific interest or natural resource extraction that are specific to each site, usually due to some geologic or ecological quality of the specific sites. Transportation to and from each site is done via air, sea, pipe, or if by road, over a purpose-built road run only to that site. Commerce (exchange of goods and services) is carried on with "the rest of the world" and not neighboring areas in the region. In practice, they are as far from any neighboring developments as from anywhere else. As a

Figure 18.2 Dimensionality of developmental growth roughly corresponds to the log of population density.


575

consequence their dimensionality is zero: distance and proximity have no meaning. Each is as close to areas nearby as to those far away.1 Rural and Agrarian Regions: One-Dimensional Development Rural and agrarian areas represent the least populated use of land in which interaction among people, economic functions, and regional transportation plays an important role. Their analysis and planning are complicated by the fact that transportation and proximity are of only limited importance, but often take not a back seat, but one alongside of, the qualities of the land itself. Here location relative to one another does play a role in determining the patterns of development, as in suburban and denser development, but qualities of the land are often the raison d'etre. And as stated earlier, in rural and agrarian areas, the proximity-related side of development generally means "I need to be close to the road." That is enough. Later in this chapter, quantitative evidence of this concept will be presented, but what this really means is that spatial interactions of development in these areas are one-dimensional. To every person or business in these areas, "the world" outside their site consists of one long road. There is the location ("my ranch" or "my farm" or "the mine" or "my summer cottage") and there is the road, which extends in one or perhaps two directions ("left and right when I get to the road") and might as well be a straight line even if it actually twists and turns. Everything is some distance away along this road. There is no concept of being in a twodimensional landscape, in the sense of being surrounded by development and having all the points of the compass from which to choose for movement, as there is in a city. All of that would be only interesting, were it not for the fact that growth in rural areas displays a one-dimensional aspect in both its dispersion and change, and its load densities, as will be discussed in section 18.3. Agrarian area growth (section 18.4) is more complex, being dominated by two dimensions (area) with respect to value of the land, but only one with respect to locational aspects of many of the loads. This, too, will be discussed later in this chapter. Towards Three Dimensions In suburban and urban areas — in villages and towns, and over the vast majority of most major cities - spatial growth and interaction become two-dimensional. Space is the prime quality of land valued in development, space differing from area by including the concept of location relative to other locations. Thus, the term space will be used here to mean space for development - the land area is 1

Normally, to the T&D planner, these areas do not exist. Electric load at each site is related purely to that site's mission, the burdens of that mission and the volume of work to be done, and the ambient conditions at the site. Electrical facilities are planned as part of the site (by the oil company or by the scientific research division). There is no utility system in the normal sense of the word and no need for T&D system planning.

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not valued solely for its ability to produce food, but for its accessibility and proximity to other development, too. Suburban and urban development is two-dimensional. People and businesses do not live on a road, they live in a road network, an urban fabric. Beyond that, completing the dimensionality of development, at the heart of a large megalopolis, up becomes a further direction of development. Space at the core of, and in other key places, in some cities has become so valuable that it is worth creating it at great cost through building higher. When does it fill up? The reader who finds the concept of dimensionality hard to grasp might wish to consider answering the question "When is the area all used up?" with respect to each of the six types of regions in Table 18.1. Both sparse and agrarian regions are "filled up" when the attribute which makes the land valuable is completely absorbed or utilized. In mining or petroleum extraction cases it is the size of the natural resource deposit. In farming and ranching it is when the land acreage is completely used for agrarian purposes. Rural areas fill up when all the accessible parcels of land near roads are "full." They can only grow when more roads are built (section 18.3 will explore this quantitatively). Suburban, urban and urban core areas are all created from rural by building more roads so the development can be increased. They are all the same fabric, so to speak, of metropolitan development. Each is "full" when all the small areas within it are developed with roads and buildings or land use to that type's density level. Once full, growth can continue only by transition to the higher density type - suburban to urban, and urban to urban core. Core development is limited by technology and the value of construction - buildings much over 100 stories are considered impractical, due to both cost and access time to the higher floors - beyond a certain point one cannot build space that is very close to everything else in the urban core, due to "vertical travel time." Transitions and Gray Areas Finally, it is worth considering that these definitions all have gray transition zones between them. Metropolitan growth includes the process of these areas changing from one type to another, and includes two redevelopment processes: 1. "Rural/agrarian land on the periphery is made "two-dimensional" via installation of enough roads/transit that land area is converted to space ~ all of it is accessible and developed with buildings. 2. Re-development of older suburban and urban land into higher density areas is done by tearing down the old and putting up higher density buildings, invariably closer together. The distinctions in dimensionality matter to the planner because they affect the way land use and load develop, and how one area interacts with another.


577

18.3 FORECASTING LOAD GROWTH IN RURAL REGIONS While the majority of land worldwide is "rural," in the sense that it is sparsely populated and not accessible through any type of dense road system, the majority of electric load is concentrated in densely populated urban and suburban regions. Therefore, quite naturally the bulk of research and development activity in spatial load forecasting has focused on predicting load growth in and around those areas, and particularly on forecasting the way in which new development springs up on the undeveloped periphery of those areas, as they expand. Rural and Agrarian Areas Rural and agrarian areas are similar in that both have relatively low population densities. However, there is a tremendous difference. In agrarian areas the land is completely (or mostly) used, but for a purpose that precludes dense human development: it is used to manufacture food. In rural areas, most of the land is not actually used and may not even exist from a practical standpoint. In the western U.S., there are vast tracts of land between the Rocky Mountains and the Sierras in which only a small portion of the land - less than 5% - is "space." In any practical sense the rest does not exist from the standpoint of the population's use of it for anything useful for anything other than isolation and view. Thus, this part of the world consists of only the land along and near roads and highways. The rest is just "there" but not relevant. Both rural and agrarian areas fit into a colloquial definition of "rural," but in this and subsequent chapters, these distinct definitions will be used for each. Forecasting Method Performance in Rural Areas Most spatial forecasting methods developed for urban and suburban load planning application do not forecast as well as when applied to rural and agrarian regions. To begin, they are far less easy-to-use and in some cases computationally burdensome - one of the few power system planning applications where computer time is still a major issue. More important, they are noticeably less accurate in these situations, as shown in Figure 18.3. This "malaise" with respect to forecasting performance does not affect just one type of forecasting approach, as shown. Both simulation and trending types show marked reduction in performance when applied to rural areas. An important clue to understanding the difference in accuracy between rural and urban applications is that the sensitivity depends on how heavily each method uses land and land use in its analysis. LTCTT trending uses land use in only the vaguest sense (it implicitly tracks area). It displays only a small performance difference between rural and suburban forecast situations. By contrast, a 3-3-3 simulation method, which uses land use extensively and in detail, shows a much larger difference. Therefore, it appears that something inherent in land use itself, or how it is modeled, is the cause of this poor performance.

Chapter 18

578

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Figure 18.9 Applied to the road link format, all forecast algorithms seem to forecast as accurately in rural situations as they do when applied to urban-suburban areas with an areabased format.

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Program and Algorithm Issues Spatial road links can be either uniformly sized (all links the same length) or of variable length. Variable length links, usually limited to a maximum dimension of '/2 mile or !/2 kilometer, have been used in all programs of this type the author has seen and reviewed.5 The length limit is defined by the spatial resolution requirements of the forecast simulation algorithm. The links need to be short enough to assure reasonable accuracy in computing the proximity and surround factors. Surround and proximity factors take on a meaning in the road link context similar to the role they play in a small area based framework. However, all have somewhat greater radii of application. As a rule, proximity factors in rural areas are similar in radius to their urban counterparts only near the outskirts of cities in really developed regions (Europe, any state along the U.S. Atlantic coast). In truly rural areas, they typically have a radius of about three to five times that of similar factors in urban and suburban settings. In really sparse areas far from cities (the U.S. West) they are more than ten times their value in suburban settings. "Close to shopping" in most areas of Wyoming means less than fifteen miles from the nearest commercial development. The best computational methods for surround and proximity factors in a road-link context do not mirror the way(s) used to compute those factors in areabased simulation formats. What will be fastest depends on how the actual data is structured and how the particular GIS system being used operates. However, in general little computational speed can be gained by using signal processing tricks analogous to those that work when programs are written in basic programming code and run on an area basis (see Chapter 14). It is best to study the specifications of the specific GIS spatial data system being used to determine how its features can be best used to improve speed and accuracy of analysis. And regardless, some true spatial analysis will remain to be done in a road link analysis. The reason is that both highways and railroads must be modeled and their proximity effects taken into account. Highways are not roads. No development occurs along a restricted access highway, except at its intersections with roads. (Development alongside an interstate highway is not built along the highway. It is built alongside the highway frontage road. Thus, highways, entered exactly as in area-based programs, are kept as a separate level. Railroads are even a bit more complicated to model in a road-link structure. The reason is that heavy industrial development will sometimes accept a rural site along a railroad, even if not near an existing road. A road will be built if needed by the industry ~ a small additional expense for an otherwise good site. 5 The author has seen only three programs developed for rural applications using the concept, including his own (Willis, Finley and Buri, 1995). However, all were designed independently (even if the other two were based on the 1995 paper) and in each case the designers reached the same conclusions about which format was best.


585

Highways and railroads can both be represented as separate sets (levels) of links in the simulation program, with the algorithm written to permit no load on highways, but permit heavy industrial development on railroads. It will still show a bias towards locations where railroads are close to the roads, but allow development that is not. But regardless, some amount of truly spatial analysis must be done with regard to both highways and railroads and their impact on road links and their development. A railroad will not have an impact only on road links it crosses. It will drive away residential development along nearby roads. Most GIS programs have proximity computations, which while not fast, are sufficient to compute proximity among road-like data sets. Since railroads and roads seldom change, this does not have to be computed in each iteration of the program, only for a few. Modeling Towns and Villages Few large regions are without several small cities or towns within them These can be modeled in rural study applications by using small road links to represent every major street in the town. The simulation forecast method will then forecast both rural and town load growth. In fact, the road-link method could be applied to model large suburban areas or even densely populated cities, in which case it would essentially be as accurate as an area-based format simulation. But in such situations, the number of road links required is much greater than the number of small areas required (roughly 500,000 would be required to model a city like Houston at a resolution equivalent to about 200,000 small areas). Finally, while the data requirements are not that much greater, the road-link approach cannot be applied in conjunction with various high-speed, multi-dimensional signal processing "tricks" (see Chapter 14) that accelerate computation and improve robustness. These are very useful, in fact necessary, for high-speed study of truly large metropolitan areas. The one weakness of the road-link method is that it cannot predict construction of new roads. It will not automatically add new road links into its database to model the expansion of the "local street system," as for example, the type of street expansion that occurs in major metropolitan areas when they grow along their peripheries. Area-based simulation programs do this (add streets as needed) implicitly. While they do not forecast the construction of major traffic corridors and highways, they do implicitly forecast the completion of local roads as growth occurs. The road-link method is best applied only to regions with no major cities and rather sparse population, as for example the western half of Kansas or parts of a region away from cities and towns. In general, it works well whenever the average distance between adjacent parallel roads is 1 mile or more. In such situations it gives measurably better accuracy than area-based (grid or polygon format) simulation while using far fewer computer resources than even a computationally optimal grid-based area method.

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Is Developing and Applying an Algorithm for Rural Study Worth the Effort? If a planner's need is only for an occasional study of a small- or medium-sized rural or agrarian area, then the answer to this question is definitely no. A standard simulation method, applied with a very high spatial resolution and with its surround factors set appropriately for rural/agrarian modeling, will do the job. Even though the resulting computations will require a lot of time (maybe having to run overnight on a desktop workstation), the overall procedure will be more satisfactory than maintaining a special forecasting program just for the occasional rural forecasting project. Using a special algorithm only makes sense when one of the following conditions is met: •

The service territory is all rural and agrarian. There are no cities or large towns involved.

•

The rural or agrarian area within the territory that must be studied is truly large - on the order of 30,000,000 acres or more (a region larger than 100 by 100 miles).

Even this recommendation is subject to an additional consideration. The quality and speed of the computer code can have a lot to do with the computation time needed for large problems. Planners who use programs written in high-level GIS system languages may find the area-based simulation computing time for even 10,000,000-acre rural areas to be untenable. Programs written in such highlevel languages have many advantages (including low development cost and good program stability). However, they carry with that a rather high computational "overhead" compared to programs written in more fundamental computing languages. More important, none of the high-level GIS languages the author has seen can apply the multi-dimensional signal processing "tricks" (see Chapter 14) that accelerate high-resolution spatial computations. On a large region studied at very high resolution, frequency domain techniques solve in as little as 1/10th the time of space-domain based methods. "Overhead" of some GIS languages is more than three to one, meaning that the overall ratio of GIS to a fast, purpose-written spatial load forecast program can be in the range of twenty-to-one. Thus, users will need to judge the speed of their standard software against their needs in making this determination. 18.4 FORECASTING LOAD GROWTH IN AGRARIAN REGIONS Agrarian regions are those in which the land is in full use, but population density is low, because the majority of that land is being used for agricultural purposes. This type of region does not share the parcel-level non-linearity of land area and load (or population). Here, an area ten times the size of another will have roughly ten times the electric load, other things being equal.


587

Most of the electric loads will be located near roads, for a variety of reasons. Chief among these is convenience for the local inhabitants: locating their homes and work facilities near roads guarantees them easy access to their property and from it to everything else. Furthermore, regardless of where loads are actually located, the utility's obligation to provide service might be restricted to within a certain distance (500 feet, or 1/4 mile, etc.) of a road. This means that from its perspective, all loads lie along the roads anyway: Farmer Brown's house might be five miles off the road, but the last 4% miles of delivery are his responsibility. The utility has only to build one-quarter mile of line. But despite this similarity to rural areas with respect to load location relative to roads, the load growth and development behaviors in agrarian regions are different. Overall, load is roughly proportional to land area. Land area is the relevant factor in the activity that defines land use - agriculture. Twice as much land means twice as much activity means something like twice as much load. Figure 18.10 illustrates this point, showing the composite load curves for three parcels of land all devoted to dairy production. The loads are composite in the sense that two of the parcels had their load metered at several locations. Those were added together to obtain the curves shown here. Figure 18.4 also shows that agrarian loads do not follow the square root type load versus land-use relationship of rural areas although their density does drop more than suburban loads as large parcel size is reached.

Figure 18.10 Dairy farms of 160, 330, and 597 acres have daily energy uses of 356, 834, and 1575 kWh (2.25, 2.6, and 2.5 kWh/acre) respectively. Peak hourly demands are 16, 34, and 60 kW (.100, .106, and .101 kW/acre) respectively.

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Analysis and Forecasting of Agrarian Area Growth The road-link method described in section 18.3 is not a recommended approach. Instead, a standard simulation method is most applicable. If necessary in order to achieve acceptable run time, a low spatial resolution can be used. The load can be computed to a Vz mile or 1 km resolution. Even 4-mile resolution works in many cases. This brings up an important point about forecasting in rural and agrarian areas. It can be used in rural forecasts to improve accuracy, where it is a way of "tricking" a standard simulation algorithm into seeing what at lower resolution is a square-root relationship between land area and load. But such high resolution per se is not needed for planning purposes. Rural or agrarian planning needs are usually satisfied by a '/2 mile or kilometer resolution. Proper Classes and End-Use Model The simulation must distinguish the type of land use in a way suitable for agrarian applications. Dairy farms have far different needs than poultry farms, or grain cultivation, or cattle ranches. Appropriate classes need to be used and an end-use model set up to represent the various energy consumption categories in each (Table 18.2 and Figure 18.11). Publications for the American Society of Agricultural Engineers provide useful information on such loads. Seasonal Load Curves Generally, agricultural loads are very seasonal. Not all types of farms and ranches in an area will have the same peak season, as shown in Figure 18.12. Due to this diversity of seasonal peaks, it may be necessary to study load curves for several times of the year, or to use one daily load curve for each month.

Table 18.2

Loads for a 145,000 Bird Poultry Egg Production Farm

Type of Load Ventilation & Curtain Air Systems Curtain Air Handlers Hen House Lights Feed Bin and Conveyance Manure Removers and Processors Egg Collecting & Sorting Cold Storage & Fans Feed Grain Processing Grain Storage & Ventilation Misc. House Loads Road & Yard Lights Totals

Installed 40.9 0.9 9.6 35.2 18.4 12.8 4.8 11.2 4.8 4.0

Contribution to Peak

22.5 0.7 5.5 19.6 10.9 6.5 3.3 5.5 3.3 2.7

12

02

144.9

80.0

Development Dimensionality: Urban, Rural and Agrarian Areas 589

Figure 18.11 Composite farm-wide load curve for the poultry farm from Table 18.2.

So 0.6

1

3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51

Week of the Year — — Dairy Farm with feed

Pork stockyards & feed

Hybrid summer wheat

Figure 18.12. Agricultural loads are typically very seasonal, so analysis and forecasting of an area may have to look at several months of the year.

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18.5 SUMMARY AND GUIDELINES Man's use of land covers a spectrum of developmental types from the exploitation of natural resources and special features at selected sites (sparse use) to high-rise development limited only by financial and architectural limits (urban core). Rural and agrarian uses both leave the land basically undisturbed with respect to buildings, paving, and other infrastructure that accompanies metropolitan land usage. But they both make use of the land, or at least selected portions of the land in the case of rural usage Rural and agrarian load growth behavior differs from that found in metropolitan areas. As a result, rural and agrarian load forecasting is best done with approaches slightly different than those that work well in metropolitan areas. With respect to rural areas, growth is "one-dimensional" - only a portion of the land actually exists for any practical purpose. In agrarian applications, use of the land is two-dimensional, but a lot of the considerations and use of land for non-agrarian purposes are still one-dimensional. Generally, spatial forecast methods cannot deal with these areas accurately unless they are adjusted to account for a number of factors unique to these areas. •

Spatial resolution must be high unless the forecast algorithm is modified to handle the dimensionality of the load growth behavior. If that is the case, then a relatively low spatial resolution - perhaps one mile - can be used.

•

Consumer and end-use classes need to be carefully set up to reflect agrarian and rural distinctions.

•

The influence of urban poles should be minimized. These aren't urban areas. If modeled, their radius has to be very high - "near" can mean 40 miles and "close enough" can mean 100 miles.

• Proximity factors in general have wider radii - about three times what is seen in metropolitan areas. Reference H. L. Willis, L. A. Finley, and M. J. Buri, "Forecasting Electric Demand of Distribution System Planning in Rural and Sparsely Populated Regions", IEEE Transactions on Power Systems, November 1996, p. 2008.

19 Metropolitan Growth and Urban Redevelopment 19.1 INTRODUCTION In most metropolitan areas, a portion of load growth occurs due to redevelopment old buildings are torn down and replaced by newer, taller, and denser construction, generally with a great deal more demand for electric power. Or, existing buildings might be gutted and rebuilt within their old shells, either upgraded within the same land use (older homes rebuilt as more modern homes) or perhaps changed (warehouses converted to retail on the ground level with residential lofts above). Regardless, this redevelopment causes electric load growth in areas of the system that had existing and stable load levels. Such redevelopment can catch planners by surprise. In some cases, once it has begun it is then over-forecast, resulting in expenses out of proportion to what could have been spent. Redevelopment can be difficult to forecast. It occurs in areas where there is already a power delivery system in place, meaning that the required additions are expensive and have a long lead time. These areas usually have urban congestion and a resident population that limits the utility's options and makes construction difficult and expensive. This chapter will look at the characteristics of redevelopment growth and ways to address it in spatial forecasts. Section 19.2 will look at how important redevelopment is to planners. Section 19.3 will then examine the various ways it can be accommodated, partially or fully, in spatial forecast methods, and the results these approaches give. Section 19.4 gives application advice, and section 19.5 concludes with a summary and guidelines.

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19.2 REDEVELOPMENT IS THE PROCESS OF URBAN GROWTH The process of metropolitan growth can be viewed as composed of several stages - including initially rural or agrarian, and then a transition to suburban, then to urban, and finally to urban-core development as the city continues to grow. Table 18.1 (see Chapter 18, section 2) summarized these developmental categories and their growth interaction dimensions. The series of transitions from rural to suburban to urban to urban core takes many years to complete, each generation (i.e., stable development plateau in one particular category) typically lasting on the order of forty years or more. For many parcels of land and in many areas of a city, the entire series is never completed. Urban core is not the manifest destiny of every acre of every metropolitan region. But whatever an area's eventual maximum density, it "gets there" through a process of growth that can be viewed as moving through several transitions, each of which corresponds to a "growth ramp" period in the S-curve growth behavior discussed in Chapter 7. Figure 19.1 illustrates the concept: an area of the system will undergo several transformations as it makes its way from undeveloped rural land to urban core. Electric load grows most rapidly during those multi-year periods of each transition, and grows quite slowly in between. Figure 19.2 shows the peak load history in an area that actually experienced three of these transitions in only a 60-year period.

Time

Figure 19.1 Theoretical multi-transition "S" curve (Chapter 7) of a small-area's growth history, viewed over a very long period, could contain as many as four transitions.

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Figure 19.2 Annual peak load history in a section (640 acres) NNW of the intersection of US 59 and Loop 1-610 in Houston, estimated from metered feeder loads, aerial and satellite imagery, and building counts. Trends include the effects of weather variation from year to year. The area experienced a post WWII building boom of suburban housing from 1947 through 1954 that made a twelve-fold increase in the previous largely rural load density. The area again saw rapid redevelopment into offices and mid-rise apartments in the late 1960s and early 1970s. Construction of office and condominium towers began about 24 years later. This area's history is like that of many areas in other cities (and other parts of Houston) and is rare only in the relatively short periods, about 25 years each, between growth transitions, a function of the robust Houston economy during the last half of the 20th century.

Figure 19.3 Conversion of rural/agrarian land to suburban is accomplished with the construction of roads that make all of the land accessible. Compare to Figure 19.6.

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In each of these transitions, land in a metropolitan area is "re-built" into a denser use of land, usually via construction of taller and more closely spaced buildings. This process starts with the "accessification" of rural land - the construction of roads throughout a rural parcel so that all of the land is accessible and thus available for development (Figure 19.3). This converts land to the two-dimensional context covered in Chapter 18, section 2. Planners and forecasters should understand that this series of transitions is the process of metropolitan growth, a process that means the load history of any metropolitan or urban regional core is always a "multiple bend S curve." Basically, the process is: • Metropolitan area growth begins with conversion of rural and agrarian land on the outskirts of the city into suburban development, including the extension of a road network into the area. The area becomes twodimensional, a part of a city. • Areas with the resulting suburban fabric, which will develop to urban or urban core levels, do so over time by redeveloping to that level. (Direct rural-to-urban development occurs only very rarely). • High-rise (urban core type) development usually occurs as a second stage of redevelopment from the once-redeveloped urban area. However, sometimes it can occur directly as redevelopment from suburban. This process and its transitions can be interpreted in three ways as shown in Figure 19.4. All three are valid representations which provide insight useful to planners.

Developmental •Dimensionality Perspective Land-Use Change^ Forecasting Approach

Rural

Suburban

Vacant )useable land

Residential, retail, light industry

"S" Curve Load \ Litt(e , History Behavior \ or no Perspective /load

Growth Ramp

Low growth period

Urban

Urban Core

Apartments, offices, • industry

Growth Ramp

Low growth period

High rise residential or commercial

Stable but Growth \ medium-rate Ramp / growth

Figure 19.4 Comparison of three views on land development type transitions.

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Four Types of Redevelopment One important distinction about types of redevelopment needs to be recognized early in any forecast of redevelopment or analysis of its causes and impacts. Redevelopment can be categorized into four types, which are outlined in Table 19.1. Urban core growth occurs in the downtown, usually high-rise portion of a city, as a continuous process linked to and driven by, the growth of the entire regional economy. A city the size of Chicago needs more urban core (activity center size) than a city the size of Syracuse. As a city grows, its activity centers, including but not exclusively limited to the central core, grow in company with the rest of the metropolitan fabric into which they are woven through their economic and transport interaction. Urban cores grow up as well as outward. Redevelopment through replacement of existing structures is a constant and gradual process. Over the long-term, in the center of any large metropolitan area, there is a constant process of replacement of older high-rise (perhaps 25 stories) with newer high rise (perhaps 40 or even 80 stories). In addition, the land areas covered by high-rise core development tends to gradually expand, usually not in a symmetrical pattern but in only one or two directions, occasionally jumping to other locations nearby. Strategic redevelopment areas involve the promoted (driven by government or major financier sponsorship) redevelopment of an entire part of a city or county and usually include the coordinated planning of the future layout and content of an area of from several blocks to several hundred acres. Examples from the last two decades of the 20th century include the redevelopment of the Inner Harbor area in Baltimore, the redevelopment of the area around the new baseball stadiums in both Baltimore and Denver, the Greenway Plaza/Summit area in Houston, and the Arena area on the edge of downtown Dallas. In all these cases, an external coordinating entity, either the local government, a quasigovernment coordinating committee, or a developer with deep financial and political pockets, made a master plan for the long-term redevelopment of a significant area. A common theme among all such strategic redevelopment initiatives is that the plan does not fit the existing urban pattern. The initiative is being taken in order to change that pattern. The entire character of downtown Baltimore, even to the extent of where the heart of the city "seemed to be" from the standpoint of activity and attraction, changed with the development of the Inner Harbor. Similarly, an entire side of downtown Denver changed dramatically in the years following the construction of the new stadium and the other changes made in company with it.

Chapter 19

596 Table 19.1 Types of Redevelopment Occurring in a Metropolitan Area Type Urban core growth

Strategic initiatives

Localized (tactical)

Nature

Predictability

Continuous growth of the downtown core and other major activity centers for the region. Growth is linked to the economy of the region Large, visionary projects whose effect, and often goal, will be to change the entire character of the urban areas around them. Almost always sponsored by or supported by local government.

Good, in principle. Involves a type of "reverse urban pole" analysis of forces driving the demand for urban activity centers. Nearly zero. These do not fit the existing pattern so they cannot be inferred from existing needs and uses. The political process that gives birth to them is unpredictable. Must be studied as scenarios. Fair. These transitions can be inferred from need or opportunistic fit by looking at the existing land use and the area around the redeveloping site(s). Good. Successful match to existing land-use class is easy to infer in simulation.

Small, isolated redevelopment projects generally done purely as business transactions. The new land use fits within the existing urban fabric. The old often did not, or was only marginal. Redevelopment Redevelopment to the same type of in kind land use as in the past. Rebuilding to increase success.

Redevelopment of this type is very likely to make major changes in land use - it is extremely unlikely that such master plans involve only upgrades or minor changes in land use. Often, the motivation behind these plans is a political desire on the part of community leaders to completely change the character of their area of the city. In fact, usually, the sponsoring agency of the redevelopment initiative wants the impact of the redevelopment project's influence on the region more than it wants the project itself. It is a strategic project, planned to shape the course of the entire metropolitan areas' future growth character. Strategic redevelopment is often impossible to forecast, because it is usually politically motivated, and if not, impossible without support of the political process. And, as mentioned above, the plan for the targeted area does not fit within the present urban fabric - such plans are nearly all visionary - based on seeing beyond what is there now to what could be. Localized redevelopment occurs on a much smaller scale. A small, singlestory strip retail center might be torn down and replaced with a two- or three-story retail/office/residential center. An old rail warehouse might be

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gutted and converted to loft condominiums with restaurants on the ground floor. Or that same warehouse might be converted to a high-tech data or telecom center. A block of row houses built in the 1930s might be replaced by a row of townhouses both taller (more stories) and with much higher load density. A good deal of this type of redevelopment is always occurring, although it is usually limited to a few areas of a city. Localized redevelopment occurs as pockets of change, individually planned and initiated, usually by small developers or the existing land owners themselves. Usually it is not part of an overall master plan. This type of redevelopment often replaces an older, worn-out subclass of a land use with a newer version of itself, or a close proxy. Major changes are rare. For every warehouse converted to loft apartments, there are four to five times as many residential upgrades. Redevelopment in kind involves rebuilding or refurbishing within the same land-use class. Older homes are torn down and new homes are constructed in their place. Old shopping is demolished to make way for new retail facilities. Often the old is not completely removed, but merely refurbished and rebuilt: the new mall is where the old was, and includes 80% of its original steel structure, but looks and feels completely new. This type of change can be viewed as a subset of localized redevelopment. But redevelopment of an area into a new version of its past land-use class requires a special focus when a land-use based simulation approach is being used for the spatial forecast. That is one reason why it is treated as a separate category. But in addition, and more important, in a very real way redevelopment in kind is different from the three types of redevelopment covered above. Its motivation and goals are different. Usually it is being done because the buildings in an area are worn out or obsolete, not because someone wants to change the area's function or purpose. In this regard it is the opposite of strategic redevelopment. The current land use is very successful and no change is desired. Only an upgrade in performance or competitiveness (in the author's view these are largely the same) of that land use is being sought. These four mechanisms for redevelopment growth occur simultaneously and interact with one another. All must be accommodated somehow in any successful forecasting process. Generally, urban core, tactical, and in-kind redevelopment can all be trended to some extent, although perhaps not with just simple extrapolation methods. Strategic redevelopment, however, requires analysis as scenarios, both due to its nature, and because of how planners most often have to treat it.

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Redevelopment: How Much and What Is It Really Like? "Redevelopment" means that some existing land use is converted into another land-use. Strictly speaking, vacant is a land-use class. On this basis, one can argue that all construction on vacant (rural, agrarian) land is "redevelopment." This is within the spirit of section 18.2's and this chapter's perspective and actually a good concept to keep in mind when designing a simulation method to handle all types of load growth. However, to many people, and certainly to most T&D and urban planners, "land use" generally means only those uses that involve the construction of permanent buildings or fashioning of the land for a specific purpose (an aircraft runway, a park, a golf course). Table 19.2 lists the ten electric consumer (land-use) classes from Chapter 11's detailed simulation example. These are the same classes as listed in Table 11.1, except that two classes of vacant have been added for completeness, and the classes have been numbered 0 - 10. In principle, all possible transitions from one class in this list to another are possible. Vacant develops to any land-use type. Old homes are demolished to make way for new homes, or apartments, or retail, or offices, or industry. Old industry is removed to make way for — anything. (Even transformation of developed land back to vacant is possible and has occurred in some communities, generally when old industrial sites are transformed back to vacant useable land prior to sale for some new purpose.)

Table 19.2 Example Land-Use or Electric-Consumer Types Number

Class

N

Vacant unuseable

0 1

Vacant useable Residential 1 Residential 2 Aprtmnts/twnh ses Retail commercial

2 3 4

Definition land not available for whatever reason land without buildings, available for building homes on large lots, farmhouses single family homes (subdivisions) apartments, duplexes, row houses stores, shopping malls professional buildings tall buildings

7

Offices High-rise Industry

8

Warehouses

small shops, fabricating plants, etc. warehouses

9

Heavy industry

primary or transmission consumers

10

Municipal/Inst.

city hall, schools, police, churches

5 6

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Table 19.3 Land-Use Change for Three Metropolitan Areas 1990 - 1998, by Class Growth of N. This Class \ 1 Residential 1 2 3 4 5 6 7 8 9 10

Residential 2 Aprt/twnh Retail comm. Offices High-rise Industry Warehouses Heavy industry Municipal/Inst.

0

1

99 93 82 71 62

2 7 8 7

75 90 89 48

Came from land that had been previously classified as . . . . 2 3 4 5 6 7 8 9 1 0 S u m

1 1 2 2 2

2 3 1 10

4 2 8 11 3

6 11 15 2

6 47

3

1

3

12

2 2 3 2 9 9 4 8 3

2 2 8 2 6 4 3 2 12

1

5 20

100 100 100 100 100 100 100 100 100 100

Table 19.3 is the first of four tables that explores the actual growth of major metropolitan areas with respect to redevelopment. These tables look at the actual land-use and load change occurring in three cities over the period 1990 - 1998. The cities were Houston, St. Louis, and Chicago. Although limited in scope, it gives some idea of how much of a limitation, if any, the classic simulation methodology's modeling of all growth as only coming in vacant areas puts on forecasting accuracy.1 Each row in Table 19.3 shows from what previous class the land for growth of a certain land-use class was obtained. The measures are area, not load density, and are given in percent of each class's total growth - each row sums to 100%. Thus, the second row shows that 93% of all new residential 2 land-use (new single-family homes on small lots) came from development of vacant land. It also shows that 1% of the land for new construction of this class came from existing homes of this class which were torn down to make way for new, and that 2% came from each of the industrial and warehouse classes. As another example, the intersection of row 6 with column 5 indicates that 47% of all land that developed into high-rise during this decade had previously been class 5 (nonhigh-rise) offices, prior to that redevelopment. This matrix says a good deal about the actual process of urban redevelopment. First, only two classes - municipal/institutional and high rise get the majority of their growth from existing development. For the rest, vacant land (class 0) is the fodder for metropolitan expansion. But in every class except residential 1 (single-family homes on very large lots) redevelopment of existing land use has a noticeable role. For apartments, offices, high rise and industry, redevelopment provides more than one-sixth of all land used for growth. 1

This data was developed from public domain satellite images, growth records, population, and load growth data by the author for randomly selected areas of all three cities.

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Table 19.4 Land-Use Change in Three Metropolitan Areas 1990 - 1998, In Total - % Growth of N. This Class . . >\ 1 2 3 4 5 6 7 8 9 10

Residential 1 Residential 2 Aprt/twnh Retail comm. Offices High-rise Industry Warehouses Heavy industry Municipal/Inst. Column totals

0 55.60 26.24 4.76 2.77 0.93 0.00 1.61 1.00 0.16 0.18 93.3

. . . came from land that had been previously classified as . . . . 1 2 3 4 5 6 7 8 9 1 0S u m 0.56 0.40 0.41 0.31 0.10

0.45 0.12 0.08 0.03

0.04 0.03 1.8

0.04 0.75

0.23 0.08 0.12

0.09 0.41

0.06

0.23 0.16 0.09 0.04

0.00 0.01 0.5

0.5

0.5

0.04

0.0

0.56 0.12 0.12 0.03 0.02 0.19 0.04 0.01 0.01 1.1

0.56 0.12 0.31 0.03 0.04 0.09 0.03 0.04 1.2

0.06

0.11

0.2

56 28 6 4 1 1 2 1 0 0.07 0 0.1 100

Table 19.5 Electric Load Changes, 1 990 - 1 998, Weighted by Peak Electric Demand Growth of N. This Class \ 1 2 3 4 5 6 7 8 9 10


0

1

15.36 32.79 7.74 5.38 5.20 0.00 2.96 0.82 0.92 3.82 750

0.16 0.56 0.66 0.61 0.59

Came from land that had been previously classified as . . . . 2 3 4 5 6 7 8 9 1 0S u m 0.50 0.19 0.15 0.17

0.08 0.03 26

0.79 18

0.38 0.15 0.67 0.00 0.12

0.45 0.92 0.50 1.55 7.03 0.08

0.24 16

30

75

0.71 0.19 0.23 0.17 0.72 0.41 0.35 0.04 0.08 0.24 0.7 24

0.71 0.19 0.61 0.17 0.62 0.16 0.03 0.95 34

0.09

0.20

03

16 35 9 8 8 10 4 1 1 1.59 8 16 100

Table 19.6 Error in Estimating Load of New Using Old Class Data - % of All Growth Growth o f ^ v This Class \ 1 2 3 4 5 6 7 8 9 10


0

1

00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.9 0.6 0.6 0.6 0.0 0.1 0.0 0.0 0.0 2.9

Came from land that had been previously classified as . . . . 2 3 4 5 6 7 8 9 1 0S u m 0.0 0.0 0.0 0.1 0.1 0.0 0.0 0.0 0.0 0.8 1.1

0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.3 0.8

0.0 0.0 0.0 0.0 0.7 1.6 0.0 0.0 0.0 0.0 2.2

0.0 0.0 0.0 0.0 0.0 5.4 0.0 0.0 0.0 0.0 5.4

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 -0.4 0.0 0.0 0.1 0.4 0.0 -0.1 0.1 0.2 0.4

0.0 0.3 0.1 0.4 0.2 0.7 0.1 0.0 0.0 1.0 2.7

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.8 0.7 1.1 2.3 8.0 0.2 0.0 0.1 2.4 15.6

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Table 19.4 looks at this data another way, normalizing the table so the entire table, rather than each row, sums to 100%. In Table 19.4, each row summed to 100% of the growth occurred in that row's class. But some classes grew a good deal, others by only a small amount. Table 19.4 shows the change in each class as a percent of total change in land area. The final column shows the row sum, the total change of each class as a percent of overall expansion. Thus, 56% of all land-use change was residential 1, while only 6% was high-density residential (apartments and townhouses). The column total for column 1 shows that over 93% of all land-use change occurred as transitions from vacant (rural, agrarian) land to some suburban or urban purpose. Thus, redevelopment constitutes only about 7% of land-use change. But T&D planners are more concerned with electric load, not land area or land use change per se. Table 19.5 shows this data weighted by peak electric demand level in each class. Thus, while residential 1 as a whole had 56% of all land-use change, due to its relatively low load per acre, it constitutes only 16% of all peak load growth. High-rise buildings, which constitute only 1% of land use change in total (Table 19.4) account for 10% of all load growth due to their load density. Overall, only 75% of load growth occurs in previously vacant areas. Redevelopment in some form is the mechanism of 25% of the new loads. Thus, redevelopment appears to be substantial enough that T&D planners cannot ignore it. However, the data in Tables 19.3 - 19.5 can be used to provide an even better measure of the magnitude of redevelopment's role from the standpoint of forecasting and planning T&D load. Table 19.6 shows the error that results from failing to account for all suburban and urban redevelopment. If offices at 69 kW/acre replace apartments at 18 kW/acre, then missing that redevelopment in the forecast means an error of 51 kW/acre. Table 19.6 shows these errors as a portion of all load growth. Total error due to ignoring redevelopment comes to a little less than one-sixth of all growth. How Serious Is the Potential Forecast Error? Assuming that the results developed above are general (they strike the author as reasonably representative of the average T&D forecast situation), does this mean that ignoring redevelopment leads to 16% error in a spatial forecast? No. The spatial error - what impacts T&D planning — can be anywhere between two times to only one-half of that value, depending on exactly what "ignoring redevelopment" means. This will be discussed in Section 19.3, under that heading. T&D planners need to recall that a spatial forecast has a where element not included in Tables 19.3 — 19.6's analysis, and spatial error sensitivities and interactions that need to be taken into account (see Chapter 8). However, planners should consider this: the best performance possible from simulation over a ten-year period is about Uf = 8 to 10%. Thus, ignoring redevelopment can easily double the inaccuracy in meeting requirements for a metropolitan forecast.

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What Must Be Done Well to Model Redevelopment Accurately? Before looking at redevelopment and how it is handled with various analytical approaches, it is worth considering what must be done by the forecaster who has decided to address it in some fashion. There are five issues: 1) Model the economic demand issue. Is there demand for growth here? How preferable would growth here be compared to growth in the nearest vacant areas? Generally, simulation methods have tools and processes that deal with this (global model, urban poles and activity center model), but these may need to be modified for application to redevelopment. 2) Model the supply issue. What already-developed areas are available for redevelopment? What are they suitable to become? Simulation methods use preference functions to consider these questions, and those need to be modified or extended to be able to handle redevelopment. Trending must use some sort of pattern recognition. The biggest issue: How is this already-developed land validly compared to vacant land which has no existing buildings or foundations or paving to inhibit/enable it for new construction? 3) Accounting for displaced growth. What happens to the old land use that disappeared to make way for the new? Was that land use needed overall - in other words does it move somewhere else? Or was it redundant and thus there is no need to model it being built elsewhere. This is mostly a matter of accounting for land-use change and comparing it to global totals, but it must be done. Note in Chapter 11 's example, page 359, that forecasted redevelopment growth displaces 19 acres of residential which is modeled as moving to the outskirts of the town. 4) Will redevelopment actually occur? How does one actually match demand, supply and displacement to determine which areas redevelop and which do not? Again, simulation models match demand and supply: it is constrained and controlled by their global totals. But this model must be modified to accommodate redevelopment: a metropolitan area with a 1% net growth could see 1.5% land-use change due to redevelopment and displacement. 5) Redevelopment within the same class. "Gentrification" or "yuppyfication" - whatever it is called - occurs constantly. Older homes are gutted and rebuilt, or replaced with new construction. Redevelopment in kind changes the load, but not the land-use class. Any method of addressing redevelopment has a potential need to address all five of these concerns, although some forecasting situations may not require all five to be addressed well. Regardless, the thoroughness with which a method addresses these five in a coordinated manner determines how effective and accurate it is in representing redevelopment growth of new load.

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19.3 REPRESENTING REDEVELOPMENT IN SPATIAL FORECASTS Redevelopment challenges both trending and simulation forecast algorithms, for a variety of reasons but ultimately with a different effect on the two types of method. Each of the two forecasting contexts can address in different ways and with varying levels of success, but they do so in different ways and present the forecaster with different challenges in doing so. In general, trending methods are less accurate at modeling redevelopment than simulation methods, but some simulation methods do not deal well with redevelopment either. Trending Algorithm Interactions Chapter 9 examined the reasons why load history extrapolation methods (trending) are relatively inaccurate when dealing with "S curve" load behavior. Functionally, the reason is that the growth ramp of an "S" departs from the recent historical load trend, so an extrapolation will not forecast it well. But more fundamentally, there is a basic incompatibility in the application of any trending method to areas that might redevelop. By its very nature, trending assumes that whatever process has been in control of the trend will continue. Implicit in its application is that the current growth drivers will remain relatively constant, even if the trended quantity does not. Growth transitions violate this assumption: something in the nature of what is driving change also changes, in this case the type and context of urban purpose in an area. For these reasons, curve-iit/extrapolation types of load forecasting are particularly ill-suited to situations where a planner knows or suspects there may be redevelopment. Simply put, trending is not much help here. Even quasigeometric (e.g., LTCCT and similar methods) trending approaches do not deal well with redevelopment. LTCCT is relatively successful by comparison to other trending algorithms in handling the initial "redevelopment" from rural or agrarian land to suburban load levels. But that success generally does not extend to an ability to forecast subsequent transitions to urban and urban core.

Table 19.7 Algorithm Redevelopment Performance (10 = Perfect, 0 = incapable) Forecast Type of Redevelopment Algorithm Core Strategic Tactical In Kind Basic Trending (regression) 6 0 3 8 Advanced Trending (LTCCT) 6 5 0 8 4 4 4 Basic Simulation (2-2-2) 8 Advanced Simul. (4-3-4) 8 8 8 8 8 Hybrid Methods 8 8 8

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The advanced template-matching algorithm (Chapter 15) can offer some promise of improved performance with respect to forecasting redevelopment, at least as compared to other trending methods. It employs analysis of other variables besides load as it compares a small area's recent growth history to the load history of other areas long in the past. Conceivably, if the template match were set up correctly, it might be able to identify a present suburban area that "looks" very much like an urban area that twenty years ago was suburban and beginning its transition to urban. It could then use that area's history to forecast the suburban area's future. The key to success with such a template matching would clearly be to find a statistically significant context (some variable or variables) that the algorithm could use to distinguish between suburban areas that might develop into urban and those that are likely to stay suburban. Such work has not been carried out to date. And in general, trending is not favored for redevelopment forecasting.

100

re 40

1995

2020

2025

Figure 19.5 Strategic redevelopment can be addressed to a limited extent with trending through artful use of the horizon-year loads (see Chapter 9). Here, a base (no redevelopment) extrapolation of load growth in a stable area of the system produces the dotted line trend. To this planners then add a "known" 18 MW of redevelopment additions expected in five years, and a total estimate of 42 MW "eventually" (twenty years). As recommended in Chapter 9, dual horizon-year loads are used. The resulting curve fit of a third-order equation (solid line) now anticipates redevelopment.

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Recommended steps when using trending Again, trending is not recommended. But if it must be used, then the horizon year loads are the mechanism with which to address redevelopment. External to the trending program, the forecaster should assess if redevelopment is likely and determine a horizon-year load that reflects the expected redevelopment. That can then be used in the curve fitting (see Chapter 9). In addition, if a redevelopment project is known, or suspected, a "horizon year" load somewhere in the near term can be used (Figure 19.5). Redevelopment Interactions with Simulation Within the context of the simulation algorithms covered in Chapters 10 - 14, the transitions from rural to suburban, from suburban to urban, and from urban to urban core are treated as transitions in land use or consumer class. The simulation approach's land-use change model concept matches the development type paradigm quite well. There is a one-to-one correspondence between the transitions in developmental type and changes in land use. The first transition is from vacant useable (rural or agrarian land is vacant as far as a spatial had forecast perspective is concerned) to some form of suburban land use. Subsequent transitions involve replacing that existing suburban development with denser development of residential, commercial or industrial land use. In the real world, density is increased using one or both of two methods: the new buildings are taller, and/or they are built closer together. In a simulation, the density increase must be handled by a change in land use, from a "low-density" land use to one of higher density (and higher load/acre). It is these subsequent redevelopments from suburban to higher density that challenge the accuracy and representativeness of simulation methods. One complication is the complexity of considerations inherent in determining if existing land use will redevelop. This involves something like twice as many factors as determining if vacant land will develop. The various considerations involved in answering "Could the present land use be replaced with something else?" are about as complicated as those required to determine "What is this small area ideally suited to become and how preferable is it to that purpose." Redevelopment of anything but vacant land requires answering both questions, not just the latter, so it is roughly twice as complicated. Beyond this, tracking the land-use changes as the program simulates growth is a bit more difficult. Finally, simulation programs can have some difficulty in dealing with redevelopment's relationship to urban poles. Basic simulation approach to redevelopment All spatial load forecast simulation approaches use some form of the following method to project where, when, and what growth occurs. Through some analytical means they evaluate the various small areas for how well they match the profile of the various land-use classes. Those vacant small areas that score

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well with respect to classes like residential, retail, offices, industry, etc., form part of the available inventory of growth space for each class. The simulation then designates the most highly rated of these small areas for each land-use category transition to that class, in amounts sufficient to satisfy the overall needs for service-territory-wide land-use growth (global inventory of growth). This "classic" spatial forecast simulation approach, as described in Chapters 12 - 14, routinely considers only vacant useable land for such transitions in land use. Programs such as CEALUS, DIFOS, ELUFANT, SLF-2 and FORESITE all took this approach: routine transitions in land use occurred only from vacant land. Redevelopment is/was handled within each program by the various workarounds and "manual intervention" methods to be discussed later in this section. This limitation was not serious in some of their applications for several reasons: • Most of the utilities using these were most concerned about meeting Greenfield growth on the periphery of their metropolitan areas. • Redevelopment could be handled to a limited but sufficient degree, in a manual, if messy, work-around manner. In addition, it is worth considering need in general. The simulation paradigm and the programs outlined above (or their forerunners) were developed in the mid-1970s and early 1980s, when the products of the post-war building boom (1948- 1958) were generally too new to be replaced except in very exceptional circumstances (such as Figure 19.2). Simply put, there was not nearly as much redevelopment then as there is now, twenty to twenty-five years later. 19.4

EIGHT SIMULATION APPROACHES TO REDEVELOPMENT FORECASTING

Despite the fact that a potential error of up to 15% is introduced into a spatial load forecast by ignoring redevelopment growth, there are work-arounds available for even the most classic of simulation approaches whose algorithms ignore it altogether. As a result, the actual error level in any simulation forecast due to an algorithm incapable of handling redevelopment is substantially lower than 15%. However these work-arounds will not address all of the redevelopment issues that are important to T&D planners, nor will they reduce error to the minimum possible. Table 19.8 summarizes eight methods or approaches that will be discussed in more detail below. Not all are recommended. None are complete - success requires using several in coordinated form. The order covered is basically from simplest to most complicated, of increasing the complexity a stage at a time as one moves from least- to most-comprehensive (and accurate) methodology. Method 1: Ignore Redevelopment Altogether Redevelopment has been completely ignored in many forecasts, with different results depending on circumstance. In cases where development is nearly all

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Table 19.8 Different Methods That Can Be Used to Address Redevelopment in Simulation-Based Spatial Load Forecast Programs Type Approach

Error Work Applies to Advantages

Disadvantages

1

Ignore redevelopment altogether

15% 1.00 Small towns, Simple rural areas

High level of error.

2

"Split classes" work-around

8% 1.30 Any size Allows use of metro area classic simulation or rural area program while controlling error to about one half

Effort, time and skills required for the work arounds are both high. Resulting error is usually low, but is occasionally great.

3

"Time-tagged" consumer class model

8% 1.05 Any size Requires additional Reasonable if metro area approximate model data on area ages. or rural area of the various End-use model modes of becomes much more complicated to set redevelopment. up and calibrate.

4

Manual intervention using spatial editor

10% 1.15 Any size By far the best way Labor intensive and metro area to handle strategic usually not highly or rural area growth, but accurate. inaccurate for other modes of redevelopment.

5

Preprogrammed redevelopment

10% 1.40 Any size Computerization of metro area (4) reduces error or rural area and improves documentability of the forecast

Usually inaccurate. Relies too much on user expertise, and requires skill to handle redevelopment well

6

Designated redevelopment areas

12% 1.01 Any size Simplifies modeling metro area of re-development. or rural area Very easy to set up. Requires less time, skill, and data than some other methods, Good set up of multi-scenario studies.

Not very accurate. Misses most tactical redevelopment completely and much strategic redevelopment.

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608 Table 19.8 cont. Type Approach

Error Work Applies to

Advantages

The single biggest 1.10 Any size metro area improvement or rural area needed to handle urban core and tactical redevelopment well.

7

Threshold factors in preference functions

8%

8

Urban pole compression model (computerized)

10% 1.20 Only very large metropolitan areas (over 1 million pop.)

More accurate than other methods at forecasting relative growth of different areas in a major city.

Disadvantages Must calibrate preference functions across-classes, which is tedious and requires great skill.

Complicated algorithm requires good expertise to set up properly.

2+7 Combine two or semi-successful 3+7 "work-around" approaches

6%

Synergy of these 1.40 Any size metro area two methods means or rural area the sum is better than the parts. Work required is not much more than that required for either.

Calibration can be difficult if this is all or mostly manual, as it is difficult to distinguish between effects of (2) and (7).

2+4 Combine all the +7 "work-arounds"

4%

1.55 Any size As good as metro area redevelopment can or rural area be handled by "work-around" methods.

Effort, time and skills required for the work-around effort are very high. Very difficult to calibrate.

4%

2.50 Any size Potentially good metro area accuracy. or rural area

Number of factors and their complexity makes for a nearly unmanageable calibration process.

or 3+4 +7 2+4 +7+ 8 or

Combine all the "work-arounds" with urban compression

3+4 +7+ 8 All of the above < 4% 1.15 Any metro All in a compatible area computerized package

As accurate as seems realistically possible.

Requires a lot of skill on the user's part. Calibration, even computerassisted, requires great skill.

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Greenfield growth, this has no consequences. Such forecasts are not uncommon. Several spatial load forecasts the author participated in for the Rio Grande Valley included no significant redevelopment because there was very little there in the beginning to redevelop. But generally, ignoring redevelopment completely is a perilous practice. Although the study of how growth occurred (Tables 19.2 - 19.4) covered earlier was limited in its scope, the conclusion that about 15 - 16% of growth occurs as redevelopment is fairly representative of most forecast situations. Planners risk roughly doubling the actual error in a forecast by ignoring redevelopment. All simulation methods evoke some process of spatial allocation. The computer program allocates among the small areas a previously performed global forecast, or it forecasts their load growth subject to a control total, which amounts to the same thing. It is worth considering the impact of ignoring redevelopment growth in this context. The spatial allocation process will allocate the global land-use change and load growth totals - in the context of spatial error discussed in Chapter 8, there will be no DC component of error. Thus, the method that cannot forecast growth as redevelopment, somewhere, will forecast that growth as vacant-area growth, somewhere else. The overall impact is easiest to picture by first considering what doesn't happen in the central, already-developed areas of the system: the load is not forecast to grow, whereas it will. Therefore, the program that cannot model redevelopment will forecast that growth to occur in outlying, vacant areas of the system. Redevelopment growth that occurs inside a city is forecast to occur on its outskirts instead. Figure 19.6 illustrates the type of forecast error that results, among the worst patterns of spatial error that can possibly occur in a T&D load forecast. While there is no DC component to the spatial error distribution, there is a great deal of low spatial frequency error. The good and bad about just ignoring redevelopment The only good aspect of this approach is that it simplifies the forecast process and reduces the effort involved. If redevelopment is not an issue, then planners have reduced their effort and made no impact on the forecast. But the "bad" usually outweighs any good. If redevelopment is an issue to any extent, the planners have introduced a significant error into their forecast by ignoring it, one which could be substantially reduced by work-arounds, even if not eliminated, regardless of how limited the programs and forecasting tools they are using might be with respect to how they model redevelopment. Method 2: Work-Around by "Splitting Classes" Normally, planners do not "totally ignore" redevelopment growth even if they are using a simulation method that models land-use change as only from vacant land. This work-around involves doubling land-use classes, creating two sets that are almost alike, but which represent land use that is redeveloping, and that

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Distance from City Core


Figure 19.6 Conceptual illustration of the impact of "ignoring redevelopment" completely with a simulation model. At the top, solid line indicates the load density of a theoretical city as one moves out from the city center, with high loads in the core and lower load density near the periphery. The dotted line indicates how the simulation, minus any consideration of redevelopment growth, will allocate load. As the forecast proceeds into the future, load densities nearest the city core will be underestimated and those nearest the periphery overestimated. Bottom, the resulting spatial error is among the lowest frequency error possible in the load forecast, and therefore has tremendous (negative) impact on T&D planning.

which is not. This doubles the number of classes that have to be used in the model. However, most simulation models were written with sufficient capacity to handle such a large number of land-use classes that it does not limit use of this approach. Land-use classes are broken into "old" and "new" sets. Both sets represent identical types of land use: residential, commercial, high rise, industrial, municipal, etc. Old land-use classes are used to represent land use throughout the system in areas that might redevelop. "New" classes are used to represent land use that has occurred so recently that redevelopment is unlikely. In general, all areas where the average age of buildings will be 30+ years by the middle of the forecast period can be classified as "old." Classes used in the end-use model have to be doubled, too. The end-use load model is used to represent differences between the load density for older as opposed to newer areas of what is

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otherwise the same class. The older areas are modeled as having a growing load on an annual basis, to account for the redevelopment that occurs in those areas. Example Table 19.9 shows an example, using as its basis the land-use classes and the class-by-class load densities from Chapter 11 's simulation example. Redevelopment in that example was not a significant issue (Springfield was basically too small a city for it to be an issue of importance) and what redevelopment there was to forecast was handled by manual intervention (see page 359). However, for the sake of this example, assume these classes, and load densities, were being used in a large metropolitan area instead of a small city like Springfield, one where redevelopment was certain to be an issue. Table 19.9, then, represents "old" and "new" class by class loads that started from the same set of class load densities (those in Table 11.8).

Table 19.9 Diversified Summer Peak Load Densities by Class (kVA/acre) for Chapter 11 's Springfield Example, Modified to Mitigate Hypothetical Redevelopment Error Consumer Class

Growth Rate

Newer areas of the system 1 Residential 1 2 Residential 2 3 Aprtmnt/Twnh. 4 Retail comm. 5 Offices 6 High-rise 7 Industry 8 Warehouses 9 Municipal 10 Heavy ind. Older Areas of the System 11 Residential 1 0.50% 12 Residential 2 0.50% 13 Aprtmnt/Twnh. 0.70% 14 Retail comm. 0.80% 15 Offices 1.10% 16 High-rise 2.10% 17 Industry -0.40% 18 Warehouses 2.20% 19 Municipal 0.50% 20 Heavy ind. 1.20%

Year Now

12

20

25*

2.97 2.87 2.73 3.09 3.03 2.80 2.75 13.98 13.75 13.52 12.99 12.50 12.02 11.80 18.18 17.83 17.59 17.40 17.25 17.10 17.00 21.69 20.51 20.00 19.86 19.75 19.65 19.55 62.69 61.40 60.50 59.50 59.00 58.80 58.65 192.00 189.90 187.20 185.00 183.00 181.20 179.80 20.58 20.00 19.50 19.00 18.75 18.69 18.66 9.21 9.22 9.24 9.32 9.20 9.28 9.30 63.04 61.80 59.35 57.50 56.73 56.73 56.73 240.64 240.64 239.00 237.00 235.00 233.00 232.00 3.04 3.09 3.09 3.06 3.03 2.99 2.97 13.98 13.89 13.79 13.52 13.27 13.28 13.37 18.18 18.08 18.09 18.40 18.76 19.66 20.24 21.69 20.84 20.65 21.17 21.73 23.04 23.86 62.69 62.76 63.21 64.94 67.28 73.18 77.10 192.00 197.96 203.43 218.46 234.83 274.58 302.30 20.58 19.84 19.19 18.40 17.87 17.25 16.88 9.20 9.62 10.06 11.00 12.05 14.37 16.06 63.04 62.42 60.55 59.84 60.23 62.68 64.26 240.64 246.45 250.68 260.73 271.17 295.78 312.61

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In this example, planners increased these old loads by the percentages shown in the second column for the "old" set. They obtained these percentage growth rates by studying load growth in older areas of the system. For example, their studies determined that over a decade, growth in the average apartment/townhouse area of the inner half of their system increased by 7.25%. This translates to .7024 percent growth, as shown for the apartments/townhouses in column 2 of the lower set of land-use classes in Table 19.9. These "redevelopment growth rates" are then applied on top of the load densities projected for normal conditions (new, or the top set, of Table 19.6). The composite growth rate is then used to adjust the base (new) land-use load densities so they grow annually, so they are "redeveloped." Note one class (industry) has a slightly decreasing "growth rate." This is rare but does happen it is being gradually abandoned/transformed to other land use types. All land use in areas of the system where the average building age is 35 to 40 years or greater is then represented with these older land-use types. The selection of this "age cutoff value" is a compromise. Thirty-five to forty years is about when redevelopment begins to occur (in most cases). However, as the forecast moves into the future, all areas of the system will age. Five years out, these "older" areas are 40 to 45 years old, ten years out they are 40 to 50 years old, and typically very ripe for some redevelopment. Thus, setting up the program in this way means that by the end of a 20-year-ahead forecast, only areas that are now 55 years of age or older will be "redeveloped." However, setting the cutoff lower (e.g., 20 years) means that early in the forecast process some areas that are "not yet ready" are represented as redeveloping. Modified method for "splitting classes" (more work) The planners could use three sets of land-use classes. They could have a new, old, and an interim soon-to-be old classification. These interim land uses would then only start growing in load in forecast year five of the forecast, thereby reducing compromise in "age cutoff date" mentioned in the paragraph above. The author is aware of one utility planning staff that used this approach. It produces a slight improvement in accuracy at a great additional cost in effort and program resources. Note that one cannot improve load forecast accuracy with this work-around by stopping the program at the end of each iteration and moving a few aging areas from new to old classifications with the program's spatial editor. (For example, areas that were only 25 years old in the base year could be moved from new to old classifications after year ten of the forecast to represent that they are now old enough to begin redevelopment). To do so would mean that the end-use model would apply ten years worth of redevelopment growth to them on the next iteration - making them "catch up" with those areas that had begun the forecast as redevelopable.

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Figure 19.7 Use of the "old and new" land-use class bifurcation reduces the amount of error and increases its spatial frequency, reducing its impact. At the top, dotted line shows the modeled spatial distribution versus actual (solid line). Bottom plot shows actual error is reduced even further - see text for details.

Impact of split classes on reducing error

Figure 19.7 shows the resulting error cross-section, corresponding to Figure 19.6's example. In examining this diagram it is important for the reader to remember that the simulation being discussed here is a spatial allocation. When using a standard simulation approach, nothing has to be done to the load densities in the outlying areas for the program to back off on over-allocating load there, if the load densities in the interior areas where redevelopment is likely have been raised. The higher "old" load density takes more of the growth and leaves less to be allocated near the periphery: over-estimation of load there is reduced.2 The resulting forecast (dotted line in the top of Figure 19.7) is closer to the actual (solid line) than was the case in Figure 19.6. Impact on T&D planning is 2

One detail not discussed in the example above. To implement this "trick" the forecaster has to first increase the load densities of the old areas of the system, as shown. He or she then must compute the increase in load that results, and manually adjust the simulation's global land-use inventory- the projection of how much land use grows overall each year, to compensate. Doing this well (to the extent possible) requires a lot of attention to detail and numerous computations. This is why Table 19.8 indicates that this approach requires up to 25% more effort. This is a tedious and labor-intensive analytical step.

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reduced even further. In the bottom of the figure, the thin dotted line shows the actual error. The heavy dotted line shows the effective error - which has been reduced even more. High-frequency components have been removed from the heavy dotted line, leaving only the "impacting" components of spatial error. This diagram is conceptual only, but the magnitude of the error diagrams here and in Figure 19.6 gives an idea of the relative level of error. This work-around cuts error due to an algorithm's inability to model redevelopment roughly in half. Advantages and disadvantages of "splitting classes" work-around This approach can be applied with nearly any land-use-based or consumer-classbased load forecast simulation. On average, it seems to roughly halve the effective error due to the program and/or users' not being able to model redevelopment. The procedure is straightforward. All analysis can readily be performed with only an electronic spreadsheet, even if it is tedious work. Against this, the "modeling" of redevelopment is really not very good, particularly with respect to spatial detail. Error is only halved. When forecasting the growth of older metropolitan areas, where there is little growth on the city's periphery, the error due to redevelopment, even if halved, would still be very significant. In addition, while the procedure to apply this is straightforward and can be done with only spreadsheets, the forecasting expertise needed to do this well is high. It requires considerable expertise to determine the rates for the older classes, to make the best compromise on setting the "cutoff age for an area, and to decide if an area is old or new or extremely high. This approach provides many opportunities for mistakes. For these reasons, it is recommended only if nothing better is available. Method 3: Work-Around Using Time-Tagged Land-Use Classes Some simulation programs not only identify the land-use class for each small area but permit the user to retain an attribute which is the "development age" of the small area - the year when the area was built out. These include particularly a version of the Westinghouse LOADSITE program (LOADSITE 2.2, August 1989) widely used in the late 1980s and early 1990s, and the CAREFUL program developed by Carrington. In these, an area of single-family homes built in 1992 would be classed as "residential 2" with an attribute of 1992. Time-tagging was originally developed to track differences in energy efficiency among older versus newer buildings. End-use models using this approach keep different load curve data for different age groups (see Chapter 4). The load curve for homes with an attribute of "1965" might reflect the lack of energy efficiency built into them, versus those with an attribute of "2002." However, time-tagging can be applied to represent redevelopment. There are no split classes. Using again Chapter 11 's set of land-use classes (Table 19.8), there would be only the original ten, not twenty. But the time-tagged end-use model would represent that after age 30, load density in residential class 2

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(residential 2) grows at .5% annually, and load density of class 6 (offices) grows at 1.1% annually. Error is reduced slightly from that of Method 2, due to better resolution of "interim age" areas which become "old enough" as the forecast moves into the future. The method is particularly effective at modeling redevelopment in the same class, which accounts for a small portion (about 2% according to Table 19.3) of redevelopment in metropolitan areas. Usually such development does change the load in such areas, something not modeled at all in most other approaches. Method 4: Manual Intervention In the Forecast Process In Chapter 11 's detailed forecast example, Susan, SL&P's forecaster, manually entered redevelopment of 19 acres near downtown into the forecast (page 359). The entire forecast process described in Chapter 11 was manual, so the handling of redevelopment there as a manual step did not seem any different than the handling of the rest of the forecast process. But any planner using a computerized simulation can apply a manual forecast of redevelopment, one done outside of the computer program and entered via the program's spatial data editor. To the author's knowledge, this can be done with every type of land-use or consumer-class-based simulation program. The process is simple to execute. The user simply determines what areas of the system will redevelop, to what land uses, in what year. He or she then uses the simulation program's data editor to change the land-use codes of the appropriate small areas at the conclusion of whatever forecast iteration is just before their date of redevelopment Figure 19.8 shows Chapter 12's diagram of the typical simulation iterative process, indicating the manual intervention. Here, the forecasters have decided to model redevelopment in year 2004. For the example here, assume they wish to model that 8 acres of municipal development, and 12 acres of high-rise commercial, would replace 11 acres of warehouses and 9 acres of light industry. As the program finishes the year-2003 forecast, they stop its operation momentarily. They manually edit: The spatial data for the areas to be redeveloped, changing the land-use codes from [warehouse] to [municipal] and [high-rise commercial] to indicate that redevelopment has occurred. The global land-use inventory, changing the counts so that on the next iteration the program will forecast 8 acres less of municipal growth and 11 acres less of high rise commercial than it otherwise would have done. If they decide that the 11 acres of warehouses and/or 9 acres of light industrial were needed in the region, and not redundant, then they add these amounts into those respective class totals for the next iteration. In this way, the growth the program does model is kept consistent with the overall global totals.

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2002 2020

2003

FORECAST A-j \ ALGORITHM

Program User

2005

2010

2007

Figure 19.8 The user can intervene in any iteration to change land use to whatever he or she believes will occur with respect to redevelopment, then let the program proceed. Here, the forecaster has intervened at the end of the forecast program's second iteration, entering data that represents redevelopment expected to occur after 2004 but before 2006.

They then re-activate the program and it completes the cycle of iterations, beginning with the iteration for the period from 2004 to 2005. The redevelopment is now included both in the totals, and in computation of the surround and proximity factors for those small areas around it. Using manual Intervention to represent strategic redevelopment In cases of known or planned strategic redevelopment, manual intervention, whether truly manual or "pre-programmed" (see method 5) is probably the best way to model the redevelopment. There are two reasons. First, even the best redevelopment algorithms cannot forecast some major redevelopment projects. Many just "do not make sense" from the standpoint of a simulation program's pattern and preference computations. The largest and most grand redevelopment projects only make sense when viewed through a "visionary lens." Often, the politicians or developers driving a major redevelopment project see how it will change the urban landscape around it. In fact, they may be driving the particular redevelopment project mostly because they want its effect on the rest of the urban area, more than they want that redevelopment itself. Baltimore's Inner Harbor redevelopment is only one example, but a great one, of a major

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initiative that changed the character of the entire region around it. Simulation programs often cannot see and therefore will likely not forecast such quantum changes in urban character. The only way to "get it right" is to manually enter it. Second, manual intervention and entry fits the needs of planners, which invariably require them to study proposed redevelopment projects as scenarios. Not all such initiatives come to fruition. Those that do have long lead times long enough for planners to consider them carefully. Often, the utility is being consulted in the matter - "If we redevelop this area what facilities will be required for electric service?" Manual entry of the redevelopment project as a scenario is the way it should be studied. Details and caveats with respect to strategic redevelopment In general, with respect to spatial data, little more is required of the planner to apply this method than the redevelopment plan's land-use being entered into the program for the areas indicated, in the year's it is planned. Considerations for the global model, however, are more involved. Planners need to determine if the particular redevelopment scenario will impact global land-use inventory changes and if so, how. Questions that need to be answered are: Will the existing land use be displaced? Should the land use be replaced by adding to the global model's land-use counts so it is "rebuilt" somewhere else in the service territory? This is the case if that land use is needed. For example, if 100 acres of homes and retail shopping are displaced as part of a redevelopment project of the city's international airport (runways lengthened, etc.) then that displaced land use needs to be "moved." This is most expeditiously and accurately done by having the program forecast it as new development in the subsequent forecast iteration. The 100 acres of land use is added to the global model's totals for growth in the next iteration. On the other hand, if the issue is 20 acres of completely abandoned and unused warehouses, then they are redundant and do not need to be modeled as displaced land use. This land use is not added into the global model. Is the redevelopment project's land use in addition to, or part of, the normal growth for the service territory? For example, a redevelopment project that includes a new convention center or stadium, along with hotels, restaurants and so forth, usually represents a net increase in the entertainment industry for a region. Therefore it is in addition to all base land-use change. An example of a net increase due to redevelopment is in downtown Denver, due to the new stadium of the professional baseball team, the Rockies. So is the redevelopment of Baltimore's inner harbor, which included a major convention center, and eventually, a new baseball stadium, etc. In such cases, the land use of the redevelopment should be added to the base-case's land-use increase amounts, as represented in the simulation's global model.

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However, a project that calls for mostly office towers and other similar employment centers represents only a planned change in where such growth might otherwise locate. Usually, this is only a change in the preferred locations of the same amount of growth that would otherwise occur. Detroit's Renaissance Center is closer to this category than it is to Denver or Baltimore's examples. It included offices and changes that were in essence competing against "edge city" development on the metropolitan periphery. In many cases, only a portion of the redevelopment is additional. Baltimore's inner harbor included shops, hotels, museums and a national aquarium, retail aimed at both tourists and local residents, and commercial space and high-rise offices. It represented a net increase in tourism, but part of it was merely relocation of commercial offices.3 Parts of that project should have been added to the global model totals (those associated with tourism) but not other parts. Using manual intervention to represent tactical redevelopment Manual intervention works well - often better than any other method - for meeting the planners' needs to study major (strategic) redevelopment initiatives. Using it to model tactical (small scale) redevelopment is much more challenging and prone to somewhat more error. However, it can be done and it has been done well at the cost of much tedious work. There are numerous approaches, all variations on the one theme to be explained here. Suppose that a detailed study of redevelopment for warehouses within the inner core (perhaps a ten mile radius) of downtown indicates that 6.5% annually are converted to retail/loft apartments. Then, planners can manually edit one out of every 16 warehouse small areas per year of forecast (in a uniform grid approach, or about 6.5% of land-use change in polygon areas). This manual intervention represents their change to retail and apartments/townhouses land use. The big issue, of course, is where the editing of small areas will be done. Planners can use their judgement, or pick those areas closest to downtown, or select random areas. All seem to produce about the same overall forecast error. A superior method, if data is available, is to identify some pattern to conversion seen in the past. There is no guarantee that past trends will continue but extrapolation of the trend is generally slightly better than random selection. This is done only in the inner areas of the system. Ultimately, with respect to tactical redevelopment, the use of manual intervention modeling is no more effective than, but just as effective as, method (2), split classes, at modeling tactical redevelopment growth.

Very few out-of-towners would have wanted to visit Baltimore's Inner Harbor as a tourist prior to its redevelopment. Now it is a popular attraction (and one of the author's favorite places for a short, fun get-away).

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Advantages and disadvantages of manual intervention The advantages of manual intervention modeling of redevelopment is that it is straightforward to apply, it works with any simulation program, and it fulfils most of the needs that planners have for modeling strategic redevelopment in multi-scenario studies. But frankly, its biggest "advantage" in the eyes of many users (and perhaps also its downfall) is that it gives planners complete control of the redevelopment modeling. They can apply their "judgement." Against these advantages, manual intervention has several disadvantages. To begin, the user's have to apply their judgement - if it is flawed, so is the forecast. And it is not good at modeling urban core, tactical, and in-kind redevelopment. Method 5: Designated Redevelopment Areas Some simulation programs permit the user to set a "redevelopment availability" flag or indicator from each small area. This was a feature in ABB's LOADSITE and in a modified form in its FORESITE programs (1992) but is now widely used, for example in Terncal's DIFOS and Carrington's CAREFUL programs. Turning this tag to "on" (setting it to 1) for a small area forces the simulation algorithm to consider that small area as vacant with respect to future development. The simulation algorithm will treat all those areas with the redevelopment flag set to "on" as if they are vacant and available for development. Generally, this approach includes land-use inventory features that count all land use that is displaced and add it into the global inventory for purposes of replacing it with "new" land-use change in vacant areas. FORESITE and DIFOS took this approach. Displaced land use is put back into the pot to be reallocated to other areas of the system. Potentially this could include other designated vacant areas, but usually it is represented as new growth in previously vacant areas of the system. The CAREFUL program uses a unique approach to this displaced land use, aimed at modeling redevelopment in a very crowded megalopolis. The algorithm forces all displaced land use to be replaced with new growth within the influence area of its nearest urban pole (actually, with an area described by 80% of the pole's radius). If no vacant land is found for this displaced growth (usually the case) the program boosts the load density of all of the land use of that type within that area by an amount calculated to match the displaced land uses' load. The approach is apparently accurate at representing a type of "continuous growth in overcrowded mixed urban conditions" which is a characteristic of cities such as Hong Kong, Shanghai, Bombay, and New Delhi (Carrington and Lodi, 1992). Modeling strategic redevelopment with the designated-area approach Use of a redevelopment flag does not do a good job of representing strategic redevelopment. It is not recommended for situations where manual intervention

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can be applied. In general, if the area proposed for a major municipally sponsored or large developer-financed project is modeled using only this approach, any simulation program will forecast it as developing into something other than what is planned for the project. The simulation will invariably pick development that fits (as much as possible) the present urban landscape around the designated area. The limitations of simulation in "seeing" the visionary overall long-term pattern of a redevelopment project, covered in Method 4 above, apply here. Since manual intervention can be used with virtually any simulation program, there are few situations where a planner should use this redevelopment-flag, designated-areas approach to model strategic redevelopment. However, if planners choose to, then the area should be zoned (if the simulation program permits this) to limit growth to only the planned redevelopment land use. Designated-area modeling and tactical redevelopment Unlike strategic redevelopment, tactical redevelopment most often does match the local urban landscape, at least in a broad sense. Thus the preference-function engine should be able to do a good job of forecasting redevelopment. However, a basic simulation program can potentially make two mistakes with respect to tactical redevelopment: it can over-forecast redevelopment, and it can get the land-use class wrong. These are potentially a problem with any forecast, but a characteristic of some designated-area models exacerbates both types of error. This has to do with how the "cost" of removing the old buildings at the site is handled by the program. Anyone wanting to build on an already-developed site has to not only buy the land, but the existing buildings as well, and then pay to demolish them. This means that an otherwise identical vacant area will always be picked over a currently-developed area for new growth. Existing development sets a threshold: land with existing use must be more suitable for its new purpose than available vacant land, in order for it to be selected for development. Simulation programs need to be able to model this cost with what are often called "redevelopment threshold" factors in order to be fairly accurate at predicting redevelopment. Thresholds: the key to success in modeling tactical redevelopment Therefore, designated-area approaches are most successful when the spatial preference factor computation applies a threshold or negative factor to the preference function calculations when the area is already developed. This threshold or negative factor represents the cost or difficulty in removing the existing land-use prior to development of the new. Looking at the preference function equation used in Chapter 13 (eq. 13.4) to compute the suitability or preference score for c class, for vacant area x,y, which was

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J PC fry) -

S(fj(x,y)*pj,c) (19-0 j=i where j indexes J (the factors) and c indexes C (the land-use classes). Pc(x,y) is the pattern match score for small area x, y to class c pj>c is class c's coefficient for factor j and by definition, pj>c e [-1.0, 1.0] fj(x,y) is the score for factor j in small area x,y Pc (x,y) is therefore somewhere between -12.0 and +12.0 for J = 12 This can be applied for a non-vacant area with the following addition of a threshold factor drawn from an N by N matrix where N is the number of nonvacant land-use classes J PC fry) = Z(fj(x,y)*pj,c-T(c,e(x,y)) (19.2) j=.l where T(c, d) is the threshold required for land use of class d to develop to class c, and e(x,y) is the existing class of area x,y In a simulation program in which preference values and all associated computations were based on absolute economic value, T would not have to be a matrix. T(c, d) would be the same for all values of d, and thus a single factor for each land-use class could represent the cost of buying and removing the old building for that class. However, most simulation programs measure preference in arbitrary units, usually normalized to a value of 1.00 = perfection within each class's context. As a result of this normalization, the same existing land use may need to have a different threshold applied for consideration of its suitability in each of the land-use classes it might potentially become. For example, suppose the cost of buying single-family old homes in an older area of the city and removing them from a site represents 10% of the total value of a new home (including lot). Then in some sense that cost represents a 10% negative factor - the site has to be very good relative to other options before someone would pay 10% more overall in order to gain a home site close to the city. Thus, an appropriate factor might be .10 for row 2, column 2, of T. But the cost of buying and removing homes represents only a tiny amount of the cost of building a high-rise building. What was a 10% factor would be far too high when judged against high-rise class preference scores. In fact, the value would be close to zero. Thus, in cases where a program uses arbitrary preference scores normalized on a class by class basis (and almost all simulation programs do), a T(c, d) matrix is required to represent redevelopment costs well. Generally, the threshold should represent the cost of removing the old land use as a portion of the all inclusive cost of the new land-use development.

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In some cases, a value should be added not subtracted: conversion of warehouses to loft apartments generally costs less than putting up an all-new apartment building. Using designated areas to model redevelopment Again, manual intervention is generally a much more satisfactory way to model strategic redevelopment than the designated area approach, and is recommended, although it does require more work and record keeping by the planners. Tactical redevelopment can be represented well if the simulation permits and the user applies well the set of threshold values in a (T(c,d)) matrix as discussed above. Some spatial load forecast simulation programs do not permit the user to apply thresholds. However, they can be set up to mimic their effect with a nearly universal work-around. Many simulation programs permit the user to enter a "development bias" factor for a small area, basically a value that will be added or subtracted to its preference scores, boosting or dropping its suitability scores in comparison to other areas and making it more or less likely to develop.4 In such a case, when the user "turns" on the redevelopment tags in a set of small areas, he or she should also enter negative factors of value for these small areas, corresponding to T(c,d). Again, this represents manual entry of the redevelopment cost of each area. And, of course, that represents the cost of clearing out the old and preparing the site for fresh development, which is a factor, T(c,d). Unfortunately, this work-around forces the planner to apply the same value for all values of d - regardless of potential land-use class the small area could become - as opposed to different values applied to each class's suitability, as was described above. Advantages and disadvantages of the designated-areas approach The advantages of designated areas are that they greatly reduce the work required of planners and forecaster using a simulation program, when modeling redevelopment. However, method cannot model strategic redevelopment well. If augmented with a matrix-based threshold value model as discussed above, it does an acceptable job of forecasting tactical redevelopment, if the threshold values are properly adjusted. There really are no disadvantages, if the approach is kept within its limitations, which means it is used only to model tactical redevelopment and all strategic redevelopment is handled by some other method (which usually means manual intervention, since programs that use the designated-area approach generally do not have any of the more advanced models discussed below).

4

Such factors are often used as a brute force way to "tune" or calibrate the dynamic characteristics of a simulation to reproduce recent growth patterns in an area. Although not frequently used, many simulation programs have features that permit this.

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Method 6: Preprogrammed Redevelopment Events Some simulation programs that use an event-driven format for their iterations have features that permit the user to enter descriptions of redevelopment events that will occur in certain areas at certain times. The user enters a series of landuse changes (from, to) for specific small areas, and a time (year). The program then applies this as a growth event. This is basically a computerized form of manual intervention (Method 4) designed to model strategic redevelopment. Everything stated above for that method applies to this. It does not work well on tactical redevelopment. Advantages of computerization over the manual (program editing) approach are that it is quicker and the user does not have to fuss with adjustments to the global model. The program handles such accounting automatically. Method 7: Generalized Tactical Redevelopment Model If a designated-area method (Method 6) is set up to work really well, with appropriately fine-tuned values of T in use, then there is little need to designate redevelopment areas within the metropolitan area - all areas, except restricted and undevelopable land, can be considered potential areas of new development. This approach can only be used when the model is very well calibrated and the T matrix is correctly calibrated. Otherwise considerable error, including obviously incorrect spatial patterns of growth, will occur. Strategic redevelopment still must be handled manually, or with Method 6. Method 8: "Compression Models" of Urban-Pole Center Growth Urban poles are used to represent the cumulative effect on regional growth of a large concentration of high-rise and urban development in an area, but what influences the growth of the urban pole's kernel - the high-rise and urban growth itself? Where in a metropolitan area does the real urban development - high-rise and dense "core" development, grow? Many planners regard the basic simulation approach as incomplete when it comes to answering this question — to modeling the locations of the causes of urban poles (industry and high-rise commercial). While the global land-use inventory model may represent that a specified amount of high-rise growth must be forecast, it does not specify where. And while preference functions (e.g., eq. 19.1 and 19.2) can compute the suitability of a location for high-rise, this is incomplete because that addresses only supply, not demand issues. What is lacking is a mechanism to represent the growth of the urban centers themselves and competition among them for high-rise growth. Does downtown continue to grow if powerful suburban centers develop? As an example, Houston, Texas, has a number of "competing" urban poles: its downtown core, Greenway Plaza a number of miles to the southwest, the Galleria/West Loop area slightly farther out to the west, a commercial office concentration at the

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intersection of I- 10 and Highway 1960 to the northwest, and many other highrise commercial centers. Just as there are different theories and examples about why cities grow, there are a large number of different methods to simulate the "competitive" growth of these types of urban poles. Among those that seem to work is the "reciprocal effect model," which will be summarized here, as representative of the general concept. Urban poles are activity centers that cannot function without an urban fabric around them, a large regional economy which can serve as the center. If the metropolitan area shrinks, so will the urban pole's basis (the high-rise and urban development), at least with regard to occupancy and economic activity). Among several different poles, their respective proximity to the rest of the urban fabric can evaluate how connected they are to the whole and thus how viable they are for future growth. Therefore, among a set of urban poles, one model of their respective development potential is to evaluate what surrounds them, not on a surround and proximity factor basis (what is nearby) but on a regional, very-low frequency spatial basis. In fact, this model usually employs the same "urban pole" functions in reverse. For each of a set of urban poles, a weighted-sum of all of the land use within that urban pole function's radii is developed, with any land use that falls in two or more urban pole radii allocated based on the respective values of each pole function. Influence for pole p at location x, y - Up(x,y) = height of pole at point x, y The economic clout of pole q, Mq, is its share of the weighted sum of land-use value within its radius, shared with other poles on an influence basis Mq = £ ^(Uq(x,y)/ZUp(x,y))x(i;A(x,y,c)*Fc) x=l

y=l

p=l

(19.4)

c=l

where A (x,y,c) = the amount of land use type c in area x,y Fc = an economic value weighting factor for land-use class c Basically, each pole gets its "share" of the land-use "value" in each small area, based on its influence over that area as compared to all other poles. The Fc weight land-use type by value - high-rise commercial land use is worth more than light industrial, etc. Usually, relative values of the Fc are based on the relative economic value of the types of land use (value in dollars to the buildings and land) are used. The actual units are unimportant since the total will be normalized to 1 .0 total for all poles below. Finally, pole q's portion of regional economic clout is computed as p)

p=»

(19.5)

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The global model's growth of urban land-use (high-rise) is then allocated to each urban pole based on its value of Cq versus the total for the region. Thus, if pole q has 22% of the economic "clout" of the region (Cq = .22) then 22% of the region's high rise will occur near its center. Even if the pole has no land use available, land is redeveloped to match this "given." The term "urban compression" comes from a perspective that the surrounding region is placing a pressure for growth on urban centers, proportional to its activity's proximity to them. Growth is driven upward if it cannot move outward. The reader unfamiliar with simulation models and growth processes might wish to consider carefully how such a model responds as the urban fabric grows. Growth of the city around a pole and the growth of the pole's basis are not necessarily reciprocal. A large pole may exert a tremendous influence over a region, yet there may be no land, or no suitable land, available for growth with its radius of influence. No land-use growth (and hence no cause-1 load growth) will occur within its influence area, despite its influence, because there is no room for expansion (or little room save for redevelopment). As a result, as modeled here, it will not grow or grow only slowly. By contrast another pole, of lesser size, may be proximate to a good deal of suitable and developable land. Land use there will develop, the land-use value within that pole's radius will grow rapidly, and its share of regional "urban clout" and hence more high rise will develop near its core. As an exercise, the reader might wish to consider what would happen with this model if a planner created an urban pole at a point where there was no highrise commercial, but assigned this new pole some influence anyway (a height and radius). High-rise commercial will begin to grow in and around the center of the pole, in the closest and most suitable local places to the pole location. If necessary, redevelopment will occur (if the simulation can handle it automatically) to force old land use out so new high rise can develop there. Thus, this model, rightly implemented, drives redevelopment. There are numerous variations on the theme of this approach, but the above equations and explanation summarize the essence of an urban-demand-driven, spatial activity center growth model. This is one way to model the demand for high-rise commercial development on a spatial basis. A standard addition to this model is that it may bifurcate urban pole influence so poles can be designated as places where only commercial or industrial, but not both, grow. Advantages and disadvantages of urban compression models When properly calibrated and set up, this model produces representative forecasts of cities like Houston, Chicago, and Los Angeles that seem to have the expected balance of growth among poles. Against this, it is quite complicated and requires a good deal of skill and often a lengthy calibration effort. If not set up correctly, a type of oscillation of growth, with growth moving from one pole to another and back again on each subsequent iteration, can occur. But the

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overriding fact is that urban activity centers do grow due to the "demand" for their influence or "services" as the core(s) of the urban areas. This model represents that, and if properly set up, can give good results. Method 8-S: Spatial Frequency Demand Urban Compression If the simulation program both implements a spatial frequency domain model and permits the user to set up "macro" commands, it is possible to implement urban compression in a frequency-domain manner which slightly improves accuracy and computation speed. The concept is the same as illustrated in Figure 14.5, with a distributed urban pole. The improvement in detail is exactly as described there, for urban pole application, and for precisely the same reasons. Some simulation programs that use spatial frequency domain computation may give the user no choice but to model urban compression with this method. 19.5

RECOMMENDATIONS FOR MODELING REDEVELOPMENT INFLUENCES

Most Important: Don't Ignore Redevelopment Above all else redevelopment should not be forgotten just because growth in new areas is easier to see, higher in growth rate, or more compatible with the type of computer program being used as a forecasting tool. Redevelopment accounts for a significant portion of electric load growth in most utility systems, and if it does not, the reasons behind that fact are worth knowing - they almost certainly influence other trends important to the planner, too. Avoid Curve-Fit Types of Trending Applied to Redevelopment Traditional curve-fit and similar trending methods simply cannot accommodate redevelopment in any meaningful manner. As discussed earlier, development of existing areas presents a classical "S curve" transition, which will create tremendous extrapolation error for any type of curve fitting. Template matching, at least the "very smart" template matching algorithms, are capable of addressing some if not all redevelopment characteristics and are much preferred among trending methods. But usually, "trending" does mean curve fitting, using polynomial functions whose coefficients are selected through multiple or stepwise regression. While not recommended, if this is the only method available then the planners have to make the best of it. In that case, horizon year loads are the mechanism with which to address redevelopment. External to the trending program the forecaster should assess if redevelopment is likely in an area and determine a horizon year load that reflects the expected completion date for the redevelopment. In addition, if a redevelopment project is known, or suspected, a "horizon year" load somewhere in the near term can be used to represent that (see Figure 19.5). A reasonable approach in some cases is to "borrow" a forecast done within this

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paradigm (e.g., a municipal population forecast) and interpret it for the horizon year loads. Regardless, this "external to the trending program" effort means the planners are in effect using simulation concepts if not algorithms. Therefore, it is recommended that they formalize their process as much as possible through development of a written procedure, along with documented notes on what and why they made adjustments and determined horizon year loads. When Using Basic Simulation, Employ All Possible Work-Arounds "Basic simulation method" as used here means algorithms and computers employing them which have no features to "grow" vacant areas or make transitions from any land-use class but vacant, that do not allow pre-programmed redevelopment, that have no time-tagging of land use, and that do not employ threshold factor (Tc) matrices. The following features are strongly recommended for any metropolitan forecast using such a basic simulation, where redevelopment might be a factor in the future growth of consumers and electric load: • Split land-use classes should be used to represent urban core, tactical, and in-kind redevelopment • Manual intervention should be used to represent strategic redevelopment projects as alternative scenarios. Planners should study the local growth rates around each of the major activity centers in the region. Based on this knowledge, program features (specifics will vary from one program to another) should be adjusted so that the growth rates of high-rise load near the center of each pole continue to grow according to recent trends. If Time-Tagging of Land-Use Is Available Time-tagging can completely model in-kind redevelopment and does an incomplete but acceptable job (i.e., better than other work-arounds) of trending urban core growth. When available, time-tagging should always be used to workaround tactical redevelopment rather than having the planner resort to the use of split land-use classes. Strategic and large-scale redevelopment should still be modeled with manual or pre-programmed intervention. If a Designated-Area Feature Is Available It should be used, but restricted to modeling tactical redevelopment. A simulation program's designated-area feature should never be used to represent strategic redevelopment projects — the simulation program is almost certain to predict the new land-use pattern incorrectly. Instead, strategic redevelopment projects should always be represented by manual intervention or as pre-

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programmed event development (essentially the same) if the program has that feature. The author has never encountered a situation that violates this rule. Generally, the designated-area feature works best when used in conjunction with redevelopment threshold factors (a T matrix) in the preference function (suitability) computations. If the program cannot apply threshold factors, the user might wish to consider the use of bias factors as an approximation (see discussion of Method 5 given earlier in this section). However, this is recommended only if the forecaster is confident that redevelopment in the designated area will lead to only one or two classes. Diversity in possible future development renders the bias approximation too error-prone (see discussion of why a T matrix is needed in Method 5, above). If threshold factors cannot be used, the user should strongly consider not using the designated-area feature and instead modeling non-strategic redevelopment with split land-use classes or time-tagging growth as recommended above. If a Threshold Factor (Tc Matrix) Is Available Some simulation programs do not have designated area features but permit the user to allow redevelopment anywhere via selection of a "redevelopment tag" or soft switch set to 1 or "Yes." In programs with this feature, the forecaster should consider using this feature to model tactical redevelopment and dispense with any need for designated areas. However, calibration of the preference functions, and of the T matrix, is now of critical importance. This applies to both the T matrix and the preference functions - they must be "dialed in" more precisely when modeling redevelopment. A simulation algorithm with this feature turned on is not nearly as robust with respect to slight set-up errors as one without this feature or with the feature turned off. When Using a Complete (Computerized) Redevelopment Simulation Forecasters using a simulation program whose algorithm utilizes time-tagging, redevelopment thresholds in preference factors, pre-programmed redevelopment events, and urban pole compression modeling have all the tools they need to represent accurate redevelopment in all its forms. These "complete redevelopment models" work with a diverse set of interrelated factors. Calibration is quite involved, and forecasters new to this level of modeling often find that keeping track of all the factors and their interactions can be very challenging. But when used correctly, the model is very representative of the entire process of metropolitan-area redevelopment growth and its competition with vacant area growth on the periphery of the region. The largest problem area in applying such a comprehensive model revolves around the interaction of urban poles and preference function/threshold values. With a number of exceptions (to be covered below), the preference function coefficients and the Tc matrix elements should be spatially invariant. In simpler

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words: the same set of function coefficients and threshold values should work just as well throughout the utility service territory being modeled, if they are set up correctly. However, if in the calibration phase it becomes clear that preference factor and threshold values are calibrated well in some parts of the service territory but not others, then one of two situations has developed: 1) The urban poles are not calibrated correctly, despite any appearance that they are. Despite all static diagnostics, the pole calibration must be revisited - it may be in error. This gets messy: preference factors need to be examined for calibration within each pole influence area. Poles where the preference factors work out well should be left alone. Those where it appears that the preference factors applied in the area show error should have their heights and radii revised. 2) The region consists of two or more very different, locationally segregated economies, so that the preference and threshold factors will not be the same. The portions of the region tied most strongly to the different urban poles are far different themselves. If differences are spotty and calibration error has any high-frequency spatial component or pattern at all, then (1) is the case. The user should apply a good deal of assessment before determining that (2) is the case, and there must be a very visible economic or demographic difference among sub-regions before this hypothesis should be acted upon. As an example of such a case, in metropolitan Houston, the east and northeast (petroleum refining, heavy industry) and west (commercial, service, high technology) sides of the city are very different "cities" that just happen to share the same urban core. Calibrate Urban Compression Last As a general rule, all dynamic features should be calibrated after all static features are calibrated. And among dynamic features, urban compression factors should be calibrated only when all other factors appear to be correctly set up. 19.6 SUMMARY Redevelopment is a constantly ongoing mechanism of electric load growth that accounts for a significant portion of the electric load growth in large metropolitan areas. It consists of four types, which occur simultaneously and intermingle their effects: 1. Urban core growth — "downtown" grows up, not just out. 2. Strategic redevelopment - large projects planned to change the entire urban fabric

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Chapter 19 3. Tactical redevelopment - small or short-term projects done for opportunistic reasons 4. In-kind redevelopment - replacement of old with new of the same type

How forecasters handle redevelopment will vary depending on the situation they face, and the tools, data, and resources they have. Perhaps the most important rule is to never ignore it, and to understand what and how even if that cannot be directly modeled. REFERENCES J. L. Carrington, "A Tri-level Hierarchical Simulation Program for Geographic and Area Utility Forecasting," in Proceedings of the African Electric Congress, Rabat, April 1988. J. L. Carrington and P. V. Lodi, "Time-Tagged-State Transition Model for Spatial Forecasting of Metropolitan Energy Demand " in Electricity Modern, Vol. 3, No. 4, Barcelona, Dec. 1992. C. Ramasamy, "Simulation of Distribution Area Power Demand for the Large Metropolitan Area Including Bombay," in Proceedings of the African Electric Congress, Rabot, April 1988. A.

M. Sander, Forecasting Residential Energy Demand: A Key to Distribution Planning," IEEE PES Summer Meeting, 1977, IEEE Paper A77642-2.

W. G. Scott, "Computer Model Offers More Improved Load Forecasting," Energy International, Sept. 1974, p. 18. H. L. Willis & J. Aanstoos, "Some Unique Signal Processing Applications in Power Systems Analysis," IEEE Transactions on Acoustics, Speech, and Signal Processing, Dec. 1979, p. 685. H. L. Willis & J. Gregg, "Computerized Spatial Load Forecasting," Transmission and Distribution, p. 48, May 1979. H. L. Willis and T. W. Parks, "Fast Algorithms for Small Area Load Forecasting," IEEE Transactions on Power Apparatus and Systems, October, 1983, p. 342.

20 Spatial Load Forecasting in Developing Economies 20.1 INTRODUCTION While a good deal of electric load growth occurs in the United States, Europe, and other "First World" countries, the majority of T&D system expansion expected in the first third of the 21st century is in countries with rapidly developing infrastructures. In many developing nations, it is common to see electric service extended into non-electrified regions, and entire new cities built, where previously there was no development. In both cases, forecasters and planners face a forecasting situation far different from those encountered in the planning of large cities and rural regions in countries with established and stable infrastructures. Electrification Projects Electrification involves extending electric T&D facilities into a populated region previously without electric service. The locations, types, and numbers of future consumers are largely known: the present inhabitants of the region. People in the region have been without power: they have a "latent demand." Some land-use growth will occur, but it will constitute a minor part of the forecasting equation compared to the impact of the incumbent population. The major uncertainty in forecasting revolves around how much and what type of electric usage these consumers want and can afford. Added to this are two issues nearly unique to electrification projects. First, electrification of a region drives its economic growth, leading to further increases in electric demand. Second, the thriving economy attracts people from nearby non-electrified regions, who are literally attracted by the "bright lights" 631

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and employment opportunity the region now has. Population grows, and with it, electric load. And, of course, a challenge the forecasters face is that while the consumers are already inhabiting the region, and while there is no doubt they want electric power and the benefits it can bring, none of the usual data sources — load readings, utility consumer billing records, etc., exist. This problem often calls for planners to be innovative and flexible in their approach to the forecast. New Cities Similar to electrification projects, but far different in planning and forecasting challenges, is a greenfield city project, in which a national government decides to build a new city in a previously rural area. Examples of such planned cities are Brasilia (Brazil) and Abuja (Nigeria). Such city projects are not uncommon in developing nations, but are virtually unknown in Europe and the United States. Both Are a Challenge But Only One Is Unique Despite its greater challenge, new-city forecasting is actually quite like forecasting load growth in developed metropolitan areas, only with a much higher level of uncertainty in all phases of electric usage. It is difficult due to the higher uncertainty, but nothing much can be done about that except to apply the best standard simulation methods with care. By contrast, forecasting load growth in the face of an electrification project is unique. The planners face a far different type of uncertainty, and they must model different driving processes for long-term growth than from those they model in other types of forecasting. New-city and electrification projects both typically occur in developing countries. Both involve extending electric service into areas currently without substantial electric facilities. But beyond these superficial similarities, their two forecast situations are very different and they present far different challenges to the load forecaster and planner. Table 20.1 summarizes the qualitative differences in the three types of forecast situations: developed area, new city, and electrification. Table 20.2 gives the author's estimation of the relative degree of difficulty encountered in forecasting each. In the author's opinion electrification and new-city projects are respectively about 25% and 50% more difficult to forecast than normal metropolitan or rural growth. This chapter will examine both electrification and new-city forecasting, focusing on what is unique to each type of situation and how the forecaster can deal with it. Section 20.2 will look at four specific elements of electrification forecasting, and the details of how each is addressed. Section 20.3 will then review an actual electrification forecasting case, done with two different forecast approaches. Section 20.4 discusses some key points about new-city forecast projects and outlines in detail the tools and procedures to address those types of situations. A summary of key points and guidelines concludes the chapter in section 2 0.5.

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Table 20.1 Qualitative Comparison of Types of Forecast Environments Type of Aspect of the Forecast Other Forecast Land-Use Usage Issues Known, but future Biggest concern in Existing Established but slowly changing. growth uncertain. planning is that an metropolis Growth in the Future land-use existing T&D infragrowth is mostly a per capita usage structure exists and may function of a large be difficult and costly is very expensive number of factors, to handle (see ->) to enhance. Mistakes local events, and are very expensive. regional conditions Growth is dominated by land-use additions and change that involves urban interaction and is difficult to analyze and forecast Electrification Land use exists and of a region there is very little uncertainty about the what and where of usage patterns. New City Project

Land use is planned and may be well controlled, but the actual pattern that will develop is very uncertain.

No local precedent exists for usage statistics by class. Great uncertainty

Impact of electricity on the local economy In-migration due to increased attraction of the region.

Established. Planning situation is Will be similar to mostly Greenfield. class-usage patterns Forecasting worries are in other nearby cities mostly related to //"the (subject to weather). expected timing and locations of land use will develop as planned. In-migration due to increased attraction.

Table 20.2 Comparison of Uncertainty by Forecast Situation Forecast Uncertainty Faced In Type of Importance of (out of 100%) Difficulty Land Use Usage Study Area M.D. E.U. L.D. E.E. In-M 1.00 .85 .15 Established, 70% 20% 5% 5% and growing Electrification of region

1.25

.25

1.00

New city project

1.33

1.15

.35

5% 15% 30% 25% 25% 65% 20%

5%

5%

5%

M.D. = metropolitan development interactions, E.U. = end use, L.D. = latent demand and appliance acquisition, E.E. = electrification impact on economy, In-M = In-migration effects

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20.2 MODELING LOAD GROWTH DUE TO LATENT DEMAND One forecasting challenge faced by utility planners in developing nations is forecasting latent demand. A good deal of the electric load growth in such nations is due to the extension of the electric grid to rural villages and towns, communities which previously did not have electric power. As soon as (or even in anticipation of when) these areas have electric power, homeowners, businessmen, and municipal leaders obtain electrical appliances and equipment to satisfy an existing but previously unserved demand for power - a latent demand. After electrification they continue to obtain electrical appliances until they "catch up" with the demand level common for their consumer class. The process of load growth in such situations is the exact opposite of that seen in most of the circumstances covered in the previous 19 chapters, and at which normal trending and simulation methods are aimed. Developed areas. The majority of growth comes from new consumers entering the area. These are homes and businesses that were not previously in the area. They relocate or build in this area with a "stock" of electrical appliances already in hand. Developing areas. The majority of growth occurs due to homes and businesses already in the area. Over time, they acquire a stock of electrical appliances. The process of growth is different. And so are the results. Figure 20.1 shows the annual peak load level (April) for two villages, one in central Africa, one in central American, during the years after electrification. The growth trend is

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Figure 20.1. Left, annual peak electric demand for an agrarian area (series of small villages) in central Africa, for the ten years after electrification. Right, similar data for an agrarian/fishing region in central America, for the eight years after its electrification.

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16 12

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36

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Figure 20.2 Current developing-area load growth does not appear to be that different in pattern from that seen during electrification of the U. S. This shows load growth in the nine years after electrification began in a rural electric system built in Kansas in 1932 up until WWII. Load growth rate during 1932 - 1936 is very high because the system is still being built and extended to new consumers.

not that different from electrification development in the U.S., and elsewhere, when electrification occurred there. Figure 20.2 shows growth of load following electrification in a rural area of the United States, displaying a broadly similar type of trend. While it is tempting to identify this behavior as part of an "S" curve (see Chapter 7) with a very long slope, it has a substantially different cause, and in detail the effect is different, as will be discussed below. General Behavior: Four Root Causes Latent demand load growth is generalized as a jump of some magnitude (immediate load) and strong but linear growth over more than a decade thereafter, as consumers and businesses gradually acquire electrical appliances and equipment. Figure 20.3 shows some of the characteristics of the behavior as they are generalized into a "developing" area load growth model. There are four phases. The immediate jump in demand when electrification is made indicates that there were electrical appliances acquired simultaneously with the extension of the electric grid into the location. Generally, municipal and community functions such as the police department and local health clinics had electric load supplied by distributed generation. Upon electrification, they switch to what in most cases is the lower cost source of power from the grid. In addition, both successful local

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industries and prosperous homeowners often had their own generation, and they too switch to the newly extended grid. Rapid growth due to acquisition of appliances and equipment by the rest of the population is the driving force behind load growth in the years following electrification. Homeowners obtain the various appliances they want to improve the quality of their life. Businesses install labor-saving equipment. Economic factors control this trend: how long will it take to acquire the appliances that homeowners and businesses want? A vigorous economy and the opportunity to work and earn money fuel rapid load growth. A recession, or widespread unemployment, slows the trend. It is not uncommon to see an initial delay in this phase of growth, due to consumer skepticism and uncertainty about the permanence of the electric supply, about how to buy and the affordability of power, and a general unfamiliarity with electrical appliances (dotted line in Figure 20.3). Economic growth spurred by electrification. Electricity does make things better. Regions with it produce more at less cost. Therefore, over time it spurs noticeable economic growth which eventually translates to higher loads. In-migration fueled by increased attractiveness of the area. In many developing countries, there is a constant "flight" from rural areas into the cities. These large and unpleasantly crowded cities are, despite all their problems, more attractive to people seeking to improve their quality of life than the rural areas they leave. One of the contributors to this attractiveness is the availability of electricity and the facilities and services it enables. Once an "electrified community" has built up a base of usage and infrastructure around electricity, if significant areas of non-electrified rural area lay beyond it, it will become a sought-after area for re-location. It will see substantial and continuing growth in population, for this reason.

1 2 3 4

5

6

7 8 9 10 11 12 13 14 15 16 17 18 1920 Years After Electrification

Figure 20.3 The generalized load growth behavior of an area after electrification.


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Forecasting Latent Demand Load Growth Since the load growth process is different from that in developed areas, one would expect that the forecasting process would also have to be different. It is. Perhaps the best way to contrast the two types of forecasting is to look at the information that is known and unknown to the planner. There is a great difference in what is known: Developed area forecasting Future consumer types, locations, and counts are not precisely known. Per capita consumption and appliance preference is well understood. Undeveloped area forecasting Future consumer types, locations and counts are fairly well known. Per capita consumption and appliance preferences are not. In other words, the information base with which the forecaster can work and the target which he is trying to predict for the future are largely reversed. The consumer locations of the "new" load are known. Their usage characteristics are not. The forecast falls into four categories, three addressing each of the three phases of growth shown in Figure 20.3 and a fourth longer-term issue. After reviewing these four categories, an example forecast addressing them will be given. Forecasting considerations for cause 1 - the initial jump The jump in load upon initial electrification is best predicted with an inventory of existing and immediately expected loads. Planners can query community leaders and survey local businesses to determine existing loads and any plans for new loads to be made simultaneously with electrification. In some cases, electrification is being coordinated with the owners of local distributed generation sources, so this is quite easy to do.' The locations of these existing or immediately anticipated loads are known, so the "forecasting" of the spatial aspect of this growth can be done precisely. Often, the temporal aspect of demand can also be determined through analysis of past usage patterns. However, the author's experience is that considerable uncertainty remains, and the best forecasts of daily load curve shape are at best estimates. The reasons are that the consumers often do not really know their electrical usage patterns. In addition, usually after electrification many institutions and businesses change their usage patterns quite noticeably. Those municipal or state agencies often turn their distributed generators over to the electric utility upon electrification and work with them to integrate those facilities into the new electric system. Private owners usually keep their generators, maintaining them as backup units for use during outages.

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Forecasting considerations for cause 2 - acquisition Again, an inventory is a good starting point, in this case, a list of the desired appliances and equipment, by class of consumer. This provides something like a per capita horizon year load level for each class. Economic analysis of the speed with which each class can acquire these appliances then provides an estimate of the rate of load growth within each class. Consumer confusion and uncertainty Anticipating if and to what extent consumer attitude - trust, uncertainty, or confusion - will affect the acquisition of appliances and equipment is usually the most difficult part of forecasting in these situations. Situations vary widely, even within one country or region. In some communities electrification is greeted with great anticipation, and there is a rush to buy appliances as soon as or even before electricity is available. In others, there is some measure of hesitation in accepting electric power and using it. Reasons are: Distrust - installation of electric service may be viewed as intrusive. There may be cultural biases against cooperating with government and large institutions, or a feeling that electric service will put users at some obligation to the government (beyond paying their bill). Skepticism - Consumers may doubt if the electric system will work well and be safe, and if it will prove a viable business, or fail, after which power will no longer be available. Uncertainty and confusion. Consumers can have doubts about electric equipment, and concerns about if and how they can afford the power. Lack of money - it takes time to save money to buy the appliances consumers want. Many people will not start saving until they see proof that the electric system is in place and works. Predicting this aspect of latent demand growth remains something of a judgement call. However, recent experience in electrification of any nearby communities provides the best indication of what to expect. If that went well from the consumer's perspective, word-of-mouth alone will allay most of their doubts. Forecasting considerations for cause 3: electrification impact Economic development, per se, is generally included in the overall macro forecast (corporate revenue forecast) done by a utility. In a latent demand forecast, the effects of electrification are generally the second greatest cause of load growth (after acquisition). They usually "kick in" after about five to eight years of electric usage, and the local economy grows. Incomes rise, acquisitions increase, and consumption of electricity rises. Generally, this is handled by


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adjusting per capita peak load values upward repeatedly. An example will be given later in this chapter. Forecasting considerations for cause 4: in-migration growth Few other aspects of T&D planning and forecasting are as difficult as forecasting the results of economic and demographic competition among different regions and cities. Beyond all the tangible factors involved, such as economic opportunity and space for growth, etc., there is often a large measure of personal preference and cultural character to the mechanism driving growth. As a result, even the most comprehensive forecasts are often incorrect. The improved attractiveness of an area after electrification tends to attract population from surrounding areas. If the region around the recently electrified community is without electricity or infrastructure, the community will be quite appealing, and growth is inevitable. If on the other hand, the community is among the last in its region to be electrified, or if there are no nearby areas from which people can migrate, then this type of growth may be minimal. Generally, polycentric activity center models akin to the urban pole models covered in Chapters 1 0 - 1 4 will give good results if slightly modified in application. As shown in Figure 20.4, the pole models attraction is stronger nearby and weaker farther away. The two key aspects of such a model are: Population: the portion of local populations that would want to move if the distance were small Distance: the distance from the study area beyond which the attractiveness is zero.

Figure 20.4 Migration model uses a type of urban pole to represent attractiveness as a function of distance. People nearby are more likely to migrate than those far away.

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Generally, such demographic movement models represent both the portion of population that wants to move and the distance these people are willing to migrate, as functions of the economic advantage gained by those who do move. If the advantage is large enough (i.e., 10 times earnings or quality-of-life potential), very nearly all of the population would move, and all of it would be willing to move "very far away." There are historical examples of such immigration, including the migration to the United States during the 16th through 19th centuries.2 Modern examples from which to model migration models are available around the world, including the "illegal" immigration into Venezuela from Columbia and Ghana, the migration of workers to Germany from Asia and Africa, and migration, legal and illegal, into the United States, Canada, and the U.K. Migratable Population Generally, the migratable portion of a local population is considered to be that portion that is young (16 - 350), relatively healthy, and not possessed of substantial real property (dwellings, businesses, farms) in their present area(s). In a region with a very poor economy and a young average age this segment can represent a majority of the population. Migration Distance People will travel thousands of miles, and spend all that they possess, to migrate to places which have a substantially better quality of life and income potential. Figure 20.5 shows the function that the author uses to estimate "attraction radius" for models like that shown in Figure 20.4. There is no body of data or evidence to substantiate this model, but it has worked well in nearly two dozen forecasts involving in-migration, done over three decades. Model Application Migration models are generally applied as a set of urban pole attractors. Each cone-like function is centered at the population center (city, port) it represents, with its height proportional to the attractiveness of the location (Figure 20.6). Usually, attractiveness is modeled as some function of the local economy, measured in gross annual product, aggregate payroll, or average income, used as the "height." The author has had the best success using the average household income as the "height." Radius of each pole is based on its advantage over conditions in the target (migrant) population.

2

Illegal immigration to the United States from Mexico is an example, but not an easy one to use as a basis for generalization, because of the illegal nature of the migration and the impact that has.


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Figure 20.5 The distance at least a small portion of the population would travel to gain an economic earning advantage, as a function of that advantage. Distance here is measured in travel days. Three years is about the total time that some immigrants to the U.S. spent relocating during the 19th century, when economic advantage gained was about five to one.

Figure 20.6 Application of a polycentric activity center model for allocation of migration populations. See text.

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Up to this point, the application of these poles is identical to that used in standard simulation models: a polycentric activity center model in which both height and radius are functions of local conditions at the center. However, the use of these pole functions now departs from the way they were applied in the standard simulation land-use model. For migrant allocation, each of the competing areas gets a "share" of the migrant population proportional to its share of the total of all poles affecting any one area. Specifically, the influence of any one pole on a specific location, x, y is: IP (x,y) = Hp x Dp(x,y) /Rp = 0

if Dp(x,y) > Rp otherwise

(20.1)

Where p indexes the poles, p e [1 . . . P] with P the number of poles, and Ip(x,y) = influence of pole p at location x, y Dp(x,y) - V( X p- x ) 2+ ( Y p-y) 2 ) Yp = x coordinate of pole p Yp = y coordinate of pole p Hp = height of pole p Rp = radius of pole p The share of immigrants from this location, for any specific pole z, is p Share of immigrants = Iz (x,y)/(^ Ip (x,y)) P =i

(20.2)

Figure 20.6 illustrates this allocation. The dark shaded area shows that overlapped by two poles. At the small square area (shaded black) these measure respectively 38 and 92. Migrations in this location will be split 38/130 and 92/130 among these two attracting areas. The total immigrant population attracted to a specific location, z, is therefore:

X Y

Immigrant population

=

P M x

M V ( >y) ( Iz (x,y)/^j Ip (x,y))y J J p= i

(20.3)

where M(x,y) is the migratable population at location x, y The electrification forecast case to be studied in section 20.3 provides more detail in showing how this approach is applied.


643

20.3 EXAMPLE LATENT DEMAND FORECAST This case is an actual forecast done in 1987 and for which more than a decadelong post-forecast history therefore exists. It was forecast using two spatial methods whose results were merged, as will be described later in this section. The spatial database was developed by ground survey (clerks walking streets and counting houses and stores) and with "aerial" photographs - literally by taking photos of the area from a small plane flying a few hundred feet overhead. Figure 20.7 is a map of the Colline Noir district (from the French for Black Hill, although the name as used locally is slightly misspelled) of towns and villages in a river valley between low mountain ranges in central Africa. The shaded area comprises roughly 400 square kilometers. Most of it was originally covered with forest, but the bottom flats along the river (including most of the shaded areas) were cleared for farming in the first half of the 20th century. The local economy is based on lumber and agee, a sisal-like vine harvested from the surrounding forest canopy and used to make rope and netting. There is also a small amount of grain drying and shipping from farms on the mountain plateaus, but this accounts for only a minute amount of the local gross annual product. The plains to the east constitute a 6400 km2 wildlife preserve (for rhinoceros), so there will never be significant growth in that direction. The figures shown in Table 20.3 were 1987 estimates (there was no national census). At that time the area had no electric system, but the Interior River Electric Authority (IREA) planned to extend electric service into the region by late 1988.

Figure 20.7 Colline Noir in 1987. Lightly shaded area is settled at low (10 persons/km2) density. Darker areas are more developed. The river is the region's predominant transportation system. Letters and arrows indicate features discussed in the text.

644

Chapter 20 Table 20.3 Colline Noir Region Factor Value Population of Colline Noir 100,000 24 Average age Population growth rate 1.7% 13,800 Dwellings 1,141 Commercial buildings 91 Industrial/shipping Schools and public buildings 62 6,800 School students Local economy based on agee, lumber, grain $13,900,000 Gross regional product (annual) Average household income (annual) $1,393 National average in nearby electrified areas $1,700

Table 20.4 Existing and Immediate Loads and Distributed Generation - kW Consumer

Location

Existing Provincial & River Police HQ Health Clinic Riverport dockyards Hotel/Waystation Radio Station Police Police Grain processing, Storage, Ship Agee Enterprises Factory Plantation Plurtan Res Plantation Welbauton Totals Existing

Installed

Peak

Gen.

A A A A A B F 30 D E C

90 50 275 1 5 10 6 10 40 120 60 687

45 50 150 1 5 10 6 10 45 70 65 395

40 35 200 1 15 3 2 diesel 45 70 75 457

G A A A A-F A-F

20 15 +25 +65 125 138 388 1075

20 15 +20 +56 125 138 191 836

379 501

Suspected & Assumed Additional

1000

250

250

TOTAL FORECAST - INITIAL

2075

1086

751

Immediate River Grain Regional Mechanical School Health Clinic Hotel/Waystation Comm.-Ind. declarations (78) Homeowner declarations (3 1 5) Total immediate Known Totals, Initial Load

Type diesel diesel diesel solar d&s solar solar diesel diesel diesel


645

Estimating the Initial Load Upon Electrification Table 20.4 shows the loads in the area prior to electrification, at locations indicated by the corresponding letters shown in Figure 20.7. A total of 687 kW of electrical equipment was installed, with an estimated coincident peak demand of 395 kW. More than 500 kW of distributed generation units, mostly diesel, provide the power. An additional 125 kW of installed load was expected. Beyond this is 388 kW of "declared" load. In advance of electrification, IREA sponsored an "early sign up" program including assistance with and discounts for electrical appliances and equipment, and rate incentives, for consumers who would order utility service in advance of electrification and give proof of equipment purchase or intent to purchase. This yielded a further 407 declared consumers. The records from this program provided a body of information from which to estimate all additional load (1000 kW, 250 coincident. In total, the inventory estimated 2,175 kW of initial load, with an estimated coincident peak of 1075 kW, from a total of over 1,000 consumer sites. Five-Year Growth Projections Due to Appliance Acquisition Table 20.5 shows the eight classes used to categorize consumers and estimated counts for each. Note that households have been stratified by income, income categories being defined so that the classes have equal populations. Under income the table lists household income for residential consumers, and annual gross revenue for commercial and industrial consumers. Class total's give some indication of the importance of the class in the economy of the region and its potential for purchase of electrical appliances and electric energy.

Table 20.5 Consumer Categories in Colline Noir Class Number Income 3400 Household 1 $400 Household 2 3400 $650 Household 3 3400 $1,129 3400 Household 4 $1,750 Wealthy households 150 $20,000 Private Comm/Ind. 2800 $1,500 Commercial 210 $9,000 Industrial 53 $68,000 Large industry 6 $1,707,000 Public/Institutional 42

Class Total $1,360,000 $2,210,000 $3,838,600 $5,950,000 $3,000,000 $4,200,000 $1,890,000 $3,604,000 $10,242,000 -

646

Chapter 20 Table 20.6 Household Priorities for Acquisition of Appliances Ranking Appliance

Load - watts Coinc. Peak

Initial !S

Annual $

1

Cookplate

400

200

$22

$16

2

Television/stereo

125

90

$115

$14

3

Fan

75

70

$25

$18

4

Lights

200

15

$25

>$8

5

Refrigerator

450

120

$200

$59

6

Air conditioner(s) 1200

800

$212

mile wide "close enough to highway factor."

J.

Close to railroad = 1 / 3 mile. The very dark shading here along the railroad is all within 1/3 mile of the rail trunk lines. Thus the diameter of that factor with respect to positive development of industry is 1/3 mile.

Calibration requires that the planner play "data detective" with care and attention to detail. In the author's experience it is the one aspect of simulation that is best learned through hands-on experience. By identifying and studying development patterns in only one year of aerial photos, satellite images, or landuse databases, the types of clues described above can be identified and used to determine the diameters, profiles, and relative weighting of the preference factors.

Using Spatial Forecast Methods Well

691

The Iso-Preference Score Rule One particular rule is quite useful in determining the relative values of the coefficients: All recent similar changes in a class should have the same preference score. One example will suffice to illustrate this concept. Figure 21.4 shows a smaller version of Figure 21.3 in which the areas of heavy residential growth in the last two years have been identified. All such areas, if being forecast for the past year, should have had a preference score that was very similar (high enough to get them selected for growth). Thus, for calibration purposes the planner can assume that the areas at A and B both have the same overall preference score. Area B, farther out but near the highway, and area A, ten miles closer in but fully three miles from the highway, are equivalent. This means that the weighted difference between being three versus one mile from a highway (close to highway proximity factor) is equal to the weighted value of being ten miles closer to downtown (central urban pole). By similar analysis of recent growth, finding and analyzing areas that grew simultaneously but had different characteristics, the relative values of factors can be identified.

Developed areas Developing areas

Figure 21.4 Illustration of the preference coefficient computation as described in the text. Recent residential growth had occurred in both areas A and B, which implied that their preference scores are similar. This fact and other similar observations about local growth were used to determine the values of the PJ C coefficients.

692

Chapter 21

identifying Missing Factors or Mismatches Figure 21.4 is based on an actual case where a factor was missing, illustrating one of the first error issues in the calibration of the original ELUFANT model at Houston Lighting and Power (1977). The program's starting calibration values forecast only residential growth in the areas marked A in Figure 21.4, when applied to back-cast growth for the prior two years. It missed entirely the roughly 25% of residential growth that occurred in the areas marked B. What it missed was obvious upon inspection. Those areas marked B were far enough north of the coast to lie in the coastal forest. Homebuyers looking north of the city could find homes on treeless lots about fifteen miles out from downtown, or forested lots at about twenty-two miles out. A portion of homebuyers were choosing each option. However the forest, and its impact on home buying, had not been represented in the database or the simulation factor setup, so the model's forecast did not reflect this. The first lesson learned by comparison of forecast to actual was that trees were important in determining residential consumer development locations. A forestation map was obtained, coded into ELUFANT's multi-map database, and used as a local factor. This was a binary map with a value of 1.0 signifying the area was forested, while 0 indicated it was not. Residential growth was occurring in both areas A and B. Therefore, both areas had the same preference scores with regard to the way the simulation modeled and applied preference scores. This meant that the advantage of "Trees = true" was equal to the difference in the weighted value of the urban poles in the areas marked A and B in Figure 21.4 (i.e., equal to the urban pole's decline over seven miles farther out from the center of the city). The residential preference factor for trees was adjusted to reflect this observation, and the program then forecast all residential growth correctly. The Stationary Process Rule As a general guideline, the process of growth of a region should be a stationary process. This means that the process doesn't change even if the city or region does. Specifically, statistics on the process of growth should remain constant over the simulated time modeled by the program. This rule can be applied to test whether the program's overall dynamics of growth are adjusted correctly. For example, in each iteration (simulation of growth over a year or more period) a simulation growth forecast program will assign growth to specific small areas based on the class-by-class preference scores. Each candidate area of growth is evaluated with respect to how well it matches the needs of each class (e.g., residential) based on its preference scores, computed on an arbitrary scale, usually in percent of "perfect" match to each class's needs. The program then


693

selects the amount of small areas it needs for growth of each class (from the global model) and starts with the highest rated small areas and works down from there. Those with high scores, perhaps all small areas above 92% preference score, are assigned growth. Those below that threshold do not see any growth. The preference threshold limit - the value below which no growth is assigned should not change dramatically from one iteration of the program to the next. Suppose that a forecast is doing ten iterations, each representing one year of growth, as shown below, with the resulting iteration-by-iteration threshold values shown: Iteration: 1 2 3 4 5 6 7 8 9 1 0 Years o f Growth: 1 1 1 1 1 1 1 1 1 1 Threshold: .82 .79 .76 .74 .72 .69 .66 .64 .62 .60 Something is wrong, because the threshold values steadily decrease. Many planners make a mistake by assuming that this trend is what should happen. They reason that as the program simulates growth it will use up all the high-scoring small areas first (true) and then have to dip down into those with lower preference scores (untrue). Thus, the trend of gradually decreasing threshold values shown above is what should happen. But if the program is set up correctly, it will "restock" the supply of really high-scoring vacant land in each iteration. For example, generally the biggest factor contributing to residential preference is the "close to residential factor." During one iteration the program may use all the vacant small areas that score really high in this regard. But it is creating new residential small areas, which mean vacant areas that were previously not close to residential are now nearby new residential areas. Their "close to residential factors" have just increased. The supply of developable land that scores high increases as a result. Other concerns being equal, if the process of growth is modeled well, the supply of high-scoring vacant areas will be restocked at just about the rate that it is depleted. In every iteration the threshold value for the selection will thus remain about the same. A reasonable trend would be something like: Iteration: 1 2 3 4 5 6 7 8 9 1 0 Years o f Growth: 1 1 1 1 1 1 1 1 1 1 Threshold: .72 .71 .67 .69 .68 .68 .66 .67 .68 .68 A permitable exception is if the iterations are not each representative of the same amount of growth, as would happen with an often used method of making the latter iterations in a forecast represent longer periods. For example, suppose a program has been set up with ten iterations as shown below. Iteration: Years of Growth: Growth % Threshold:

1 2 3 4 5 6 1 1 1 1 1 2 2.1 2.1 2.0 2.0 2.0 4.1 .82 .79 .83 .83 .82 .74

7 3 6.2 .70

8 5 9.8 .68

9 10 5 5 9.4 9.3 .66 .66

694

Chapter 21

This indicates what is probably a good calibration and a stationary process. The first five years certainly indicate that, and calibration should probably be left without further adjustment (unless other diagnostics indicate a need). Very little can be inferred from the absolute values of the thresholds - a value of .92 or a value of .68 may be equally valid. These values are on an arbitrary scale and depend on the factors and coefficients and the growth rate - a forecast with a lot of growth occurring in each iteration will dip lower into each iteration's stock of available growth room. Thus, there is no set level for the threshold that is "best." Generally, however, planners should be concerned if the threshold is much above .90 or below .66. If the numbers dip below .66, the program may be modeling iterations that are too long. In the example above, the author would change the final three iterations (representing 15 years) to five iterations, each modeling only 3 years of growth. Diagnosing temporal trends in threshold values Small deviations from iteration to iteration are tolerable if the trend over several iterations is not towards an overall change. Thus, something like the following trend would be satisfactory. Iteration: 1 2 3 4 5 6 7 8 9 1 0 Years o f Growth: 1 1 1 1 1 1 1 1 1 1 Threshold: .79 .78 .74 .72 .73 .72 .74 .77 .71 .76 However, if the trend drops gradually and steadily in each iteration, something like that shown in the example below, then the factors aren't adjusted well. Iteration: 1 2 3 4 5 6 7 8 9 1 0 Years o f Growth: 1 1 1 1 1 1 1 1 1 1 Threshold: .72 .71 .67 .63 .58 .56 .52 .48 .46 .43 This very steady trend downward gives very little clue as to what is wrong, but something is not calibrated correctly. A slow consistent trend often indicates problems in surround rather than in proximity factors which need adjustment (and there are rare exceptions to that rule). Other than that there is no real clue in this. Planners should check spatial patterns of growth for additional clues. If the trend has an accelerating rate of decrease, the first suspect is any data that needs to be updated manually in each iteration. This is usually an indication that new highway data or similar manual entry changes have not been entered at an appropriate rate. Such a trend would be Iteration: Years o f Growth: Threshold:

1 2 3 4 5 6 7 8 9 1 0 1 1 1 1 1 1 1 1 1 1 .92 .90 .90 .86 .82 .77 .70 .62 .52 .45


695

This indicates that, up through the third iteration, there was enough land close to highways, but that after that the better land had been used up and not enough highways were input for that year's growth. The stock of highways needs to be replenished.1 If the trend is toward increasing values, even at just a slight rate - then the opposite is true, there are too many highways or other new attributes being added in each iteration. Exceptions There are cases where the values will, and should, decrease in each iteration. The most common is when growth is being modeled in a region that is "running out of room." Marginal land must be developed when preferable land is all developed, and as a result preference threshold values should drop. One situation where this happens, but should not be allowed to occur, is in cases where the growth in a service area moves outside the utility territory. As an example, a forecast of City Electric Department's T&D loads might study only those areas inside the city limits (its service territory). Yet, as the city nears complete development, growth moves outside the city limits. If City Electric planners model only their service territory, the preference factors in that area would drop as marginal land had to be selected for growth. But that would be the wrong case. The only acceptable way to model growth in such a situation is to represent areas outside the service area, so that the competition for growth in and out of the service territory is properly modeled. Chapter 11 's example discussed this with respect to the mythical Springfield city limits and the growth of factory-caused scenarios. Section 21.6 will discuss this. If, however, there was no developable land outside those city limits (perhaps the city is on an island or otherwise landlocked by areas off-limits to growth) then one would expect that as the available, high-scoring land is absorbed by growth, land less suited to each class must be selected. Thresholds would drop. 21.4 TRICKS AND ADVICE FOR USING SIMULATION PROGRAMS WELL This section covers a number of "tricks," or ways to apply simulation-based spatial forecast programs well. These are based on using the features included in most simulation-based spatial load forecast programs. Planners using any one particular program may have to interpret the steps described here into the format of operation used in their program. However, what is covered here is appropriate for all simulation-based programs the author knows. Most simulation-based programs have a feature which permits the user to perform spatial arithmetic and apply spatial functions on a customized, step-by1

Or, perhaps there really is no more road and highway construction because of real limitations on further growth of construction. In such a case the process of growth will not be truly stationary. This does happen.

696

Chapter 21

step basis. In some programs this permits the user to interactively perform spatial analysis one function at a time. In others, the user creates small macro files which are then executed to perform a series of customized steps. Either format permits the user to add, subtract, multiply and scale various data levels, to apply various logical, comparison and extraction functions, to "filter" data maps by applying surround or proximity functions, and to perform other basic steps used in the algorithm itself. Many of the functions covered below are implementable using that method. Customizing Urban Pole and Preference Function Shapes Using Standard Linear (Cone) Functions Often, a forecast situation requires a "flat" urban pole. There is an attraction or siting requirement for proximity to a particular center or amenity, but not one with a linear attractive function of distance. Instead, it is much more digital: within a certain distance the factor is satisfied, outside that distance the factor is not satisfied. But many spatial simulation programs can only model all urban poles as "cones" with a user specifiable location, radius, and height - the value that decreases linearly with increasing distance out to the radius. A good approximation of a pancake function can be formed with two conepoles, one with a negative weighting factor, as shown in Figure 21.5. This results in a "pancake" with a height of one for a radius of nine and a "diagonal edge" out to radius ten. Figures 21.5 and 21.6 show other such "tricks." The ease with which such pole profiles can be created is one reason that most simulation programs have been written to handle many more poles and proximity factors than are usually needed, so that two, three, four or even more can be "stacked" together to shape the factor versus distance function as needed.

Distance

Figure 21.5 A "flat" urban pole can be created by using two conic urban poles. Top, cross-sections of the two poles. Bottom, 3-D view of the two, and their result.


697

Figure 21.6 The pancake function is not the only customized shape that can be produced using combinations of urban poles.

Ax value

•

Figure 21.7 Top, applying a pancake function of radius 12 twice in successive steps is equivalent to applying a decreasing-value function of radius 24 one time. To perform the operation, the pancake function is applied to the base map (i.e., that upon which the factor is being applied), then the factor is applied again to the result from that first application. The result is equivalent to applying one factor with a nearly linearly decreasing cross section and a radius of 24. Bottom, a function that creates a "times two radius" pancake function can be implemented through two applications of a "shell" function, then subtraction of the values in the base map times a scalar, A, equivalent to the sum of the shell function cellular values.

698

Chapter 21

Working Around Surround and Proximity Factor Limitations Some spatial simulation programs limit proximity and surround factor functions to within rather narrow maximum radii, typically around three miles, which is more than sufficient in most cases. Others limit the profile (cross-section) to only a pancake function, and/or only a linearly decreasing weighting function of distance. Most of these limitations can be worked around using clever application of functions. Stacking (adding) factors Adding factor result maps created from the same base (i.e., with factors applied to the same map, such as railroads) can work around limitations a program may impose on the factor profile. The "tricks" shown for urban poles in Figures 21.5 and 21.6 can be applied to surround and proximity factors, to work such limitations. For example, two "close to commuter rail hub" factors can be applied for residential in a large city, one with a weighting of 1.0 and a radius of }A mile, the other with a weighting factor of —1.0 and a radius of % mile. They result in a "ring function." Such a combination models a commuter hub proximity preference that says, "I want to be close to but not immediately alongside a commuter hub" even if the program will not allow factors of such complex profile. Widening radius and shaping profiles using sequential application Users of programs that impose a limit on factor radius can generally work around this by applying factors sequentially. There are two situations in which users may need to apply factors with a wider radius than a program limit permits. 1) In special cases where a factor must extend beyond the limit permitted by the program. Such situations are rare with respect to actual proximity and surround factors. However, a user who wants to create distributed urban poles (see Chapter 14) using a program not written to provide that feature can work around that by computing them as "very wide" surround factors (radii « 15 miles). 2)

When a grid-based map format program is being applied at a high spatial resolution beyond that for which the program was designed. For example, suppose a program might have been designed around application at 40-acre resolution (grid cells V* mile to a side), and written with a limit of 12 cell widths (3 miles) in the application of all proximity and surround factors. Yet the user wishes to apply this at 1/8 mile resolution, which means 3-mile proximity factors that now require 24-cell radii.


699

Applying two 12-cell radius pancake functions in succession (Figure 21.7) can produce an acceptable approximation of a 24-cell-width linear-weighted surround function. Figures 21.6 and 21.7 also give examples of other sequential operations and the results they produce. A caveat: know the basis of factor computations A few simulation-based forecast programs and some spatial data systems used to implement simulation apply search, surround, and proximity factors as square not round, functions. A surround factor computation for "all residential within 1A mile" adds up all residential in a square of X and Y dimension of 1 mile, centered on the point of analysis, instead of computing it only within the radius of 'A mile, as illustrated in Figure 21.8. Similarly, proximity factors for the nearest railroad may find a railroad at distance x=l, y=l, and return that as within one mile (actual distance 1.414 miles). This square rather than circular application is a result of shortcuts made to simplify programming and increase analytical speed. Although such approximations are becoming rarer as GIS and spatial database programs become more sophisticated, they are not uncommon. What is common is that any approximate nature of the function computations may not be documented where it is easy to find. One commercial spatial simulation load forecast program the author has seen uses square functions, but has circular functions in illustrations of its application.

Figure 21.8 Correct implementation of a surround factor in a grid (left) and an approximation used in some simulation programs (right).

700

Chapter 21

In other cases the issue is more complicated. One popular spatial database manipulation and facilities data system has two different ways or "levels" at which it can be "programmed" to perform surround, proximity and search functions. One implements square functions, the other implements circular functions. Although that distinction was documented in the user's guide, it is only explained in the details about implementation, and not obvious even to a person studying those instructions. Another issue relates to the resolution of computations on polygon programs. Some systems work with only the center of area of a polygon, computing all attributes for that location. Others analyze corner points or take other computational means to estimate area, distance relationships inside polygon areas. Figure 21.9 illustrates one such shortcut and the approximation it renders. Contrast enhancement for factor, pole, and other searches Often, planners playing "data detective" can create "custom" maps using the interactive or macro features of a program, in order to search for characteristics such as the locations of urban poles, or in order to diagnose factors and calibration. A "trick" in contrast enhancement is useful when performing studies like those described earlier where one is searching for specific small locations using a low-frequency function like an urban pole. Raising a map to a power (e.g., cubing it with the third power) can increase contrast enhancement. For example, the result maps (e.g., step 4 in method 1 for urban poles discussed earlier) can be cubed, or raised to even a higher power on a point-bypoint basis, to enhance visibility of maximum points. This technique enhances contrast and makes the highest value points stand out more visibly in displays, as shown in Figure 21.10. 21.5 PARTIAL-REGION FORECAST SITUATIONS Very often planners must deal with the forecasting and planning of cities, towns, and regions that are cut through by boundaries or barriers which are "artificial" from the standpoint of the forces and interactions controlling regional load growth, but which are very real to the planners. A classic example is a "border city" such as Laredo-Nuevo Laredo, which straddles the U.S.-Mexican border. Laredo, served by AEP West, is the U.S. side of a metropolitan area stretching across the U.S.-Mexico border. The growth of Laredo, the U.S. side, cannot be fully analyzed unless it includes some assessment of Nuevo Laredo, the Mexico side, particularly since a majority of the "metropolitan area population" resides in Mexico. Similarly, planners at CFE (the electric utility serving most of Mexico) cannot neglect Laredo, in the U.S., when forecasting Nuevo Laredo's load growth, because the U.S. portion of the metropolis contains a majority of some types of the region's commercial development.

701


School |

|

One Mile Figure 21.9 A 170-acre parcel of land near the corner of Tryon Road and Piney Plains Road in Gary, NC, shown as it is modeled in a polygon format. To gain speed, factor computations in some programs would treat this polygon's attributes as all located at its center of area (marked with an X). Such an analysis would conclude that all of this land was within 1 mile of a nearby school, when in fact only about 45% of it is. The best work-around to the inaccuracies this approximation introduces when using such a program is to split the area into a number of smaller parcels.

Figure 21.10 Cubing a map on a point-by-point basis will improve contrast and make maximum points stand out. Here, the filtered result of method 2 (Figure 21.2) is cubed, greatly improving the detectability of local maxima. This is useful in pole detection as well as numerous other applications in setting up and calibrating a simulation program.

702

Chapter 21

Service and Study Boundary Situations In these situations the forecasting of load growth in a vitally important area of the utility's service territory is complicated by the following issues: •

The utility service territory does not represent the complete "whole" of the region.

• The utility service territory does not represent all of the area that is competing for growth that might occur - growth trends in this area could move somewhere else. •

Load growth in the utility area is influenced, perhaps driven by, and interacts with population, events, and development outside the utility service area.

•

Detailed system, meter, and data on these events and development is not available outside the utility territory and not available to planners.

Each of these issues will be discussed in more detail below, along with recommendations and procedures to handle then. But the overall guideline in such "boundary limitation" situations is to maintain a holistic perspective. Planners must always keep the whole region foremost in their mind, even if their job is to forecast on only a small portion that lies in their service territory. In addition to this first category of planning limitations due to political boundaries, utility planners face two other types of "boundary limitations" that are qualitatively similar. The first of these includes studies where service area boundaries, not political boundaries, limit the area the planners are forecasting. Planners at Constellation Energy cannot assess the forces shaping the growth of Baltimore's southern and western suburbs without taking into account the influence of Washington DC, outside their service area to the southwest. Similarly, the Research Triangle Park area of North Carolina (Raleigh, Cary, Durham, Chapel Hill) is served by both Carolina Power and Light and Duke

Table 21.1 Definitions for Partial-Region Study Study area

Territory that planners plan their system

need

to

forecast

to

Influencing region

The entire whole from which influences on growth in the study area emanate.

Outside area

That portion of the influencing region that is not in the study area


703

Power. Planners for each utility face a challenge because they do not have the data or familiarity with the entire region. Cobb EMC, which serves the northwest corner of Atlanta, cannot assess most of the forces causing growth in their service area, if they ignore the majority of that city just because the largest part of its metropolitan area is outside their service territory. The third of the three partial-region forecasting situations includes planning situations in which the "boundary" is self-imposed. Most large electric utilities organize themselves into discrete operating divisions. Planners for the "western metropolitan" division around a metropolis may need to forecast only their division area, yet find it is part of the larger utility service area. In order to limit the effort involved they do not want to study more than "their" area, yet it is influenced by the larger service territory. Here, they can obtain data and information if they want, but they wish to limit as much as possible their study effort. Such self-imposed boundary situations should not be allowed to happen. Simply put, "district by district" forecasting of areas that are part of one influencing region is not a recommended procedure. While planners for each of the three operating districts at a utility like ComEd (Chicago) may very well plan their T&D systems in a largely self-sufficient manner, the forecast of their joint service territory should be (and is) done as one coordinated effort. Table 21.1 provides some definitions used throughout this section. As mentioned, the fundamental problem in a partial-region forecast study is that growth inside the study area is driven or influenced by factors among the outside areas, for which the planners have no or only limited data. Impacts from the "trending" perspective Within the paradigm of trending - forecasts are in some sense extrapolations of past trends - the major impact of the partial region situation is that the present trends may change due to a changing allocation of load growth among the study and outside areas. Figure 21.11 gives an example. With trending, this partial region situation manifests itself as a lack of data and lack of modeling capability for the planners. They do not have historical data for the entire region nor, usually, a tool that can use it if they did. A hierarchical regression (see Chapter 9, Figure 9.12) can often identify situations like that shown in Figure 21.11. Often, the best tool for this situation is a savvy planner who is alert for this situation and innovative in looking at it from a non-standard perspective. The goal is to reproduce something like the analytical inference that was shown in Figure 9.12, to determine when and how much growth will deviate from historical trends in already-developed areas. Utility planners should not expect too much from such a technique in a situation like this without work and some trial and error adjustment on their part. Frankly, the best solution to this situation is a group of savvy planners who are alert for this situation and innovative in looking at it from the non-standard

Chapter 21

704

Maria Area

GobbB/C

8000

Gecrcja Rower and other summing utilities

Figure 21.11 In the 1970s Cobb EMC's service territory to the northwest of Atlanta was outside the periphery of metropolitan Atlanta. As the metroplex expanded outward through the 1980s, it suddenly grew into Cobb's service territory, and growth "took off." Left, the overall trend in Atlanta as a whole did not change, but Cobb's load (right) took a radical change from historic trends. This data was estimated by the author from FERC, REA, and population data for the Atlanta metropolitan region, which is split by several electric utilities. It was adjusted to remove economic recession effects and other such influences.

perspective. Their goal should be to reproduce something like the analytical inference shown in Figure 9.12. By studying spatial trends and availability of land, they can determine when and how much growth will deviate from historical trends. The planners should expect to have to do a good deal of work in this type of situation - methodology alone will not do the work for them. Overall, this is a job for simulation, but the problem is that that they will need some inkling of the overall trends to drive that simulation. The planners must develop the overall region trend (total shown on the left side of Figure 21.11) and develop a firm basis to relate his study area to that. Generally, they will not have access to the load data on the entire region and if they do it will not necessarily be consistent or complete. Generally, good estimates must be made from other, non-load data that is available. A recommended way to develop the overall region load estimate is to use population data (census data is always available) and assumed per capita load values, adjusted for observable differences in the industrial and commercial components of the study versus outside regions. Another way is to count total squares miles of development (using satellite or aerial photos). This does not mean using a land-use approach, but merely counting the geographic development rates of the metropolitan area as a whole as it spreads out among the various utility service areas.


705

Impacts from the "simulation"perspective Planners working with a simulation tool have a model that can accurately analyze the interactions of their study area with those outside it; one that can easily anticipate, quantify and time changes in historical load growth trends like that shown in Figure 21.11. The major impact of the partial-region situation is that planners do not have detailed system, meter, and consumer data on the outside areas involved. The key point here is the word "detailed." The planners cannot easily obtain detailed information on the outside areas, but they do not need detail. Simulation tools can forecast the whole region, including the study region, without having detailed information on the outside area. Thus, the planners should set up the simulation database with detail and accuracy on their study area, and with only approximate, estimated land-use data on those areas outside their service area (Figure 21.12). The resulting small area forecast of those outside areas will not be highly accurate, but that for the study area will be very nearly as accurate as if the entire database were input with a high level of detail. The reason this "trick" works is the nature of the spatial interactions that simulation mimics in its forecast. The spatial detail in a simulation's small area database is needed in order to compute the short-range factors that influence the precise locations of various land-use growths, such factors as "industrial locates within % mile of a railroad" and so forth. None of these factors have a great effective distance. Many range less than a mile and none go out beyond three miles. The long-range factors (urban pole, regional balance of land-use types) are blurred by location. Viewed from the spatial frequency domain, spatial detail is needed only to compute high-frequency components of the simulation model. Low-frequency components do not depend on the spatial detail. For example, the location and size of the next mall to develop in Laredo, Texas, is not a function of exact locations of malls and market areas in Nuevo Laredo, across the Rio Grande. Its location will be a function of short-range factors related to local roads and topology in Laredo. Its size and the timing of its development will depend on the total amount of malls, markets and retail, and population in the combined region (Laredo and Nuevo Laredo) - the longdistance impacts. The exact locations of the markets and malls in Nuevo Laredo will not affect these either. What is important is their magnitudes. Developing a "fuzzy" database like that shown in Figure 21.12 is quite easy, even if the utility does not have access to detailed demographic and system data for the outside areas. Both sides of the diagram in Figure 21.12 were developed from "public domain" data obtained at no cost over the Internet, using the sources and data, and requiring the efforts shown, in Table 21.2. The detail for Laredo, with a spatial resolution of about 1A mile, is barely sufficient to drive a 2-3-X simulation algorithm in a detailed forecast (sufficient for a study area). Total time to develop, and to calibrate the region to load data, was 4.3 hours. Total time to develop the Mexico-side data was only 36 minutes.

Chapter 21

706 MATILDA© Area: Border Test Database: Larsym

Title: Base Year Data Format: CMPAR

Alg: SUSI-Q Feature: Display

10:07 AM 09/14/01 LWillis ESC 3

Figure 21.12 Example developed by the author from public domain (Internet) satellite photos for both sides of the Texas-Mexico border at Laredo, Texas. This display of electric energy usage makes clear the different spatial resolution used on each side of the national border for a 2-3-X spatial simulation model forecast of the U.S. side. Nuevo Laredo is represented with very little detail, but that is sufficient for the algorithm to determine Nuevo Laredo's effects on the detailed area growth in Laredo.

Table 21.2 Sources and Development Times for Database Behind Figure 21.12 Sub-Area Laredo

Data Spatial land-use Land-base (roads, etc.) Population & Demog. System load Cust/load data Total Laredo

Nuevo Laredo

Spatial land-use Population & Demog. System data Elec load Total Nuevo Laredo

Source

Effort - hr.

TerraServer.com & similar USGS maps Municipal & state web sites FERC filings/Corp. Report Est. from experience

2.6

TerraServer.com Expedia.com Est. from experience Est. from experience

0.3 0.1 0.1 QJ. 0.6

0.2 1.0 O5 4.3


707

"Competitive Land" Considerations Figure 21.13 illustrates another type of "partial area" consideration, one that affects mostly studies for municipal utilities. The significant issue here is modeling the competition among various land parcels for the growth that will occur. As a city nears complete development of the municipal service territory (i.e., the areas within the city limits) growth does not stop, it moves on beyond the city limits). Proper modeling of the region requires that the entire metropolitan region, not just the areas within the city limits (and the municipal utility's service area), be represented in the spatial model. As more and more of the highly-rated land inside the city limits is "used up," growth begins to move outside the city limits, to land ideally suited to development there. This occurs long before all the land, particularly all marginal land, inside the city limits is used. Within reason, development will move to ideal conditions farther out rather than take marginal land inside the city and spend money to make those marginal conditions closer to ideal. This is a fundamentally different issue with respect to modeling the entire region than that discussed above (Figure 21.12). Although there are similarities, the issue here is making certain that the forecast program realizes that there are other options for growth than just locations inside the city limits. The only acceptable way to model this is to represent areas outside the service area in detail, so that the natural competition for growth in and out of the service territory is properly modeled. These areas must be represented with complete detail, unlike the "fuzzy" model that can be applied to situations like that shown in Figure 21.12. Roads, undevelopable areas, and other features of the outlying land must be shown. Chapter 11 gave an example of this with respect to the mythical city Springfield, its city boundaries, and the growth the alternate scenario (truck factory) caused to occur in and out of those boundaries. What is the difference between the situations shown in Figures 21.12 and 21.13? Why can planners represent "the other part" of the region with little detail in the situation in Figure 21.12 but not in the situation depicted in Figure 21.13? In the first instance, the long-term historical trend for the planner's region includes only "their part" of the region. In the latter case, the long-term trend, and all the history the planners have to work with, includes the trend for all their region. Thus, in Figure 21.12, the planners need to only model the rest of the region to the extent needed to understand its influence on their territory. They have a "partial region" global history from which to assess and project "global" growth for their territory. But in Figure 21.13, the problem the planners have is that they do not know what part of the historical trend will move outside their city limits they need the program to tell them that. And to do that, it needs all the detail.

708

Chapter 21

Figure 21.13 Top, a growing city's peripheral development is reaching its city limits. At the lower left, if the municipal electric utility department's planners extrapolate the overall growth, but model it as occupying only areas inside their city limits, their forecast model will "fill in" marginal areas with long-term growth. As a result, they will have a distorted forecast and miss seeing the real pattern of growth, which involves a portion of the growth jumping outside the municipal city limits. A spatial model must represent the entire region over which growth could take place, in order to see the results of competition of various land parcels for the growth that will occur (lower right).


709

21. 6 FORECASTS THAT REQUIRE SPECIAL METHODOLOGY Retirement and Retirement-Based Communities Some areas within the United States specialize in providing comfortable communities for older and retired persons. Sun City, Arizona, and Sarasota, Florida, are two areas that often come to mind, but there are many other areas, all essentially small cities, that are dominated by a "retirement economy." These areas are not only different from most cities in terms of demographics, but also have a different driving mechanism behind their economy. They can be modeled for spatial load forecasting using simulation methods, but planners applying those tools must modify the application through adjustment of various factors. Differences are: Residential is the basic industry. Unlike most cities and most examples in this book and those in Chapters 10 and 11, a retirement community has no basic industry of the normal sense - no factories, no corporate headquarters or R&D labs, and no industrial or commercial enterprises that "earn" money for the regional economy. The income driving the community comes from outside its region in the form of pensions and savings. Among the land-use classes, residential is the "industrial" class in the sense that it creates the region's income. Land-use mix. Obviously, the mix of land use in a retirement community will differ from that of the average city. First, there is no industry or basic commercial development. Second, there may be more, and will certainly be different, mixes of retail commercial: entertainment and recreation retail (marinas, golf courses, etc.) in addition to the more general shops, stores and retail facilities in any city. Also, the residential classes tend to be slightly more dense in development and smaller in overall size of lots. Residential siting will differ from that in cities and communities driven by basic industries. With no job site issues to constrain selection of home sites, locational options tend to cluster around amenities (golf courses, shoreline) and are close to shopping. In fact, there may be no, or few, long-range siting factors at work in the region. In the language of simulation, there are no urban poles, or at least none with wide diameters. Electric load characteristics differ. Activity patterns of retiree households are different than those of "employed" households in cities with industry and basic commerce. Daily load curves will be different. Load densities will be different. Often retail commercial load patterns are affected, too. Planned communities. Many retirement cities are well planned on a master basis, with the entire community laid out on a holistic basis. There are two reasons. First, many of these communities are planned and financed by a single developer (e.g., Sun City's Del Webb). Second, and

710

Chapter 21

perhaps as important, there are fewer compromises needed in the planning of the community's layout and what is zoned where. With no industry or basic commercial development to fit into the community or to accommodate, community layout can be almost entirely based on residential and esthetic needs. Master plans tend to be satisfactory to all concerned, and there are far fewer deviations over time than in other cities. Driving growth trend. With no basic industry driving the local economy, many of the recommended ways of tying the region's growth to projected employment or to government economic projections cannot be applied. As mentioned early in this book, such government projects are often done on the basis of extensive research and with involved models and are useful where there is local heavy industry and commerce. The most obvious trend to use is senior population, but available projections of this trend are often flawed, produced by someone else's trending with what turns out to be very little rigor behind its analysis. A projection of the community's income as a whole, taking into account projected population increase (due to a growing housing base) and changes in pension and investment income, is sometimes useful. Thus this is one situation where planners need to adopt one or two approaches not normally recommended: either accept the master developer's projections (with skepticism) or simply trend past population growth rates. Thus, for the forecasting of retirement communities, a standard simulation forecasting method can be used. Simulation forecasts need to be set up with different land-use inventory mixes, different global model drivers, and different preference function factors. Driving forces need to be linked to some relevant to the local economy. Finally, a set of electric load curves designed specifically for retirement areas need to be developed. When the retirement community is part of a larger region The issues raised above do not disappear if the retirement community is only a part, not the entirety of, the region being studied. The recommended way to model them in such a case is to create separate land-use classes for the retirement community. Typically it needs only distinctive classes for: Single-family homes Multi-family homes High-rise residential Retail Usually only these four classes need be made unique to cover the modeling needs for the retirement communities. The retirement community might have


711

some content of other land uses (e.g., municipal, institutional), but these can be the standard land uses used everywhere else in the region. But these four classes need to be set up to model only the retirement area. They have different densities (usually higher than normal), preference functions (tied to local amenities), ties to urban poles (low), and electric characteristics than their corresponding landuse classes used in other areas. Table 21.3 gives an example, showing the typical set of land uses that might be used in a 2-3-X simulation model, with the four additional classes needed to represent the differences in the retirement community. The table shows only load density, but consumer densities, preference functions, and urban pole interactions are also different. A final complication when modeling retirement and non-retirement together is that the retirement classes must be tied to specific locations (the retirement community) and not allowed to developed in a scattered fashion around the community. The easiest way to do this in a standard model is to put aflat "urban pole" in the center of the retirement community (see Figure 21.5) with a sufficient radius to cover it. The retirement classes are then heavily linked to it, and their equivalent non-retirement classes are given a strong negative factor.

Table 21.3 Classes Used in Modeling a Retirement Community in Company with a More Normal Region Class Rural residential Single family homes Apartments/condos Retail market commercial Office/professional Institutional High-rise commercial Warehouses Light industry Heavy Industry Single family homes - ret. Apartments/condos - ret. High-rise residential - ret. Retail market commercial in retiree areas

kW/mile2 360 2200 3100 2800 3900 4400 14500 1300 3300 7800 2600 2900 5600 2500

712

Chapter 21

Regions with Tourist and Recreational Economies Regions that "earn their living" from tourism or recreational activities require adjustments to the model's set-up when being forecast with standard simulation. In many cities and regions, tourism and entertainment is a basic industry - one that markets its product and services to areas outside its region. However, in a few areas like Orlando, Florida, Las Vegas, Nevada, and Branson, Missouri, Myrtle Beach, South Carolina, and Dakar, Senegal, it is the major or only industry contributing to the economic viability of the region. This industry has several characteristics that planners must consider when forecasting and when using a simulation-based tool to do that: The region's basic industry has its primary elements like the theme parks, golf courses, beaches, marinas, campgrounds, parks, cruise ship terminals, and so forth, and secondary elements like hotels, motels, restaurants, and similar types of development. All are part of the local basic industry, the land-use component that fuels the region's economy. Land-use mix. The land-use mix in tourism and recreationally-driven regions differs substantially from that in a metropolitan area with a more diverse or normal economy. First, and obviously, there is no heavy industry or commercial sector in the local economy. Second, the tourism infrastructure - hotels, motels, restaurants and so forth is often quite distributed. Third, the demographics of the local workforce, and the land use required to support it, are quite different than in diverse-economy cities. The majority of jobs in these industries tend to be lower paying service jobs. Housing reflects that. Particularly if the industry is seasonal, most if not all housing will be multi-unit housing, composed mostly of smaller units. Commercial retail siting in cities with significant tourism differs noticeably from that diverse-economy and non-tourist economy cities. Tourism is one of the few basic industries that can consistently outbid commercial retail class for land. In a city like Cleveland or Atlanta, retail commercial development occupies the prime land at most major intersections and is the land-use that generally "outbids" other land uses for sites near prime high-traffic corridors. In regions with tourist-driven economies, the local basic industry usually outbids retail commercial, pushing it to sites just off the heavy traffic corridors and adjacent to, not at, major intersections. Electric load characteristics differ. The temporal load behavior of tourism economies differs from that of "normal" areas in terms of its


713

daily, weekly, and seasonal variations. Most of these differences will be rather obvious to those studying the region. Daily load curves often are fairly flat, or may even peak at atypical times such as late at night - the usage pattern is linked to the temporal concentration of the tourism activities. Thus, load curves in some areas of Las Vegas are fairly flat due to the 24-hour activity at casinos and show hotels. Generally, in nearly all tourism areas, weekend loads do not fall off from weekday load levels, and in fact are often higher (due to increased weekend, short-term tourism). Finally, many tourism areas have a seasonal economy. Demand in the region varies not only among the activities directly associated with the tourism industry, but among the residential and local infrastructure development. "University" and Government Towns A number of small cities in the United States and elsewhere around the world, are "university towns" in the sense that the primary basic industry in the city is a university or institute of higher learning and research. Examples are State College, PA (Perm State University), College Station, TX (Texas A&M University) and South Bend, IN (Notre Dame). Somewhat similar are government hubs - cities which exist primarily because they are a state or regional capital, such as Tallahassee, FL (state capital of Florida and home of one of its major universities). In a community driven only by a university or similar institute, the electric demand of the entire region is most closely linked to total enrollment at the university rather than faculty count or other factors. Thus, if enrollment at Big State College increases from 10,000 to 10,500 in one year without accompanying faculty and facilities increases, there will perhaps not be an immediate 5% increase in community demand, but there will be something close to it, growing to 5% over time. Figure 21.14 is based on a small college town in the U.S., and shows full-time student enrollment and full-time equivalent faculty and staff count for the university. The diagram clearly shows that load tracks enrollment better than faculty and staff count (faculty and staff grew little during the 1990s while enrollment grew at about 2% annually, as did electric load). Regression analysis of the 1972 - 2001 data confirms that enrollment counts most; the linear two-variable equation that fits best is: Peak demand (MW) = 3.97 x Enrollment + .95 x Faculty & Staff

(21.1)

Thus, peak regional demand is most closely tied with enrollment: students count for four times as much as faculty in determining overall regional demand. Interestingly, an appliance-based input-output end-use model for year 2001 shows

Chapter 21

714

J

30,000

100

25,000

84

20,000

67

15,000

50

10,000

33

5,000

17

it *- 2 o0

of §E

^

T3 (0 P

LU

1

1972

1

T"

1975

1980

1985

1990

~i—i—i—i—i—i—r~ 1995 2000

Year Figure 21.14 Enrollment, faculty and staff, and peak demand for Fort Bend, a college town, from 1972 to 2001. Faculty and staff counts were kept nearly constant throughout the 1990s as enrollment increased. Electric demand grew in proportion to enrollment, not the number of faculty and staff.

the opposite. It indicates that electric load due to direct and indirect causes in the local economy is attributable to students and faculty as 2001 Peak (MW) = 2.17 x Enrollment + 5.99 x Faculty & Staff

(21.2)

Given their use of electricity at work and home, and the electric load their household's and work facilities' consumption of services and goods creates in the community, in 1986 each faculty and staff member created a demand for 5.99 kW, while students each created only a bit more than a third of that. The apparent mismatch between the explanations of regional load from equations 21.1 and 21.2 can be explained by applying the equation 21.2 to 1986, rather than 2001. For 1986, the equation works out to be 1986 Peak (MW) = 2.14 x Enrollment + 5.22 x Faculty & Staff

(21.3)

The years 1986 and 2001 are unique in the period studied because they are respectively the years with the lowest and highest ratios of students to faculty + staff. In 1986 there were 2.27 students per faculty and staff, in 2001 the ratio was 2.79. A higher student/faculty ratio increases electric usage among faculty.


715

College-town land-use and spatial structure differences As mentioned in Section 21.2 university towns have somewhat different land use characteristics than "typical" small or large cities. The most important difference for the spatial forecaster is the land-use inventory, as illustrated in Table 21.4, which compares the land-use content of a "typical college town" to that of a typical small city (Springfield, from Chapter 11). The college town data is the average of three educational communities in the U.S., normalized to the same population as Springfield (here, population includes students). Land-use comparison is then in percent of the total for the typical town. The first point to note is that the university town occupies only 65% of the land of a typical city of equivalent population. The college town has less of every land use except high-density housing and institutional (the university itself) development. In some sense, the college town is concentrated more than a typical town. While it may seem as if this is due to a desire on the part of all portions of the community to be close to the university, this is not the case. The "concentration" is a result of the higher density land use. The college town trades a good deal of low-density housing space for an increase in high-density housing, and loses a good deal of low-density development such as warehouses and light industry.

Table 21.4 Comparison of Typical and College Towns of the Same Size - in percent of total land area Land-Use Class

Typical Town College Town

Residential 1

56.3%

15.2%

Residential 2

27.9%

18.7%

Apartments/twnhses

6.0%

23.9%

Retail commercial

4.0%

4.0%

Offices

1.5%

0.8%

High-rise

0.5%

0.2%

Industry

2.1%

0.4%

Warehouses

1.1%

0.3%

Municipal & Institution

0.2%

1.8%

Transmission level TOTAL

0.4%

0.0%

100%

65.3%

716

Chapter 21

Generally, there is a more uniform concentration in a university town, in the sense that the town has only one pole - it is concentrated around the university with no other major centers of focus for the community. Even a small, typical town might have two poles - one representing its industrial base and the other its "downtown" or retail and offices services center. In a university town there is usually only a short distance between the university and the downtown hub. Not so in a factory or refinery town, or in most other single-economy communities. There are few important structural differences in how the various land uses locate in a university town, as opposed to other towns and cities. The various land uses respond to proximity and surround pressures in very close to the same manner as they do in any city. Generally, mass transit is more important to local residents, and this does show in very detailed assessment of how residential land use locates. But it makes little difference because in college towns the mass transit (city and campus bus) system is much more comprehensive than in most others. As a result, the standard set-up for a simulation program's preference functions can be used.

REFERENCES Canadian Electric Association, Urban Distribution Load Forecasting, final report on CEA Project 079D186, Canadian Electric Association, 1986. J. L. Carrington, "A Tri-level Hierarchical Simulation Program for Geographic and Area Utility Forecasting," in Proceedings of the African Electric Congress, Rabat, April 1988. I. R. Lowry, A Model of Metropolis, The Rand Corporation, Santa Monica, CA, 1964. C. Ramasamy, "Simulation of Distribution Area Power Demand for the Large Metropolitan Area Including Bombay," in Proceedings of the African Electric Congress, Rabat, April 1988. H. N. Tram, H. L. Willis, and J. E. D. Northcote-Green, "Load Forecasting Data and Database Development for Distribution Planning," IEEE Transactions on Power Apparatus and Systems, November 1983, p. 3660.

Recommendations and Guidelines 22.1 INTRODUCTION This final chapter summarizes the practical aspects of spatial forecasting of electric loads for T&D planning. Section 22.2 reviews the prioritized requirements listed in Chapter 1 and indexes which chapter, recommendations and pitfalls are pertinent to each. Section 22.3 discusses ten recommended practices or policies for effective, economical electric utility spatial forecasting. Section 22.4 similarly presents some pitfalls to avoid. 22.2 SPATIAL FORECASTING PRIORITIES Table 22.1 is somewhat similar to Table 1.6 and summarizes the spatial load forecast requirements discussed in this book, assigning each a relative importance based solely on the author's subjective judgement. However, the table differs slightly in several respects from the way this information was listed in Chapter 1. First, the priorities are not listed in order of overall importance, but instead separated into two categories - (1) algorithm and procedure related and (2) applications related. The first category includes those issues that affect the selection and specification of the forecasting algorithm and the procedure the utility sets up to use that method and which a utility basically sets in stone once it has selected an algorithm and defined its procedures. The second embraces those issues of application, or how the planners choose to study, consider, and use whatever load forecast method they have. While some of these issues overlap

717

Chapter 22

718

into algorithm and method, these are for the most part issues which the planners can change or at least fine-tune at the time they do a particular forecast. For example, although many algorithms somewhat restrict temporal resolution that can be set up, in all cases planners can decide what future time periods they will forecast (what years, seasons, for how far into the future), even if in some cases they must innovate to work around program restrictions. Thus, temporal resolution is mostly a decision about application, and its proper application is the responsibility of the forecasters using the tool. By contrast, spatial resolution is dependent on algorithm and procedure, and usually cannot be altered once the methodology has been put in place. Thus it is the responsibility of those making the selection of algorithm and designing the utility's forecasting/planning procedures. Table 22.1 also lists for each item the relevant chapters where concepts allied to it are covered or where a planner interested in that item might seek further information and detail. It also lists the pertinent recommendations and pitfalls from sections 22.2 and 22.3 of this chapter that affect each.

Table 22.1 Priorities in Spatial Electric Load Forecasting Issues that Are Predominately Algorithm and Method Related Rank

1 2 5 9

10

Requirement

Importance Chapters

Forecast MW - how much

Recom.

Pitfalls

10 Spatial resolution - where 10 Representational accuracy - why 8

1,7,8

1,6

2

1,7,8,18,19

1,6

4

1,7,18-21

5,8

Load curve shapes Reliability value/need

4, 18,20 2,4

5 5

5 5

Issues that Are Predominately Application or User Related Rank

Requirement

Importance Chapters

3

Temporal resolution - when

10

4

Weather normalization - how

9

6 7

Consistency with corp. forecast Analysis of uncertainty - why

7

8

Consumer class forecast - who

Load curve shapes 10 Reliability value/need

9

Recom.

Pitfalls

1,6

1,2,4,7,8 5,6

3

1, 11, 13,17,21 1,7,20,21

1,4 4,5,8

6

1,8,9,18-20

1,6

5

4, 18,20

5

5

2,4

5

7

6

5

Recommendations and Guidelines

719

Table 22.2 Required Characteristics of Spatial Load Forecasting Methods for the Electric Power Industry Characteristic

% Applications

Forecast Forecast Forecast Forecast

annual peak off-season peaks total annual energy some aspects of load curve shape (e.g., load factor, peak duration) Forecast peak day(s) hourly load curves Forecast hourly loads for more than peak days Forecast annual load duration curve Power factor (KW/KVAR) forecast Forecast reliability demand in some fashion Multiple scenario studies done in some manner

Critical?

100% 66% 85%

Yes

66% 50% 15% 10% 20% 15% 66%

Yes

Yes

Base forecasts updated - at least every five years - at least every three years - at least every year - at least twice a year

100% 66% 50% 5%

Forecast covers period - at least three years ahead - at least five years ahead - at least ten years ahead - at least twenty years ahead - beyond twenty years ahead

100% 100% 50% 20% 20%

Yes

Spatial forecast "controlled by" or adjusted so it will sum to the corporate forecast of system peak (coincidence adjustment may be made)

50%

Yes

Forecasts adjusted (normalized) to standard weather Weather adjustment done rigorously

80% 25%

Yes Yes

Consumer class loads forecast in some manner End-use usage forecast on small area basis DSM impacts forecast on a small area basis Small area price elasticity of usage forecast

35% 5%

Spatial electric load forecasting

Electrical Load Forecasting: Modeling and Model Construction

Mother Load

Metallica - Load

Economic Forecasting

Economic forecasting

Economic Forecasting

Ocean Forecasting

Colour Forecasting

Cognitive Load Theory

Cognitive Load Theory

Load bearing fibre composites

Carrier Load Estimation

The Glycemic-Load Diet

Server Load Balancing

A Load of Bollocks

Server Load Balancing

Economic forecasting: An introduction

Time-Series Forecasting

Statistical Demography and Forecasting

Forecasting Oracle Performance

Spatial statistics

Spatial Econometrics

Spatial Econometrics

Get a Load of This

Rapid Load Testing on Piles

Get a Load of This

Spatial Statistics

Electric Rayne

Electric Eden

Time-series forecasting

Spatial electric load forecasting

Electrical Load Forecasting: Modeling and Model Construction

Mother Load

Metallica - Load

Economic Forecasting

Economic forecasting

Economic Forecasting

Ocean Forecasting

Colour Forecasting

Cognitive Load Theory

Cognitive Load Theory

Load bearing fibre composites

Carrier Load Estimation

The Glycemic-Load Diet

Server Load Balancing

A Load of Bollocks

Server Load Balancing

Economic forecasting: An introduction

Time-Series Forecasting

Statistical Demography and Forecasting

Forecasting Oracle Performance

Spatial statistics

Spatial Econometrics

Spatial Econometrics

Get a Load of This

Rapid Load Testing on Piles

Get a Load of This

Spatial Statistics

Electric Rayne

Electric Eden

Time-series forecasting

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