Leading-Edge Electric Power Research

LEADING-EDGE ELECTRIC POWER RESEARCH

LEADING-EDGE ELECTRIC POWER RESEARCH

CIAN M. O'SULLIVAN EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2008 by Nova Science Publishers, Inc.

All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Leading-edge electric power research / Cian M. O'Sullivan, Editor. p. cm. Includes index. ISBN-13: 978-1-60692-427-3 ISBN-10: 1-60021-981-0 (hardcover) 1. Electric power systems. I. O'Sullivan, Cian M. TK1005.L39 2008 621.31--dc22 2007033915

Published by Nova Science Publishers, Inc.

New York

CONTENTS Preface

vii

Expert Commentary Commentary A Analysis and Characterization of Complex Inter-Area Oscillations from Measured Data: A Time-Frequency Perspective A. R. Messina, E. Barocio and M. A. Andrade

1

Research and Review Articles Chapter 1

Chapter 2

Chapter 3

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners for Wall-Fired Boilers Zhengqi Li

5

Fuel Cell Combined Cycle Power Generation System Installed into Micro-Grid Shin’ya Obara

127

Electricity from Renewable Energy Sources: A Multi-Criteria Evaluation Framework of Technologies Fausto Cavallaro

173

Chapter 4

Gas Turbines and Electric Distribution System Francisco Jurado

205

Chapter 5

Micro CCHP: Future Residential Energy Center R. Z. Wang and D. W. Wu

239

Chapter 6

Sensitivity Calculation in Real Time Transmission Network and Energy Markets Jizhong Zhu

265

Wide-Area Monitoring and Analysis of Inter-Area Oscillations Using the Hilbert-Huang Transform A. R. Messina, M. A. Andrade and E. Barocio

285

Chapter 7

vi Chapter 8 Index

Contents Unconventional Problems in Power Systems Protection Mahmoud Gilany and Mohamed A. Mahmoud

317 331

PREFACE This book presents new and significant research on electric power. The world is becoming increasingly electrified. For the foreseeable future, coal will continue to be the dominant fuel used for electric power production. The low cost and abundance of coal is one of the primary reasons for this. Electric power transmission, a process in the delivery of electricity to consumers, is the bulk transfer of electrical power. Typically, power transmission is between the power plant and a substation near a populated area. Electricity distribution is the delivery from the substation to the consumers. Due to the large amount of power involved, transmission normally takes place at high voltage (110 kV or above). Electricity is usually transmitted over long distance through overhead power transmission lines. Underground power transmission is used only in densely populated areas due to its high cost of installation and maintenance, and because the high reactive power gain produces large charging currents and difficulties in voltage management. A power transmission system is sometimes referred to colloquially as a "grid"; however, for reasons of economy, the network is rarely a true grid. Redundant paths and lines are provided so that power can be routed from any power plant to any load center, through a variety of routes, based on the economics of the transmission path and the cost of power. Much analysis is done by transmission companies to determine the maximum reliable capacity of each line, which, due to system stability considerations, may be less than the physical or thermal limit of the line. Deregulation of electricity companies in many countries has led to renewed interest in reliable economic design of transmission networks. Chapter 1 - The kind of swirl coal burners is given. Radial-biased-combustion and centrally-fuel-rich swirl coal combustion technology was developed. In the air and the airparticle test facilities, the single sensor hot-film and the anemometers were used to measure air and air-particle flows in the near-burner region of different swirl burners. Both cold air flow and reacting flow experiments were performed in the industrial 50, 220, 410, 670 and 1025 ton per hour boilers. On an air-particle test facility, the characteristics of the pulverizedcoal concentrator with cone vanes were investigated. The influence of structure parameters, such as run parameters such as swirling vane angle and burner cone angle and length, and run parameters, such as non-swirl secondary air, central air and air supply, and primary air flow type on divergent angles, diameter and length of the central recirculation zone, mixing characteristic of the primary air and the secondary air, in-situ gas temperature and NOx formation near the burner zone, carbon in ash and NOx emission of boilers was determined with the radial-biased-combustion burner. The difference characteristics of gas/particle flow

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and coal combustion of the centrally-fuel-rich and dual register burners were obtained. The experimental results show that the two new burners simultaneously have the ability of high combustion efficiency, flame stability, low NOx emission and resistance to slagging and high temperature corrosion. The air-surrounding-fuel combustion theory was put forward. Chapter 2 - The introduction to urban areas of the micro-grid system has the following characteristics. (a) The distance between the heat-supply side and the heat-demand side is short, and effective utilization of exhaust heat is possible. (b) It is linked with the load leveling of the existing large-sized electric power facilities. (c) Since a facility suitable for the energy-demand characteristics of a region can be installed, energy efficiency may increase and facility costs may decrease. The micro-grid using a proton exchange membrane type fuel cell (PEM-FC) may greatly reduce environmental impact. However, when connecting an energy system to the micro-grid of a city area and operating, partial load operation occurs frequently and power generation efficiency falls. And, the electrode material (especially the catalyst material and the proton exchange membrane) of PEM-FC is expensive, and its system is complex. Consequently, it is necessary to connect two or more power generation systems to the micro-grid, and to design optimization of an operation plan for the purpose of maximization of power generation efficiency. Therefore, the methods of an improvement of the efficiency of the power generation system connected to the micro-grid installed into a city area are described. In this chapter, it consists of subjects of three studies on the micro grid. In these studies described in this chapter, the improvement of the subject of the micro grid is tried by combining fuel cell and other power equipment. Section 1 describes "Operation Plan of Micro Grid Using PEM-FC/Diesel Engine Generator Combined System." Section 2 describes "Carbon-Dioxide Emission Characteristic of Micro Grid Using PEMFC/Hydrogenation City gas-Engine Combined System." Section 3 describes "Dynamic Characteristics of Micro Grid Using PEM-FC/Woody Biomass Engine Combined System." Chapter 3 - The energy policy of many Western governments aims to diversify supply and reduce dependence on foreign sources and thus to maximise benefits from internal resources. Undoubtedly, the main strategy underlying this is one that seeks to optimise the use of renewable energy sources (RES). The development of these sources, as well as their market penetration, depends however not only on political will but also on sound management of energy demand in order to rationalise and stabilise energy consumption. In addition to fortifying the guaranteed energy supply, RES represent a potential that cannot be overlooked. This lies in their ability to reduce greenhouse gas emissions and thus to stem the growing trend of global warming, one which has accelerated particularly in recent years and which is due mainly to the use of fossil fuels for producing electricity. The use of RES for the production of electric power brings huge benefits both in terms of environmental protection as well as savings in non-renewable resources. Nevertheless, the very nature of RES raise technical and economic problems that create a considerable gap between their potential capacity and ways to feasibly exploit them. Their many different forms and the ways in which they may be used have to be carefully examined in order to evaluate the costs and other technical and environmental factors involved. The planning and appraisal of sustainable energy projects involve rather complex tasks. This is due to the fact that the decision making process is the closing link in the process of analysing and handling different types of information: environmental, technical, economic and social. Such information can play a strategic role in steering the decision maker towards one choice instead of another. Some of these variables (technical and economic) can be

Preface

ix

handled fairly easily by numerical models whilst others, particularly ones relating to environmental impacts, may only be adjudicated qualitatively. In many cases therefore, traditional evaluation methods and the chief economic and financial indicators are unable to deal with all the components involved in an environmentally valid energy project. Multicriteria methods provide a flexible tool that is able to handle and bring together a wide range of variables appraised in different ways and thus offer valid assistance to the decision maker in mapping out the problem. Chapter 4 - Lately, the use of gas turbines following the deregulation of the electricity supply industry has become greater quickly. The motivation for modeling the gas turbines and their controllers is determinant to the interpreting of their impacts on distribution systems. The model predictive control (MPC) is used to damp the oscillation when the power distribution system is subjected to a disturbance. MPC is selected because it can explicitly handle the nonlinearities, and constraints of many variables in a single control formulation. The IEEE 13 node power distribution system is employed to demonstrate the effectiveness of MPC to damp the oscillations of gas turbines. Among fossil fuels, gas is the most quickest, with a growth rate nearly double that of coal and oil. The electricity generation field is the leading market for gas. The natural gas business has a great interaction with the electricity market in terms of fuel consumption and energy conversion. On the other hand, the transmission and distribution activities are very similar with the natural gas transportation through pipelines. The power losses in gas and electric systems are compared. It is also demonstrated that the electricity system results more convenient for longer distances of gas wells from electricity consumption area. Chapter 5 - Combined cooling, heating and power (CCHP) system, as a distributed energy system, can work all the year and provide cooling/hot-water/power in summer, heating/hot-water/power in winter and hot-water/power in other seasons. In CCHP systems, the total energy efficiency increases to over 85%, while the average energy efficiency of conventional fossil fuel fired electricity generation systems is around 40%. The energy efficiency promotion of CCHP systems results in emission reduction compared to the conventional methods of generating heat and electricity separately. And as a distributed energy resource, CCHP systems also increase in the reliability of the energy supply. With the overall development of CCHP systems and related technologies, the utilization of micro CCHP systems in the residential sector is emerged as a growing potential. The article focuses on the micro CCHP systems for single-family applications (around 10 kW) and multi-family or residential district applications (under 200 kW). The status quo of micro CCHP systems is briefly presented and diverse combinations of technologies existing in applications or experimental units are listed through comprehensive literature review. Various technologies available or under development are introduced, such as reciprocating internal combustion engine, micro-turbine, fuel cell, Stirling engine, absorption chiller, adsorption chiller and so on. Afterward, the tendency and issues of micro CCHP systems are discussed. The review shows that micro-CCHP applications are entering into average families as a nextgeneration residential energy supply center. Chapter 6 - The calculations of the several sensitivities such as loss sensitivity, voltage sensitivity, generator constraint shift factor, and area based constraint shift factor become very important in energy management system (EMS) and energy markets. This chapter focuses on the analysis and implementation details of the above-mentioned sensitivities calculations in the practical transmission network and energy markets. The power operator

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uses them to study and monitor market and system behavior and detect possible problems in the operation. These sensitivities calculations are also used to determine whether the on-line capacity as indicated in the resource plan is located in the right place on the network to serve the forecasted demand. If the congestion or violation exists, the generation scheduling based on the sensitivities calculations can determine whether or not a different allocation of the available resources could resolve the congestion or violation problem. This chapter also comprehensively discusses how to compute and use the sensitivities under the different references such as the market-based reference, and the energy management system based reference. The calculation results of the several sensitivities are illustrated using the IEEE 14 bus system and AREVA T & D 60-bus system. Chapter 7 - Many transient processes in power systems involve phenomena that vary in time and space in complicated ways. Comprehensive monitoring of large-scale power systems by means of properly placed time-synchronized phasor measurement units (PMUs) provides the opportunity to analyze and characterize complex inter-area swing dynamics involving all or most of the power system. Wide-area real-time monitoring may prove invaluable in power system dynamic studies by giving a quick assessment of the damping and frequency content of dominant system modes after critical contingencies. Measured data, however, may exhibit quite different dynamics at each system location or exhibit abrupt changes, dynamic irregularities, or be complicated by nonlinear trends or noise. Traditional Fourier and Prony methods for system identification are unable to resolve the localized nature of these processes and hence provide little useful information concerning the nature of noisy, time-varying oscillatory processes. In this Chapter, a new method for analyzing the temporal dynamics of nonlinear and nonstationary inter-area oscillations using a local empirical mode decomposition (EMD) method and the Hilbert transform is presented. Two novel algorithms are developed to address nonlinear and non-stationary issues. The first method is a local implementation of the empirical mode decomposition technique. The second is an algorithm to compute the Hilbert transform using finite impulse response (FIR) filters. By combining these approaches, the method can be used to analyze complex signals for which the conventional assumptions of linearity and stationarity may not apply and can be implemented for on-line estimation of modal damping and frequency using synchronized wide-area measurement systems. The physical mechanism underlying nonlinear time-varying inter-area oscillations is investigated and methods to characterize the observed oscillatory phenomena in terms of physically meaningful modal components are proposed. Emphasis is placed on identifying modal content in the presence of noise and nonlinear trends. Issues concerning the implementation of the method and numerical considerations are also discussed. As specific applications, data obtained from PMU measurements from a real event in the northern systems of the Mexican interconnected system are used to examine the potential usefulness of nonlinear time series analysis techniques to characterize the spatio-temporal characteristics of the observed oscillations and to determine the nature and propagation of the system disturbance. The efficiency and accuracy of the method is demonstrated by comparison to other approaches. Chapter 8 - Of the numerous electric power faults an Electric Engineer comes across in a life time, only a few of these faults are memorable- the rest being routine ones. In this chapter, some of those unconventional faults, which are mainly related to power system protection, are presented. The chapter presents five case studies of actual field incidents

Preface

xi

rather than hypothetical scenarios. The objective of the chapter is to present a typical approach for analyzing the faults in power systems.

In: Leading-Edge Electric Power Research Editor: C. M. O’Sullivan, pp. 1-3

ISBN: 978-1-60021-981-8 © 2008 Nova Science Publishers, Inc.

Expert Commentary

ANALYSIS AND CHARACTERIZATION OF COMPLEX INTER-AREA OSCILLATIONS FROM MEASURED DATA: A TIME-FREQUENCY PERSPECTIVE A. R. Messina, E. Barocio and M. A. Andrade Large sparse power systems form an extremely complex dynamical system which usually possess many degrees of freedom and poses a challenge for simulation and analysis. Forced complex oscillations triggered by the loss of major system resources may manifest highly complex spatial and temporal dynamics and involve a large number of machines and take place over a great range of time and time scales. Proper understanding of the underlying dynamics causing these oscillations requires investigation of the various types of temporal nonlinear interactions involving the fundamental modes of the system. Such features may be obscured or distorted in the normal spectral analysis approach. The analysis of spatio-temporal dynamic patterns is important for many reasons. Nonlinearity causes the fundamental waves or temporal modes to interact, leading to frequency and amplitude modulation and to a phase relationship known as quadratic phase coupling between the frequency components involved. Mounting evidence suggest that these interactions can have a significant impact on system performance such as the modal content of the observed oscillations and may the design of controllers. Further, it is also possible that nonlinearity contributes to non-stationary behavior in the record. Characterization of non-stationary behavior is, on the other hand, required for both, detailed understanding of the mechanisms leading to instability and addressing the key questions of how the temporal oscillation modes evolve over time. Accurate tracking of temporal behavior allows replicating the events leading to the observed oscillations, and analyzing the specific condition, control action or device on modal content. With the recent development and application of sophisticated measurement systems, the detection and characterization of the temporal evolution of temporal oscillations is becoming ever more important. Non-stationary system behavior may result from the effects of sequential faults, control actions, and changes in system topology and operating conditions. The issue of stationary is particularly important in studying the system response to large and abrupt changes in system

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topology or operating conditions, and in tracking the system response to sequential faults. This information may be used to understand the source mechanism of oscillations, to examine dynamic trends and phase relationships between key system signals, and to detect and recognize instability signatures in the dynamic activity of the system. Detecting and identifying sources of nonlinearity and nonstationarity in observed time series are difficult problems. In large, loosely connected power systems, the analysis and characterization of inter-area oscillations from measurements is a formidable challenge. Measured data is noisy, non-stationary and often of limited duration. Extracting the dynamics of interest from a limited and usually reduced number of measurements is a complex problem. This is particularly true in the study of measured, inter-swing dynamics in which local and inter-area motions may participate in the observed oscillations. The detection and characterization of temporal oscillations in measured and simulated data is greatly complicated by non-stationary variations in system dynamic behavior. Postmortem data from recent wide-area electrical disturbances shows that complex oscillatory processes are often accompanied by short-term, irregular, event-type features that make the on-line analysis and control of transient performance very difficult. Successful analysis of highly complex dynamic events requires analysis approaches with high levels of sophistication including the ability to treat nonlinear and nonstationary data, increased time – and frequency resolution, accuracy, and ease of implementation among other features. In this contribution, a critical review of methods for the analysis, modeling and characterization of transient processes in power systems is presented with an emphasis on methods of analysis of nonlinear, time-varying time series. Attention is focused on the most promising advances and areas where knowledge is still insufficient or incomplete. Recent developments and future challenges are emphasized. Several time and frequency representations have been explored over the last few years to analyze dynamic processes that are characterized by nonlinear and non-stationary characteristics. Most of them have been developed for the analysis of linear system response and are available in many commercial packages. However, its effectiveness is often hampered by the presence of non-stationarity in the process and the validity is limited to processes showing linear behavior. Two traditional methods, Prony and Fourier-based spectral analysis methods are now used routinely as tools for investigation and characterization of inter-area oscillations. These methods do, however, suffer from a number of disadvantages, the most important of which is the assumption of stationarity that renders them invalid for many applications. A further complication in the application of these techniques is that power system signals are, at best, quasi-periodic and the periods may change over time. This can result in a degradation of resolution in spectral estimates. Ambient data from on-line measurements and recordings from real events are the best resource to investigate nonlinear, nonstationary effects on system behavior. Recorded data display a very rich structure indicative of a variety of intrinsic nonlinear activity in the system dynamics. The detection of temporal changes in the system response following a sequence of faults requires a monitoring technique that accurately represents the major relations among the process variables. Rapid advances and significant developments in communications technology have led to the fast evolution of wide-area measurements systems. Wide-area measurements provide the opportunity to analyze and characterize inter-area swing dynamics in complex interconnected power systems. This refined information can be used to advantage in system control and

Analysis and Characterization of Complex Inter-Area Oscillations…

3

monitoring, and model validation. For many applications, however, a complete framework for dynamic security monitoring is still evolving. The significant improvement in the quality of power system stability data achieved during recent years has led to a variety of improved methods for estimating spatial and temporal characteristics of the observed oscillations. Methods currently used to predict nonlinear random response include higher order statistics, auto-regressive moving average techniques and joint time-frequency representations. Among such procedures, the continuous wavelet transform, empirical orthogonal analysis and the Hilbert-Huang technique have proved to be useful tools for analyzing and studying the time-varying modal characteristics of lightly damped nonlinear systems subjected to large perturbations where the limitations of Fourier-based techniques make the investigation of localized or time-varying features difficult or uninformative. These methods partially avoid some of the problems associated with conventional spectral analysis in the non-stationary setting above, but they still have some limitations. Application of time-frequency transformation tools has been successful in determining both, the interacting modes and the distribution of non-stationarity. These methods give a sharp description of the time-varying frequency content of the physical phenomena and can be implemented for on-line monitoring of transient processes. Of particular interest are applications where these techniques are used to extract modal information on an on-line basis or for real-time control of system behavior. This is a relatively neglected field for which further research is needed. As these techniques mature, however, their efficient implementation for the analysis of complex processes hinges upon their computational streamlining. Recent studies suggest that various levels of refinement are required according to the application. Also the considerable potential for combining time-frequency analysis with other analytical approaches, for prediction of spatio-temporal patterns, remains unrealized. Future applications could be focused now on the implementation of smart wide-area monitoring, protection and control systems based on sophisticated methods of analysis of time series. While much progress has been made, there are still some important issues that need to be addressed before such approaches can be realized for in-line monitoring and control of transient oscillations.

In: Leading-Edge Electric Power Research Editor: C. M. O’Sullivan, pp. 5-125

ISBN: 978-1-60021-981-8 © 2008 Nova Science Publishers, Inc.

Chapter 1

RADIAL-BIAS-COMBUSTION AND CENTRAL-FUELRICH SWIRL PULVERIZED COAL BURNERS FOR WALL-FIRED BOILERS Zhengqi Li∗ School of Energy Science and Engineering, Harbin Institute of Technology, 92, West Dazhi Street, Harbin 150001, P. R. China

ABSTRACT The kind of swirl coal burners is given. Radial-biased-combustion and centrally-fuelrich swirl coal combustion technology was developed. In the air and the air-particle test facilities, the single sensor hot-film and the anemometers were used to measure air and air-particle flows in the near-burner region of different swirl burners. Both cold air flow and reacting flow experiments were performed in the industrial 50, 220, 410, 670 and 1025 ton per hour boilers. On an air-particle test facility, the characteristics of the pulverized-coal concentrator with cone vanes were investigated. The influence of structure parameters, such as run parameters such as swirling vane angle and burner cone angle and length, and run parameters, such as non-swirl secondary air, central air and air supply, and primary air flow type on divergent angles, diameter and length of the central recirculation zone, mixing characteristic of the primary air and the secondary air, in-situ gas temperature and NOx formation near the burner zone, carbon in ash and NOx emission of boilers was determined with the radial-biased-combustion burner. The difference characteristics of gas/particle flow and coal combustion of the centrally-fuelrich and dual register burners were obtained. The experimental results show that the two new burners simultaneously have the ability of high combustion efficiency, flame stability, low NOx emission and resistance to slagging and high temperature corrosion. The air-surrounding-fuel combustion theory was put forward.

∗

Tel.: +86 451 86 41 8854; Fax: +86 451 86 41 25 28; E-mail:[email protected]

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Zhengqi Li

1. INTRODUCTION 1.1. Problems of Coal Combustion Technology In China, utility boilers consume about 27 % of the total coal production and generate about 70 % of the country’s electricity, and emit the majority of pollutants in cities. China’s coals are mostly low-grade with low calorific value. They either have small amounts of volatile matter or high moisture and/or ash content. Generally the flame from these coals is not stable. The ashes also have low ash fusion points, thus a tendency to slag in the furnace. The power industry requires coal combustion techniques, which have flame stability, no slagging propensities, high combustion efficiency and which meet pollution control standards. It is difficult to attain these requirements simultaneously because they are often in conflict with each other. For example, the quality of coal provided to power plants often fluctuates and is usually low-grade. It is very difficult to keep a stable flame with this type of coal, especially when the load is low. This also lowers combustion efficiency. To achieve a stable flame, supporting fuel is often used which increases the operating cost [1]. Another example of these conflicting requirements is the attempt to decrease the emission of the pollutant NOx. An effective method is to raise the pulverized coal (PF) concentration and delay the mixing of air with the coal stream. However, this method has a propensity to form slag in the furnace and also decreases combustion efficiency [2-5]. Clearly there is a challenge in meeting these conflicting requirements of the industry.

1.2. Characteristics of Swirl Coal Combustion Technology Swirl coal burners are applied widely on boilers with different capacities. Compared with the tangential firing boilers, boilers with swirl burners have the following main characteristics: (1) They can avoid imbalance of temperature of super heater in tangential firing boilers. (2) Swirl burners form their own coal flame independently and don’t influence each other. (3) There is no strict demand for furnace shape and the furnace shape with swirl burners does not need to approach to square. It is convenient to the arrangement of convection heating facilities. (4) Swirl burners are arranged evenly on front-wall and back-wall. The distribution of quantity of heat imported to furnace is relatively uniform. It reduces the slagging trend resulted from excessive high gas temperature in the central region of furnace. (5) There is no need to increase the thermal capacity of a single burner when the unit capacity increases. We just increase the width of furnace and increase the number and rows of burners. Swirl burners take high gas temperature central recirculation zone as thermal source to make air/coal mixing ignite.

1.3. Types of Swirl Coal Burners According to the types of the secondary air and the fuel concentration of primary air/coal mixing, swirl coal burners can be divided into three categories: general, air-staged combustion and fuel-bias-combustion types.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

7

1. General Type of Swirl Burners This type of burners is the burner whose secondary air is introduced to furnace without being divided into two parts and whose primary air/coal mixing is not concentrated. The following burners have the characteristics: double-volute swirl coal burner, tangential-register vane burner, axial-register vane burner and the volute burner with axial-register vanes. 2. Air-Staged Combustion Type of Swirl Burners This type of burners is the burner whose secondary air is divided into two or more parts and whose primary air/coal mixing is not concentrated, the following burners have the characteristics: (1) The dual channel swirl burner [7]：Without swirling, the primary air is ejected to the furnace. The secondary air is separated to two parts. Most of the secondary air passes through axially bent vanes, and without swirling, the rest secondary air is ejected to the furnace at a large velocity via another air channel. This type of burner has good regulating property. It is the first generation low NOx burner of BW Company. (2) The SM burner：The primary air is non-swirling and the secondary air swirls via axial bent vanes. The primary and secondary airs account for 80-90% of the total air. The rest of air is introduced to furnace through four symmetrical-arrangement nozzles around the burner. This type of burner is suit to slag tapping and dry ash extraction boilers. (3) The RSFC burner: The primary air is non-swirling and the secondary air is introduced to furnace through three concentric nozzles. Each nozzle has a swirler. Any one or all of the nozzles can be used for the injection of externally recirculated flue gas through the burner. With the RSFC burners, radial stratified combustion is formed in the near burner region, the coal burnout decreases, the NOx emission is reduced, and high temperature corrosion is prevented. (4) Volute-vane swirl burner [7]: The primary air is introduced to furnace through volute. The secondary air is separated into two swirling streams. They are injected to furnace through axially bent vanes in the inner and outer channels respectively. (5) Primary Air Exchange(PAX) burner[9]: For coals with very low volatile matter, such as semi-anthracites and anthracites, further provisions are required to obtain satisfactory ignition performances. With such coals, volatile matter has diminished to such an extent that the heat it contributes to the burner ignition zone is insufficient to sustain ignition. Temperature in the ignition zone has to be increased by controlling heat loss, returning heat from char reactions to the ignition zone, and further preheating the air and fuel prior to its introduction to the burner. BandW developed the PAX burner for these applications. The PAX burner utilizes a device in the burner nozzle to vent off primary air and replace it with hot air to preheat the fuel immediately prior to ignition. The extracted primary air, accompanied by a small percentage of the coal, is vented into the furnace and uses the main burner as its ignition source. The dual register design (derived from the Enhanced Ignition-Dual Register burner) is used.

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3. Fuel-Bias-Combustion Swirl Burner This type of burners is the burner whose primary air/coal mixing is separated to increase the fuel concentration. As the fuel concentration increases, the flame stability improves. It has two categories. The primary air/coal mixing is separated into two streams: the fuel-rich and the fuel-lean streams. The fuel-rich stream is introduced to furnace through burner, and the fuel-lean one is introduced to furnace solely at a certain furnace position. This is a high fuel concentration swirl burner. The fuel-rich and fuel-lean streams are introduced to furnace through different channels of burner. This is the fuel-rich and fuel-lean combustion swirl burner. (1) High Fuel Concentration Swirl Burner (1) Former Soviet Russia’s high fuel concentration swirl burner: Coal concentration combustion experiments were done on a 30×105kw T∏∏-210A type boiler. The air/coal mixing was conveyed in a new tubule by compressed air and the fuel concentration of the mixing was as large as 40-50kg (coal)/kg (air). The primary air duct only conveyed air. Before arriving at the burner outlet, pulverized coal was injected to the primary air at a suitable position and a fuel concentration of 0.9 kg (coal)/kg (air) was gotten. The results showed that air/coal mixing ignited earlier and NOx emission sharply decreased.

Figure 1. a) NSZ burner with external fuel enrichment and b) NSW burner with internal fuel enrichment.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

9

(2) Low-NOx burner with external coal concentrator(NSZ burner, see Figure 1a)[10]: Swirl type concentrator is inserted between coal mill and burner. Enriched coal-air mixing flows through the burner fuel nozzle. In order to intensify mixing of fuel with hot internal recirculation gas, this enriched mixing is supplied via four separate ducts, parallel to the burner axis. Air and transporting gas from concentrators are released into the combustion chamber above the burners.

(2) The Fuel-Rich and Fuel-Lean Combustion Swirl Burner (1) Dual Register(DR) burner and Enhanced Ignition-Dual Register(EI-DR) burner(see Figure 2) [9]: The EI-DR burner has axial vanes in the inner secondary air duct and tangential or axial vanes in the outer secondary air duct and the swirling direction of the inner and the outer secondary airs is identical. Pulverized coal is separated and concentrated into the wall zone of the primary air duct by the conical diffuser. Then, the coal rich zone is near the wall zone of the primary air duct and the coal lean zone is in the central zone of the primary air duct. The structures of DR and EI-DR burners are the same. The designed primary air velocity of EI-DR burner is less than that of DR burner, and The designed secondary air velocity of EI-DR burner is larger than that of DR burner. (2) Low-NOx burner with an internal coal concentrator (NSW burner, see Figure 1b) [10]: Such complex system of NSZ burners is a source of many problems, especially in boiler reconstruction. Usually there is no room available for fitting release ducts from concentrators, while many pipelines make access to the boiler difficult and complicate maintenance and repairs. It then proved necessary to design a new burner with an internal coal concentrator. Enriched coal-air mixing is directed towards the axis of flow in the form of several slightly swirled streams, while lean mixing is directed to the secondary air stream. When burner operates properly, ignition takes place in the zone of the finest released particles and thus a shield is formed, protecting the main combustion zone from the secondary air inflow. Secondary air is supplied via two coaxial ducts outside the main fuel nozzle, and fuel is mixed with the secondary air outside the “rich” combustion zone. Secondary air flow in both these ducts is controlled. (3) Dense dilute dual-channel burner (see Figure 3) [11]: Along the radial direction, from outside to inside, they are outer secondary air, inner secondary air, primary air in sequence. The inner and outer secondary airs become swirling after they pass through axial-register vanes. The swirling intensity of the two air streams can be regulated by pulling or pushing the axial-register vanes. The primary air/coal mixing becomes swirling through bent vanes in primary air duct. After the swirling coal-air mixing entering the four axial arrangement channels, the swirling of air is restrained and swirls weekly. Because of coal inertia, the pulverized coal gathered in near pulverized coal collector region and four fuel-rich and fuel-lean air/coal mixings are formed. Then fuel-bias combustion along the circle direction is gotten.

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Figure 2. EI-DR burner and the position of the monitor pipe (the dimensions are in mm): (1) particle deflector, (2) primary air duct, (3) inner secondary air duct, (4) outer secondary air duct, (5) watercooled wall, (6) axial vanes (the angle is 60º), (7) tangential vanes (the angle is 25º), (8) conical diffuser, (9) monitor pipe.

Figure 3. Dense dilute dual-channel burner: (1) cone, (2) axial vanes, (3) flame stabilization ring, (4) axial vanes, (5) pulverized fuel feeder, (6) throttle valve, (7) bent vanes, (8) central pipe, (9) primary air duct, (10) pulverized coal distributor, (11) pulverized coal collector, (12) inner secondary air duct, (13) outer secondary air duct.

1.4. The Prospect of Swirl Coal Combustion Technology In early stage, it was thought that we should use large swirl number and central recirculation zone to have the coal flame stable. We ignored the factor of coal concentration.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

11

So, the general and air-staged combustion burners adopt various measures to increase the swirl number and central recirculation zone. Because the fuel concentration of primary air/coal mixing is little, especially with swirling primary air, most of the pulverized coal is threw off to the low gas temperature region near the secondary air under the effect of centrifugal force. There is a little coal near the high gas temperature central recirculation zone. There is no zone where both the fuel concentration and the gas temperature are high. The quality of coal provided to power plants often fluctuates and is usually low-grade. It is very difficult to have flame stable, especially when the load is low. Xu et al. [12] has experimentally studied the gas flow fields, temperature distribution and gas composition distribution in pulverized coal precombustion chamber, numerically calculated particle trajectories in combustion processes for different particle diameters and presented a new flame stabilization theory - “three high zone” principle. This principle says that if flow carrying over pulverized coal particles forms a high concentrated coal particle zone that is also a high-temperature and appropriate oxygen zone (called “three-high zone ”) at a local region near the burner mouth, this zone can stabilize pulverized coal flame. Three-high zone theory makes us have a good understand of the effect of high fuel concentration. It can give us the reason why the flame stability is bad when both the swirl number and central recirculation zone are large. Air-staged combustion burner can decrease the formation of NOx effectively. The outer secondary air segregates the water-cooled wall from reducing atmosphere in the burner center zone. It can prevent furnace wall and water-cooled wall from high temperature corrosion and slagging propensities, but the carbon-in-ash increases to some extent. High fuel concentration has good flame stability, especially at low load. Since the fuel-lean air/coal mixing carries off some air, the carbon-in-ash increases to some extent. Fuel-rich and fuel-lean combustion technology combines high fuel concentration technology and air staged combustion technology together. Meanwhile, with the fuel-rich and fuel-lean mixings introduced to furnace stratified, the burner intensifies further staged combustion. Some fuel-rich and fuellean combustion technologies have high combustion efficiency, flame stability, no slagging propensities and high temperature corrosion and low NOx emission. It is the prospect of the swirl combustion technology.

2. RADIAL BIASED COMBUSTION SWIRL COAL BURNER 2.1. Concept of Radial Biased Combustion Swirl Coal Burner Qin proposed the radial biased combustion swirl coal burner in 1993 [13]. A fuel concentrator is installed inside the fuel-conveying duct which radially separates the primary air/coal mixing into two jets of different fuel concentrations (Figure 4). The fuel-rich primary air/coal mixing is in the inner annulus and the fuel-lean one is in the outer annulus. Surrounding them is a secondary air jet, which is also divided into two annular parts – the inner swirling secondary air jet and the outer non-swirling secondary air jet. The swirler is made of axial vanes.

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Figure 4. The RBC burner: (1) wall, (2) non-swirl, (outer) secondary air duct, (3) Swirler, (4) swirl (inner) secondary air duct, (5) primary air duct, (6) central pipe, (7) flame igniter, (8) damper, (9) enricher, (10) fuel-lean primary air-coal mixture duct, (11) fuel-rich primary air-coal mixture duct.

2.2. Experimental Research on Pulverized-Coal Concentrator with Cone Vanes 2.2.1. Structure And Parameters of Pulverized-Coal Concentrator with Cone Vanes Figure 5 shows the structure of pulverized-coal concentrator with cone vanes[14]. Under the impact of cone vanes installed inside the primary air duct, the majority of the pulverized coal carried by the primary air is concentrated in the central zone of the primary air duct, with a fraction of coal in the peripheral zone of the primary air. Passing the cone vane, the fuelrich primary air/coal mixing is formed behind the concentrator and the peripheral primary air with a fraction of pulverized coal flows over the cone vanes. The fuel-lean primary air/coal mixing is formed outside the fuel-rich primary air/coal mixing. In Figure 5, R2 is the radius of the primary duct; R1 is the radius of the central pipe; and R is the radius of the outlet of the end vane. The principal parameters of the concentrator include a few of vanes n, the angle of vane β, the setting angle of vane α, the vane length L, the axial projection length of vane L1, the space between vanes L2, the coverage ratio of vanes ε, the blockage radio ψ and the area ratio of the fuel-rich primary air, etc. The coverage ratio of vanes was calculated from the following equation:

ε = H1 H2

(1)

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

13

where H1 is the radial projection length of overlap between two vanes, H2 is the radial projection length of vanes (see Figure 5), with m per unit. The blockage radio was calculated using the following equation:

ψ = 1 − f0 f

(2)

where f0 is the area of the end vane outlet (see Figure 5), f is the area of the primary air duct, with m2 per unit.

Figure 5. The enricher with cone vanes.

The area fraction of the fuel-rich primary air/coal mixing was calculated from the equation of

fr , where fr (m2 per unit) is the area of outlet of the fuel-rich primary air. f

The performance parameters of concentrator include：the air ratio Ra , the coal concentration ratio Rrl, the enriching ratio Rr, the resistance coefficient ξ. The air ratio Ra is the ratio of the air feed in the fuel-rich primary air/coal mixing to the air fed in the fuel-lean primary air/coal mixing. The coal concentration ratio Rrl is the ratio of the coal concentration of the fuel-rich primary air/coal mixing to that of the fuel-lean primary air/coal mixing. The enriching ratio Rr is the ratio of the coal concentration of the fuel-rich primary air/coal mixing to that of the primary air/coal mixing. The resistance coefficient was calculated using the following equation:

ζ = ΔP ρuin 2 2g

(3)

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where ΔP (Pa per unit) is the static pressure difference between the inlet and outlet of concentrator, ρ (kg/m3 per unit) is the density of the primary air, and uin (m/s) is the inlet velocity of the primary air.

2.2.2. Impact and Rebound Phenomena of Particles on Wall of the Cone Vane Figure 6 shows the impact and rebound phenomenon of particles on wall of the cone vane. The velocity of a rebounding particle is determined using the restitution coefficients,

et ( ≡

V2t V ) and en ( ≡ 2 n ), that is measured by experiments. The rebound particle velocity V1t V1n

components, V2t and V2 n are then calculated by the following manner:

V2t = etV1t

(5)

V2 n = etV1n

(6)

Figure 6. Impact and rebound phenomena of particles on wall.

It should be noted here that the restitution ratios, which are determined experimentally, vary according to the flow velocity, and the combination of particles and target materials. The vane material generally is wear-resistant cast steel or ceramic so that the service life of concentrator is more than an overhaul period (always be four years). The performances of pulverized-coal impacting on wear-resistant cast steel or ceramic are being investigated by us. Results obtained by other researchers can be taken as reference. The expressions for the restitution ratios for sand particles impacting on the 410 stainless steel from Grant and Tabakoff [15] are as following:

et =

V2t = 1.0 − 2.12 β1 + 3.0775β12 − 1.1β13 V1t

(7)

en =

V2 n = 1.0 − 0.4159 β1 + 0.4994 β12 − 0.292β13 V1n

(8)

The expressions for the restitution ratios for 157-177 μ m silica sand particles impacting on the target materials: 2024 AL, Ti 6-4 and INCO718 from Wakeman and tabakoff [16] are as following:

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

et =

V2t = 0.953 − 0.000446 β1 + 0.00000648β13 V1t

en =

V2 n = 1.0 − 0.0211β1 + 0.00228β12 − 0.000000876β13 V1n

15

(9) (10)

2.2.3. Structure Optimization for Cone Vane of Pulverized-Coal Concentrator Experiments were carried out on a gas-particle two phase test facility to optimize the structure of pulverized-coal concentrator. The full industrial-scale concentrator studied in the experiments was designed for a 670-tph coal-fired boiler. A scale ratio of 1:3 was employed. Coal ashes from a power plant were used to be the particles. The ratio of the model size to the actual burner size is 1: 3. For the concentrator model, the value of 2 time of R2 is 165 mm, the value of 2 time of R is 65mm, n is 3. The particle concentration is from 0.26 to 0.64kg (coal)/kg(air). Table 1 shows the experiment results. The results indicate that with three vanes, the air ratio is up to 1; the coal concentration ratio is up to 2; the enriching ratio is up to 1.4; and the resistance coefficient is less. The industrial experiments of the concentrator show that with this coal concentration ratio the burner can keep a stable flame. Table 1. Concentrator model structure parameters and experimental results [14] case L1(mm) L2(mm) α(°) β(°) R(mm) Ra Rrl Rr ξ

1 20 50 15 15 53.8 1.064 2.56 1.42 2.31

2 20 55 13.6 15 54 1.112 2.49 1.40 2.36

3 20 45 16.7 15 53.7 0.992 2.29 1.39 1.97

4 20 40 10 10 66.7 1.08 1.74 1.26 2.34

5 20 45 20 25 45.6 0.871 1.70 1.28 2.35

6 20 45 25 10 42 0.96 1.77 1.29 2.01

2.2.4. Improved Pulverized-Coal Concentrator with Cone Vanes In order to reduce NOx emissions greatly, the coal concentration ratio should be large, which need to be installed more vanes. With increasing of the number of vanes, resistance of concentrator increases, which lead to pulverized coal plugging in the concentrator and difficulty to set vanes. Structural improvement has been made to separate the end vane and isolation ring between the fuel-rich primary air/coal mixing and fuel-lean primary air/coal mixing. When the fuel-rich primary air/coal mixing passes through the zone between the end vane and isolation ring, the air in it can diffuse into the fuel-lean primary air/coal mixing along radial direction and the pulverized coal still gathers in the fuel-rich primary air/coal mixing under inertia effect. Thus, the improved concentrator achieves a larger concentration ratio with less resistance. Figure 7 shows the improved pulverized-coal concentrator with cone vanes, where x is the distance from the end vane to the isolation ring, H is the length difference between R2 and R1, and Rr is the radius of the inlet of isolation ring.

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Figure 7. The improved enricher with cone vanes.

2.2.5. Structure Optimization and Performance of the Improved Pulverized-Coal Concentrator Table 2 shows the experimental parameters. Table 2. Structure parameters of the improved coal concentrator model [14]

case L1 ( mm )

1

2

3

4

5

6

7

8

9

10

11

50

50

50

50

50

55

60

65

50

50

50

β (°)

20

25

35

40

30

30

30

30

30

30

30

L2

3.0

3.0

3.0

3.0

3.0

3.0

3.0

3.0

2.0

2.5

3.5

0.39

0.48

0.65

0.73

0.56

0.62

0.68

0.73

0.56

0.56

0.56

Ψ

L1

1. Resistance Characteristics (1) Influence of Dip Angle of Vane on Resistance The influence of dip angle of vane β on resistance was determined by experiments in cases 1-4. The expression for resistance coefficient ξ was as following:

ξ = 0.53 + 0.57

β 20

(11)

The expression indicates that with the increasing of dip angle of vane the resistance of concentrator and the angle of air flowing over vanes to the fuel-lean primary air/coal mixing increases and the value of

f0 f decreases. With the value of 0 decreasing, the air velocity f f

increases. It is the main reason of resistance increasing.

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17

(2) Influence of Vane Length on Resistance The influence of vane length L on resistance was investigated by experiment in cases 5-8. The expression for resistance coefficient ξ was as following:

⎛ l ⎞ ⎟ ⎝ 50 ⎠

ξ = −0.70 + 1.74 ⎜

(12)

The expression indicates that with the vane length increasing the resistance of concentrator increases. With length of vane increasing, the radial projection length of the vanes increases and the flow area of the fuel-rich primary air/coal mixing decreases.

(3) Influence of the Distance between Vanes on Resistance The influence of the distance between vanes L2 on resistance was determined by experiment in cases 9-11. Figure 8a shows the relation between resistance coefficient ξ and normalized distance

L2 . Resistance coefficient decreases and then increases with the L1

normalized distance increasing. With the normalized distance increasing, the air/coal flow area of the fuel-lean primary air/coal mixing increases and the angle of air flowing to the fuellean primary air/coal mixing. It results in the resistance coefficient decreases. With the normalized distance increasing further, eddies behind the vanes becomes large. It results in the resistance increases. The experiment indicated the minimum resistance coefficient ξ is obtained when normalized distance

a

L2 is 2.5. L1

b

Figure 8. Influence of normalized distance between vanes (a) and blockage ratio (b) on resistance coefficient [11].

(4) Influence of Blockage Radio on Resistance The blockage radio is a parameter which synthetically shows the influence of the angle of vane, the vane length, the number of vanes and air flow areas of the fuel-rich and fuel-lean

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primary air/coal mixings. Figure 8b shows the relation between resistance coefficient ξ and blockage radio ψ. The experimental result indicates that the blockage radio could express the variation of resistance well. In the cases that the blockage radio ψ is less than 0.6, the resistance coefficient increases slowly while the blockage radio increases. In the cases that the blockage radio ψ is more than 0.6, the resistance coefficient increases remarkably while the blockage radio increases. Thus, considering the reduction of resistance of the concentrator, it is recommended that the blockage radio for concentrator design should be less than 0.6. The experiment results indicate that the resistance of the improved coal concentrator reduces by 50% compared with the former concentrator. The influence of distance between the end vane and the isolation ring on distribution of air flow. In case 6, the distribution of air axial velocities at the inlet of the isolation ring with different x (see Figure 7) were measured. Figure 9 shows the results of measurement, where

U m is the axial velocity of air flow in the primary air duct.

Figure 9. Influence of distance from the end blade to the isolation ring on distribution of air axial velocities at the inlet of the isolation ring [11].

In the radial direction, the air axial velocities of the fuel-rich primary air/coal mixing is larger than that of the fuel-lean primary air/coal mixing. The distribution of air axial velocities becomes flat while x increases. At x=0.23H, partial region is in the eddy current zone.

fr X f and normalized distance H on Figure 10 shows the influence of normalized area the air ratio Ra. For radial biased combustion burner, it is recommended that the air ratio Ra should be in the range from 0.75 to 1. The influence of distance between the end vane and the isolation ring on radial separation performance.

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Figure 10. Influence of fr/f and X/H on the air ratio [11].

Figure 11. Influence of fr/f and X/H on the coal concentration ratio [11].

fr X f Figure 11 shows the influence of normalized area and normalized distance H on X the coal concentration ratio. With H from 0 to 0.5, the minimum coal concentration ratio is X obtained. The coal concentration ratio increases and then decreases with H increasing. The

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X maximum coal concentration ratio is achieved while H is 1. The phenomenon is caused by the inertia effect of the pulverized coal. In the region near the outlet of the end vane, the pulverized coal still move into the zone of the fuel-rich primary air/coal mixing under the

X inertia effect. As H is larger than 1, the coal concentration in the zone of the fuel-rich primary air/coal mixing reduces because the pulverized particles diffuses into fuel-lean primary air zone in the radial direction.

fr X We should choose appropriate values of H and f to achieve the appropriate air ratio fr X and coal concentration ratio. When H is 1 and f is in the range from 0.3 to 0.4, the air ratio is in the range from 0.7 to 1 and the coal concentration ratio is in the range from 5 to 8.

2.3. Effect of Structure Parameters on Gas/Particle Flow Near the Burner Region 2.3.1. Effect of Angle of Secondary Air Swirling Vane on Characteristics of Isothermal Flow Issuing from the Radial Biased Combustion Burner A number of axial fixed swirling vanes are installed in the secondary air duct of radial biased combustion burner. The vanes were curved according to a special profile line and had an angle of β with burner axis. After passing through the vanes, the secondary air rotates and develops into a swirling jet at a certain swirling momentum at the secondary air outlet. For adjusting the swirl number of the secondary air near the burner outlet, the secondary air is divided into two flows, a non-swirling outer secondary air flow and an inner swirling secondary air flow. The swirl number can be adjusted by controlling the ratio of inner secondary air to outer secondary air. Some relations between vane profile and design parameters have been presented in document [17]. Some studies have shown that to keep the length of the straight section at the vane outlet and the bending radius of the vanes at appropriate values, the non-dimensional height of the vanes should range from 1.5 to 1.9. On the condition that the number, angle and cover ratio of vanes remain constant, an optimized vane profile can be obtained by choosing an appropriate non-dimensional vane height. And a reasonable design of the shape of the vane can make the secondary air flow out into the furnace at the angle that is the same as the angles of the vanes. The angle β of vanes influences deeply on the characteristics of swirling jet in the near burner region. A cold flow experiments in a small-scale burner were carried out to investigate the effect of the angle of the vane (the experimental angles are 55°, 58°, 60° and 65°) on the characteristics of the flow issuing from the burner [18]. The original burner is used on a coal-fired 670-tph utility boiler. The ratio of the small model to the original burner is 1: 3. An IFA300 constant-temperature anemometer system with a single-sensor hot-film probe was used to measure the mean velocities and turbulent parameters of swirling jet issuing from the burner by method of

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21

rotating the probe. Ribbons tied to a coordinate-frame were used to measure the airflow direction in the flow field. Table 3 shows the experimental parameters. Table 3. Experimental parameters for different swirling vane angles Primary air ratio, %

Secondary air ratio, %

Inner Swirl secondary air ratio, %

Velocity of the primary air, m/s

Axial velocity of the secondary air, m/s

Reynolds number at the burner outlet

19.5

80.5

85

8.5

12.0

1.6×105

1. Swirl Numbers of Airflow Issuing from the Burner at Different Swirling Vane Angles Assuming that the airflow passing through the vanes is idealized and non-viscous, the angle between the airflow direction and the burner axis is β . Thus the related expression for the tangential velocity w and the axial velocity u was w = u tan β . Ignoring the influence of the static pressure on the axial momentum, the swirl number of secondary air S can be calculated as follow: R

S=

2 2 ∫ u tan β r dr 0

R

∫ u rd dr 2

0

0

R

∫ u r dr 2 2

= tan β

0 R

∫ u rd dr

= tan β f ( u, R, d 0 )

2

(13)

0

0

where R is the divergent radius of airflow at the burner outlet and r is the radius of the measurement point. In the swirling jet, the secondary air takes most of the part, thus the swirl number of swirling jet issuing from burner is determined by the tangent of swirling vane angle. As shown in Equation (12), the swirl number rises with the increase of β . Comparison between the experimental data of the swirl number and tangent of swirling vane angle is shown in Figure 12, and the value of f ( u , R, d 0 ) is approximately set to 1.

2. Influence of the Swirling Vane Angle on the Flow Resistance to the Secondary Air Because the ratio of the non-swirling outer secondary air is relatively small (no more than 25%), the main resistance to the secondary air occurs when the swirling secondary air passes through the swirling vanes. The resistance to the inner secondary air passing through swirling vanes includes frictional resistance and local pressure loss. The frictional resistance h f 1 was calculated from the following equation:

h f 1 = k1

ρu 2 2g

(14)

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where k1 is the frictional resistance coefficient which is determined by surface roughness, length and air humidity of the vanes,

Figure 12. Effect of

β

ρ ( kg / m3 ) is air density.

on swirl number and resistance.

When the airflow passes through the swirling vanes whose angle is increases from u to

ρ , the velocity

u , which leads to a local pressure loss. The local pressure loss h f 2 cos β

can be calculated as follow:

h f 2 = k2

ρ ⎛ u2

ρ u 2 tan 2 β 2⎞ − = u k ⎜ ⎟ 2 2 g ⎝ cos 2 β 2g ⎠

(15)

where k2 is the coefficient. It stays constant in a certain vane structure. Then, the resistance to the inner secondary air passing through swirling vanes can be calculated as follows:

hf = hf 1 + hf 2 = where

ρu 2 2g

(k

2 1 + k 2 tan β ) = ξ

ρu 2

(16)

2g

ξ is the resistance coefficient which can be calculated from the expression

ξ = k1 + k2 tan 2 β . When the swirling vane angle β is ＞45°, k1 is relatively small and can be ignored, then ξ is positively proportional to the square of the tangent of β . Assuming that the value of k2 is 1, the relation between ξ and β should follow the curve shown in Figure 12. The resistance coefficient is small when β is ＜50°. When β ranges

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

23

β is above 70°, the resistance coefficient rises sharply with increase of β . So, the increase of β not only enlarges the length of central from 50° to 65°; it begins to increase. When

recirculation zone, but also increases the resistance to the secondary air simultaneously. It is suggested that β should not be over 70° in the design of swirling vanes.

3. Influences of Swirling Vane Angle on the Flow Field Structure and the Mean Velocity Distribution of the Swirling Jet Issuing from the Burner Table 4 shows the length LCR 2 and maximum diameter dCR 2 of the central recirculation zone of the swirling jet issuing from the burner. d is the diameter of the none-swirling secondary air cone. When

β increases from 55° to 65°, dCR 2 increases by 20% and LCR 2

increases by 30%. The ratio of the maximum mass flux of the recirculation air in the central recirculation zone to the mass flux of primary air (defined as the maximum normalized recirculation ratio) is shown in Table 4. The maximum normalized recirculation ratio can basically shows the mass flux of the recirculation flow in the central recirculation zone because the mass flux of primary air has no remain constant. While β is ≥60°, the maximum normalized recirculation ratio is up to 2, which shows that the recirculation gas can supply enough heat for the ignition of fuel-rich primary air/coal flow and satisfy the flame stable combustion for low-grade coal. Table 4. Influence of swirling vane angle on the length of central recirculation zone and maximum normalized recirculation ratio

β

55 °

58 °

60 °

65 °

LCR 2 / d

1.49

2.0

2.0

2.0

dCR 2 / d

0.8

0.86

1.0

1.0

Maximum normalized recirculation ratio

1.160

1.261

1.909

1.931

The distribution of the mean axial and the tangential velocities at different

β (55° or

60°) of the swirling jet are shown in Figure 13, where x is the distance from the burner outlet to the velocity point, U0 is the mean velocity of the airflow at the outlet of the burner. As shown in Figure 13a, near the burner outlet ( x / d = 0.0 ), the axial velocities have two peak values along the radial direction. The higher one is the velocity peak of the secondary air; the lower one is the velocity peak of the primary air. In the central pipe of the burner, there’s no air fed into the furnace, so the axial velocity is low near the outlet of the central core, which helps to form the front stationary point of central recirculation zone. The central core and the cone between the primary air and swirling secondary air (see Figure 4) have guide effect on the primary and secondary air. Also, large turbulent mass exchange occurs because of the great velocity gradient between primary air and secondary air. Thus, the primary air and the secondary air mix rapidly and the velocity peak of primary air disappears at the crosssection x / d = 0.25 , which shows that the primary air has well mixed with the secondary air. At a large swirling vane angle, the velocity peak of the jet decays rapidly.

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Figure13. Profiles of axial mean velocities (a) and tangential mean velocities (b) in the jet with different swirling vane angles.

When

β is set to 55 ° , the swirl effect of the jet and the centrifugal force of fluid

micelles are weakened. Most of the air is concentrated in the central zone of the jet and the flare angle of the jet reduces. Therefore, the length of central recirculation zone became short. As shown in Figure13b, at different angles of β (55° or 60°), only one tangential velocity peak appears. The tangential velocity is low in the central recirculation zone and in the flow field near the primary air outlet because the primary air is non-swirling and the airflow in the central recirculation zone is mostly the axial backflow. Influenced by the swirling vanes, the maximum tangential velocity appears in the airflow field near the secondary air outlet. As the swirling vane angle increases, the maximum tangential velocity and the swirl number rise, and the tangential velocity decays rapidly. It shows that the primary and secondary air is mixed intensely. Furthermore, the tangential velocity decays more rapidly than the axial velocity and in highly swirling flow the tangential rotation disappears soon because of the large turbulent mixing. At the cross-sections x / d ≥ 1.0 , the tangential velocity of the jet falls down to zero, and the flow becomes wholly axial. The differences between the two radial velocities with different β s are very few.

4. Turbulent Stress Distribution in Swirling Flow Field at Different Vane Angles Figure 14 shows the spatial distributions of normalized turbulence fluctuating root-meansquare (RMS) velocities and turbulence normal stresses at different vane angles. Figure 14a shows that the turbulent fluctuating RMS velocities are low in the region of central

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

25

recirculation zone and jet boundary. It reaches a peak value in the interface of recirculation zone and the mainstream zone.

Figure 14. Profiles of normalized root - mean - square velocities and normalized turbulent stresses with different swirling vane angles.

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As the vane angle increases, the peak value of the turbulent fluctuating velocity moves outward along radial direction and the turbulent fluctuating velocitoes at the outlet increas but decay rapidly. At x / d ≥ 0.5 , the turbulent fluctuating velocities at a larger vane angle are already less than that at a smaller vane angle. Distribution of turbulence normal stress is nonhomogeneous. It is low in the region of central recirculation zone and the jet boundary and reaches a radial maximum value in the region near the boundary of recirculation zone and the region of the mainstream zone of secondary air.. However, , the turbulent normal stress in the region of swirl burner outlet is not the maximum in the whole flow field. As the flow expands downstream, the turbulent energy produces continually. At x / d = 0.25 − 0.5 , the turbulent normal stress reaches a maximum value and gradually declines afterwards. Thus, this region with intense turbulent fluctuation is advantageous for pulverized-coal combustion. In practical operation, the radial biased combustion swirl burner increases the pulverized-coal concentration in this region, which improves the ignition of air/coal mixing. It also improves the flame stability and combustion intensity. Turbulent normal stress is obviously anisotropic. '2

'2

The maximum values of axial normal stress u and tangential normal stress w are high, which indicates that the turbulent flow fluctuates more intensely in the two directions. '2

Meanwhile, the normal stress v in the radial direction is smaller than stresses those in the other two directions near the burner outlet, and gradually increases downstream. As the swirling vane angle increases, the turbulent fluctuation intensity of airflow obviously rises at the outlet and the turbulent mixing is enhanced. The maximum turbulent normal stresses in the three directions are raised more than two times. The higher turbulent fluctuation intensity is advantageous for the burning of the air/coal flow. The intense mixing in the early stage of the jet quickens the dissipation of turbulent energy; so the turbulent energy becomes lower in the later stage of the jet. At x / d = 1.0 , u and w at the smaller. '2

'2

at larger vane angle are less than those

5. Influence of Swirling Vane Angle on the Mixing Characteristics of Swirling Jet Figure 15 shows the ratio distribution of the fuel-rich primary air flow (Rrp) and the whole primary air flow (Rp) at different vane angles by the temperature tracing method. The fuel-rich primary air ratio at a certain point is the ratio of the fuel-rich primary air mass flux to the total mass flux at the same point in the flow field. The Primary air ratio can be obtained in the same way. The results show that the peak of the fuel-rich flow ratio reaches 40% near the outlet of burner. The peak locates at the cross-section r / d = 0.25 which is close to the central recirculation zone near the boundary of central recirculation zone, the ratio of the fuelrich primary air flow remains high, which is advantageous for the heat and mass transfer between the fuel-rich primary air /coal mixing and the high temperature gas in the recirculation zone. The peak ratio of the fuel-rich primary air flow does not fall until the flow reaches the cross-section x / d = 0.5 . The high ratio in the early combustion of the fuel-rich primary air flow can improve the flame stability, and lower the emission of fuel-NO by forming a reducing atmosphere. As the vane angle increases, the peak ratio of the fuel-rich primary air flow moves outwards in the radial direction and the mixing of the fuel-rich primary air flow and the gas in central recirculation zone is weakened. This is because as the swirl number of secondary air

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

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increases, the divergent angle of jet rises and the primary air expands outward under the driving of secondary air.

Figure 15. The ratio distribution of fuel-rich primary air Rrp （a） and primary air R p （b） at different vane angles.

Figure 15b shows the ratio distribution of the fuel-rich primary air flow which shows the mixing of the primary and secondary air. In the region near the outlet ( x / d = 0.25 ), the influence of the vane angle on the fuel-rich primary air ratio is not significant, and the maximum ratio can reach about 80%. The increase of vane angle will enhance the mixing of primary air and secondary air in the downstream and uniform the distributions of primary air ratio uniform in the whole flow field. Figure 16 shows the axial decay of the maximum ratios of the fuel-rich primary air flow, Rmrp, at different vane angles. In the region ranging from x / d = 0.25 to 1, when the vane angle becomes larger, the mixing intensity of the primary and secondary air was enhanced and the maximum ratio of the fuel-rich primary air flow declines much more rapidly. Also the distance, at which the ratio of the fuel-rich primary air flow is kept high, is shortened. At the downstream of the cross-section x / d = 1.0 , the mixing of the primary and secondary airs is already uniform. The ratio of the mixing flow flux to the entrance flux is 1：4. It indicates that the mixing of the airflow at the outlet of the swirl burner is intense, and the distance for air-flow to be uniformly mixed is short. The increase of the swirl number can strengthen the mixing of primary and secondary air. The gas/particle two-phase experiment with PDA shows a remarkable phenomenon that the diffusion velocity of particle-phase is slower than that of the gas phase [19]. In the region near the recirculation zone, the diffusion

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velocity of pulverized-coal particle obtained from the two-phase experiment is slower than that of fuel-rich primary air ratio obtained from the single phase experiment.

Figure 16. The axial decay of the maximum ratio of the primary air Rmrp at different vane angles.

As mentioned above, as the vane angle increases, the length of central recirculation zone and the divergent angle of jet increases as well. The normalized backflow ratio and turbulent fluctuation of airflow increase as well. It is advantageous for the coal flame stability. But when the vane angle increases to a certain extent, the resistance to the secondary air become greater, and the primary air and secondary air mixed much earlier. The peak value of the fuelrich primary air ratio moves outwards in the radial direction. The particle load of the fuel-rich primary air declines near the recirculation zone. It is disadvantageous for the ignition of air/coal flow. So, the vane angle should be chosen in the range between 60 ° and 65 ° .

6. Conclusion Swirl number S is proportional to the tangent of vane angle β . As the vane angle β increases, the swirl number becomes higher. Resistance coefficient of the secondary air is proportional to the squared value of vane angle β . When the vane angle is over 70 ° , the quick increase of local resistance coefficient of the vane will make the resistance to the secondary air too large. When the vane angle increases, the tangential velocity of the flow at the outlet rises. The axial velocity peak moves outward in the radial direction. The divergent angle of jet and the length of central recirculation zone increase. The recirculation rate also increases to supply enough heat for the ignition of the fuel-rich primary air/coal mixing. When the vane angle β increases, the turbulent fluctuation velocities increase also, and its maximum value moves outwards in the radial direction. Distribution of turbulent normal stress is non-homogeneous and anisotropic in the airflow near the outlet. At the interface of the mainstream and the recirculation zone which ranges from x / d = 0.25 to 0.5, the

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turbulent normal stress has a maximum value. The intense turbulent fluctuation in this region is advantageous for the combustion of pulverized-coal. When the vane angle increases, the turbulent normal stresses in the three directions obviously rises. The maximum value can be 2 times as large as before. It shows that the turbulent fluctuation and mixing are strengthened. And the mixing of jet also is also strengthened with the increase of vane angle.

2.3.2. Effect of Division Cone Angles between the Fuel-Lean Primary Air/Coal Mixing and the Swirling Secondary Air on Particle-Laden Flows Near the Burners Figure 17 shows burner cones. The cone between the fuel-lean primary air/coal mixing and the swirling secondary air has the influence on the secondary air flow direction, the secondary air velocity and the mixing of primary and secondary airs. A two-dimensional particle dynamics anemometer was used to measure gas/particle flow characteristics with division cone angles of 10°, 22.5°, and 30° [20]. The size ratio of the burner model to the utility burner in a 670-tph coal-fired boiler was 1: 6. The particles, up to 8μm, were used to measure the airflow velocity and turbulence, whilst particles of diameter in the range from 10 to 100μm were to represent the particle (solid) phase flow.

Figure 17. Burner cones: 1. Core, 2. the cone between the fuel-rich and the fuel-lean primary air/coal mixtures, 3. the cone between the fuel-lean primary air/coal mixture and the swirling secondary air, 4. the cone between the swirling and non-swirling secondary air, 5. non-swirling secondary air cone.

1. Effect of Cone Angles of Air and Particle Velocities Figures 18, 19 and 20 show profiles of mean axial velocities. With the increase of the cone angle, the positions of peak values of air and particle mean axial velocities move outward along the radial direction, the peak values reduce and the jet decay is faster. It shows that the increase of the cone angle strengthens the secondary air radial diffusion. Otherwise, with the increase of the cone angle, the divergent angle of the jet increases, and then the cross-section of the jet enlarges. It results in the decrease of air and particle mean axial velocities.

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Figure 18. Profiles of air and particle mean axial velocities with the cone angle of 10°.

Figure 19. Profiles of air and particle mean axial velocities with the cone angle of 22.5°.

Figure 20. Profiles of air and particle mean axial velocities with the cone angle of 30°.

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Figures 21 and 22 show the effect of cone angles ( α1 ) on the maximum diameter of central recirculation zone ( dCR 2 ) and the air divergent angle, where r is the distance between the burner center line and the boundary of the jet. The air divergent angle of jet is the air semi-velocity divergent angle which is the angle formed by two air semi-velocity boundaries. The air semi-velocity boundary is the line where the mean axial velocity is half of the maximum mean axial velocity at a cross-section. With the increase of cone angle, dCR 2 and semi-velocity divergent angle increases. The reason is as follows: the larger the cone angle is, the more swirling secondary air diffuses outward and the larger the swirling secondary air radial momentum are. Furthermore, the larger the cone angles are, the larger the area of the fuel-lean primary air is, and the less the primary air velocity is. The increase of the central recirculation zone size increases the recirculation flux of the high-temperature gas. It is advantageous to coal combustion.

Figure 21. Effect of cone angles on the maximum diameter of central recirculation zone.

Figure 22. Effect of cone angles on air semi-velocity boundary.

Figures 23, 24 and 25 show profiles of mean tangential velocities. Profiles of air and particle mean tangential velocities with different cone angles are similar. With the increase of cone angle, the positions of air and particle mean tangential velocities move outward along

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the radial direction. The mean tangential velocities increase slightly because the increase of area of swirling secondary air outlet.

Figure 23. Profiles of air and particle mean tangential velocities with the cone angle of 10°.

Figure 24. Profiles of air and particle mean tangential velocities with the cone angle of 22.5°.

Figure 25. Profiles of air and particle mean tangential velocities with the cone angle of 30°.

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Figures 26, 27 and 28 show profiles of air and particle axial Root Mean Square (RMS) velocities. Figure 29, 30 and 31 show profiles of air and particle axial RSM velocities. Air and particle axial RSM velocities are similar to air and particle tangential RSM velocities. The RSM velocities are less in the central recirculation zone. They are larger near the boundary of central recirculation zone and in the region of swirling secondary air where there are larger shear layers. With the effect of airflow diffusion and turbulent transport, the air and particle axial and tangential fluctuation velocities decrease. At x / d = 0.11~0.51, the air and particle axial and tangential fluctuation velocities with

α1 =30° are larger than that with

α1 =10°. It shows that with the cone angle increasing, the air and particle velocity gradient between primary air and secondary air becomes larger. The increase of air and particle turbulent transport ability strengthens the air and particle momentums and mass transport between air/particle flows. It can increase coal combustion and flame propagation velocities. So, it is advantageous to the ignition and combustion of pulverized coal.

Figure 26. Profiles of air and particle axial RMS velocities with the cone angle of 10°.

Figure 27. Profiles of air and particle axial RMS velocities with the cone angle of 22.5°.

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Figure 28. Profiles of air and particle axial RMS velocities with the cone angle of 30°.

Figure 29. Profiles of air and particle tangential RMS velocities with the cone angle of 10°.

Figure 30. Profiles of air and particle tangential RMS velocities with the cone angle of 22.5°.

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Figure 31. Profiles of air and particle tangential RMS velocities with the cone angle of 30°.

2. Effect of Division Cone Angles on Particle Size Distribution The particle mean diameter (d10) is the average of diameters of particles. Figure 32 shows profiles of particle mean diameter. The distributions of particle size with different cone angles are similar. Because the primary air is non-swirling, the large particles go downstream under the inertia effect, and the little particles are easy to diffuse into the secondary air and the central recirculation zone by the driving of airflow. Therefore, in the region x / d =0.11~0.51, the particle mean diameter is little in the central recirculation zone and near the wall, but is larger outside the central recirculation zone. With the jet developing, particles gradually mix each other. The distribution of particle mean diameter is to be uniform.

Figure 32. Profiles of particle mean diameter with different division cone angles.

3. Effect of Division Cone Angles on Distribution of Particle Concentration Figures 33 and 34 show profiles of number concentrations for particle size in the range from 0.5 to 100μm, where Cn is the number concentration at a given point and Cmax is the largest number concentration in the same cross section.

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Figure 33. Profiles of particle number concentration with different division cone angles.

Figure 34. Profiles of particle number concentration with the cone angle of 22.5°.

There is a peak value of the particle number concentration at every cross-section at different cone angles. The peak value of particle number concentration with the cone angle of 10° is closer to the central recirculation zone than that with the cone angle of 30°. The particle number concentration with the cone angle of 10° is less than that with the cone angle of 30° near the wall at x / d = 0.11~0.51. This is because that the cone at the angle of 10° delays the radial diffusion of primary air and makes more particles stay at the central zone. The particle number concentration is large in the central zone. With the jet developing, the particle number concentration becomes uniform radially behind x / d = 1.

4. Effect of Division Cone Angles on the Mixing Characteristics of Primary Air and Secondary Air Measurements were performed on a single-phase test facility to investigate the mixing characteristics of primary air and secondary air [21]. Figure 35 shows profiles of the maximum ratio of fuel-rich primary air with different cone angles. When the cone angle is

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners… less, for example

37

α1 =31.0°, the mixing of fuel-rich primary air and other airflows is delayed

and the maximum ratio of fuel-rich primary air is larger in the flow-field because the secondary air is driven outward. When the cone angle increases more, for instance α1 =37.1°, the mixing of primary air and secondary air is weakened in the early stage but strengthened in the latter stage. Compared with other two cone angles, the maximum ratio of the fuel-rich primary air with the cone angle of 37.1° declines more quickly. It shows that the mixing of primary air and secondary air in the latter stage is larger. Behind x / d=1, the mixing of fuelrich primary air is over.

Figure 35. The axial profiles of the maximum ratio of fuel-rich primary air with different cone angles.

5. Conclusion (1) With the increase of division cone angle, the secondary air is driven outward along radial direction; and the central recirculation zone becomes larger. Meanwhile, the positions of the peak value of fuel concentration move outward along radial direction, and the particle concentration reduces near the central recirculation zone. (2) With the increase of the division cone angle, air and particle axial and tangential fluctuation velocities become larger near the burner outlet. (3) With the increase of the division cone angle, the mixing of primary air and secondary air is delayed in the early stage.

2.3.3. Effect of The Length of the Division Cone between the Swirling Secondary Air and the Fuel-Lean Primary Air/Coal Mixing on Characteristics of Isothermal Airflows Near the Burner Region Experiments were carried out on a single-phase test facility to investigate the effect of the increase the length of the division cone between the swirling secondary air and the fuel-lean primary air/coal mixing on isothermal airflow issuing from the burner model [14]. The size

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ratio of the burner model to the utility burner in a 670-tph coal-fired boiler was 1: 3. While the other structural parameters of the burner modal is invariable, the ΔL for experiments are 6.8mm, 13.5mm, 20.3mm and 27mm, namely the ΔL / d are 0.018, 0.036, 0.055 and 0.073. The boundary of the air (particle) jet is defined as the edge at which the air (particle) axial mean velocity is 10% of the air (particle) maximum mean axial velocity .Up to a downstream jet distance of x / d =0.52, jet borders of the air and particle are almost a straight line [22]. In order to show the jet development, the divergent angle of the air (particle) jet is defined as the angle between the air (particle) jet borders at the section from x / d =0.1 to 0.52. Figure 36 shows the effect of the increase of the cone length on the size of central recirculation zone and the divergent angle of jet. The increase of the cone length enhances the outward diffusion of secondary air, and enlarges the size of central recirculation zone and the divergent angle of jet. While ΔL is 27mm, namely ΔL / d is 0.072, the jet issuing from the burner model shows instability: the close jet, which has a stable central recirculation zone, becomes an open jet, which has no central recirculation zone, with a little disturbance. Figures 37 and 38 show the effect of the increase of the cone length on the ratio and the maximum ratio of primary air. With the increase of the cone length, the maximum ratio increases at x / d =0.25-1. It shows that the mixing of primary air and secondary air weakens. At x / d >1.0, the increase of the cone length has a little influence on the mixing of primary air and secondary air. In the region of burner outlet, the large ratio of primary air can make the coal concentration of the primary air/coal mixing large. It is advantageous to the ignition of pulverized coal. And the delay of mixing of primary air and secondary air is advantageous to reducing the fuel NOx formation in the beginning of coal combustion. By consideration of the above results, ΔL should be in the range from 0.018d to 0.055d.

Figure 36. Effect of the increase of the cone length on the size of central recirculation zone and the divergent angle of jet ( α ).

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Figure 37. Effect of the increase of the cone length on the characteristics of mixing of primary air and secondary air.

Figure 38. Effect of the increase of the cone length on the maximum ratio of primary air.

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2.3.4. Effect of the Angle of Non-Swirling Secondary Air Cone on Characteristics of Isothermal Airflows Near the Burner Region The non-swirling secondary air cone is the outmost cone of the burner (see Figure 17). With angles of non-swirling secondary air cone ( α nons ) of 0°, 10° and 30°, experiments were carried out on a single-phase test facility [21]. Figure 39 shows the maximum diameter and maximum normalized air recirculation rate of the central recirculation zone. The maximum diameter increases linearly with the increase of α nons . While α nons changes from 0° to 30°,

dCR 2 / d changes from 0.73 to 0.84, increasing by 15.6%. The maximum normalized air recirculation rate ( qrp ) also increases with the increase of

α nons . While α nons changes from

0° to 30°, qrp changes from 0.36 to 0.54, increasing by 47%. Figure 40 shows the air semivelocity lines of jet with different increases linearly with increase of

α nons . The air semi-velocity divergent angle ( α1 2 )

α nons . While α nons increases from 0° to 30°, the α1 2

increases by 30%. The linear expression was as following: α1 2 = 0.42α nons + 35.69

(17)

The increase of the cone angle enhances the radial guidance to the non-swirling secondary air and reduces the restriction of non-swirling secondary air against the swirling secondary air. It makes the swirling secondary air diffuse more rapidly along the radial direction under its centrifugal effect. The experiments for mixing characteristic of primary air and secondary air show the change of angles of non-swirling secondary air cone just has a large influence on the flow characteristic of secondary air, but has a little influence on the mixing of primary air and secondary air. It is suggested to increase the angle of non-swirling secondary air cone. It is advantageous to the low grade coal combustion with large central recirculation zone.

Figure 39. Effect of the angle of non-swirling secondary air cone on the maximum diameter and maximum normalized recirculation rate.

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2.3.5. Influence of Two Burner Core Geometries on Particle-Laden Flows Near Swirl Burners A three–dimensional particle-dynamics anemometer (PDA) made by Dantec was used in this study. We get the total number concentration (number of particles per unit measurement volume) and particle volume fraction (the percent volume of dispersed phase to carrier phase). The overall uncertainties in measured values of the particle diameter and the particle concentration are typically 4 % and 30 %, depending on optical configuration; the mean velocity, typically 1 %. The measurable range for velocities is –500 m/s to 500 m/s, depending on optical configuration; for particle diameters, 0.5 to 1000 μm, respectively. The experiments were done on a test facility [19]. Titanium dioxide powders were fed via the electromagnetic oscillating feeder into the main air duct which sent the central air, the primary air and the secondary air between the wind box and the blower, to trace the air flow. The particle mass flow rate was 0.12 kg/hr, and particle diameters were smaller than 10 μm. Glass beads were fed via another feeder into the fuel-rich primary air/coal mixing duct. The particle density of the glass beads was 2500 kg/m3. The particle-size distribution obtained by the PDA is shown in Figure 41. The particles between the diameters from 0 to 140 μm amounted to 98.4 %. Therefore, the particle size distribution was almost the same as that of pulverized coal. Particle density varies greatly with respect to coal types, and generally speaking is about 2200 kg/m3 for bituminous coal. The characteristics of glass beads are similar to those of pulverized bituminous coal. The principal idea in the present study is to use the phase information to distinguish between signals from seeding particles and dispersed-phase particles. A necessary condition for allowing seed measurements by the phase-Doppler method are that the seeding particles are spherical and the refractive index must be identical.

Figure 40. Air semi-velocity lines of jet with different angles of non-swirling secondary air cone.

Otherwise, the PDA may catch the particles passing through the measurement volume with low efficiency. Glass beads can meet these requirements. The spherical characteristic of

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the titanium dioxide particles is not the same as that of the glass beads, and the refractive index of the titanium dioxide particles is different from that of the glass beads. Because the smaller particles are lost during the experiment, it is difficult for the PDA to detect more particles, whose diameters are smaller than 10 μm, to obtain the information of the gas-phase. We tried to use the titanium dioxide particles. Compared with the case without the titanium dioxide particles, the PDA caught smaller particles in the case with the titanium dioxide particles. So the titanium dioxide particles were used although they are not quite adequate for phase-Doppler measurements.

particle number %

15

10

5

0 83.79

0

167.58

251.37

335.16

418.95

Particle mean diameter (μm)

Figure 41. Particle size distribution.

Figure 42 shows the burner model with the common core of which diameter is 63 mm. Figure 43 shows the sawtooth shaped core of which diameter is 57 mm and the height of the tooth is 10 mm. The following definition of the particle swirl number Sp and the air swirl number S are used:

Sp = ∫

do / 2

0

S=∫

do / 2

0

⎛1 ⎝2

do / 2

ρ p ω p Qr 2 dr ⎜ d ∫ 0 ⎛1 ⎝2

do / 2

ρωur 2 dr ⎜ d ∫ 0

⎞ ⎠

ρ p u p Qrdr ⎟ ⎞ ⎠

ρu 2 rdr ⎟

(18) (19)

where: do is test section furnace diameter, m; ρp is the glass bead material density, kg/m3; ωp, up are the mean tangential, axial velocities of the particles with the size distribution in the range from 0.5 to 100 μm, m/s; Q is the dispersed particle volume flux with the size distribution in the rang from 0 to 100μm in the measured location, m3/（m2 · s）; and ω, u are the air tangential, axial velocities, m/s. Table 3 shows experimental parameters. During the experiment, the total secondary air mass flow rate and primay air parameters were kept constant. The air mass flow rate was controlled within an accuracy of 5 %. Burners with the sawtooth shaped and common cores have the same primary air exit area.

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Figure 42. The burner model (the dimensions are in mm). 1. central air 2. primay air and glass beads 3. swirling secondary air 4. non-swirling secondary air.

Figure 43. The structure of the sawtooth shaped core.

The particles (titanium dioxide and glass beads), up to 8 μm, were used to measure the airflow velocity and turbulence whilst particles (glass beads) of the diameter in the range 10 to 100 μm were to represent the particle (solid) phase flow. From the view point of modeling, particles whose diameters were between 0.5 and 100 μm were used for particle volume flux and number concentration and volume fraction analysis. Particle volume flux was defined as particle volume crossing a unit area of the measurement volume per unit of time. Air/particle flow characteristics were measured in sections of x/d = 0.1, 0.22, 0.52, 1.02, 2.02, 3.32.

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1. Velocity Figure 44 shows profiles of the gas/particle mean axial velocities. With the sawtooth shaped and common cores in the cross sections from the burner jet to x/d = 1.02, there are two peak values in profiles of air/particle mean axial velocities. The peak zone near the burner center is the primay air and particle mixing flow zone, and another peak zone near the wall is the secondary air flow zone. In the x/d = 0.1 cross section, the peak value near the burner center is larger than that near the wall. With the primay air and particle mixing diffusing into the secondary air, the peak value near the burner center gradually reduces. On the other hand, the peak value near the wall gradually increases. As the jet stream developed, the secondary air diffuses into the wall zone, and the velocity peak value also gradually reduces. And its radial position also gradually moves toward the wall. The profiles of gas/particle mean axial velocities are almost independent of the cone structures. The central recirculation zone and near wall recirculation zone of the burner with the sawtooth shaped core are the about same as those of the burner with the common cone. The profiles of the gas/particle axial fluctuation velocities, the gas/particle mean tangential and fluctuation velocities are almost independent of the cone structures.

Velocity (m/s) -5 0

a

5 10 15

0

5 10 15

0

5

10

0

5

10

0

5

0

5

400 350 300 250 200 150

Radius (mm)

100 50 0 -50 -100 -150 -200 -250 -300

b

-350 -400

x=17.7 mm x=39.4 mm x=92.5 mm x/d=0.1 x/d=0.52 x/d=0.22 particle (the common core) − air

x=181 mm x=358 mm x=588 mm x/d=1.02 x/d=2.02 x/d=3.32 particle (the sawtooth shaped core)

Figure 44. Profiles of air (a) and particle (b) mean axial velocities with different cores.

Figures 45 and 46 show profiles of mean radial velocities and radial fluctuation velocities. Although the primary air/coal mixing was non-swirling, due to the angle of the cones, mean radial velocities were larger in the primay air and secondary air flow zone (the radius r > 28.2 mm) at the section of x/d = 0.1. They were smaller in the wall zone. There was one peak value in the profiles. With the jet development, the mean radial velocities gradually decreased. At the sections of x/d = 0.1 and 0.22, with the sawtooth shaped core the particle mean radial velocity is smaller than the air mean radial velocity in the primay air and

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secondary air main flow zone. That make lots of particle remain in the central zone of the burner.

Velocity (m/s) -10 0 10 20 30 -10 0 10 20 30 400

a

0

5 10 15

0

5

10

0

5

0

5

350 300 250 200 150

Radius (mm)

100 50 0 -50 -100 -150 -200 -250 -300 -350

b

-400

x=39.4 mm x=92.5 mm x=181 mm x=358 mm x=588 mm x/d=0.52 x/d=1.02 x/d=2.02 x/d=3.32 x/d=0.22 particle (the common core) − air particle (the sawtooth shaped core)

x=17.7 mm x/d=0.1

Figure 45. Profiles of air (a) and particle (b) mean radial velocities with different cores.

RMS velocity (m/s) 0 10 20 30 40 102030405060 10 20 30 40 5 10 15 20 25 5 10 15 20 25 5 10 15 20 25 400

a

350 300 250 200 150

Radius (mm)

100 50 0 -50 -100 -150 -200 -250 -300

b

-350 -400

x=17.7 mm x=39.4 mm x=92.5 mm x/d=0.1 x/d=0.52 x/d=0.22 particle (the common core) − air

x=181 mm x=358 mm x=588 mm x/d=1.02 x/d=2.02 x/d=3.32 particle (the sawtooth shaped core)

Figure 46. Profiles of air (a) and particle (b) radial fluctuation velocities with different cores.

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The air mean radial velocity appears positive and negative fluctuation near the zone that radius is about 100 mm. At the sections of x/d ≥ 0.52, the sawtooth shaped core has little influence on the mean radial velocities. With the sawtooth shaped core the air and particle radial fluctuation velocities are clearly larger than those with the common cone in the central recirculation zones and primay air and secondary air main flow zones at the sections of x/d = 0.1-0.52 and the zone that radius is less than 150 mm at the section of x/d = 1.02. In these zones, air/particle flow possesses larger radial turbulence transport capacity.

2. Particle Concentration Figures 47-49 show profiles of particle volume fluxes, normalized particle number concentration and normalized particle volume fraction for particle size in the range from 0.5 to 100 μm with different cores, Cn is the particle number concentration at a given point and Cnmax is the largest particle number concentration in the same cross section, Cv is the particle volume fraction at a given point and Cvmax is the largest particle volume fraction in the same cross fraction. As a lot of particles spray into the test section from gaps of the sawtooth shaped core (see Figure5), in radially measured fields of cross sections of x/d = 0.1-1.02, the peak of the particle volume flux with the sawtooth shaped core is closer to the burner center than that with the common core. From the profiles of normalized particle number concentration and normalized particle volume fraction, we can see that at the sections of x/d = 0.1-1.02, the sawtooth shaped core make the central recirculation zone possess more normalized particle number concentration and normalized particle volume fraction than those with the common core.

-4

3

2

Particle volume flux (10 m /(m s)) -1 0 1 2 3 4 5 6 -2 0 2 4

0 1 2 3 4

150

-0.5 400

0.0

0.5

0.0

0.5

0.0

350 125 300

Radius (mm)

100

250 200

75 150 50

100 50

25 0 0

-50

x=17.7 mm x/d=0.1

x=39.4 mm x=92.5 mm x/d=0.52 x/d=0.22 the common core

x=181 mm x=358 mm x/d=1.02 x/d=2.02 the sawtooth shaped core

Figure 47. Profiles of particle volume fluxes with different cores.

x=588 mm x/d=3.32

0.5

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

47

Cn/Cnmax 0.0 150

0.5

1.0

0.5

1.0

0.5

1.0

0.0 400

0.5

1.0

0.5

1.0

0.5

1.0

350 125 300

Radius (mm)

100

250 200

75 150 50

100 50

25 0 0

-50

x=17.7 mm x/d=0.1

x=39.4 mm x=92.5 mm x/d=0.52 x/d=0.22 the common core

x=181 mm x=358 mm x/d=1.02 x/d=2.02 the sawtooth shaped core

x=588 mm x/d=3.32

Figure 48. Profiles of number concentration with different cores.

Cv/Cvmax 0.0 150

0.5

1.0

0.5

1.0

0.5

1.0

0.0 400

0.5

1.0

0.5

1.0

350 125 300

Radius (mm)

100

250 200

75 150 50

100 50

25 0 0

-50

x=17.7 mm x/d=0.1

x=181 mm x=358 mm x=39.4 mm x=92.5 mm x/d=0.52 x/d=1.02 x/d=3.32 x/d=0.22 the common core the sawtooth shaped core

Figure 49. Profiles of normalized particle volume fraction with different cores.

The peak of the particle volume flux with the sawtooth shaped core is closer to the burner center than that with the common core. The more the position is close to the burner center, the higher the gas temperature becomes. The sawtooth shaped core makes the high pulverized coal concentration well match high temperature. At the same time, there are larger air radial

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fluctuation velocities and radial turbulence transport capacity near the border of the central recirculation zone. These factors make the pulverized coal easily heated, ignited and keep flame stable [12, 24, 25]. There is the larger pulverized coal concentration in the central recirculation zone with the sawtooth shaped cone. The central recirculation zone is a low oxygen concentration and reducing atmosphere zone. This zone can effectively control the NOx formation [50].

3. Particle Diameter Figure 50 shows profiles of particle mean diameters for particle size in the range from 0.5 to 100 μm. The particle mean diameter is the arithmetic mean diameter. The profiles are almost independent of the cone structures.

Particle mean diameter (μm) 20 400

30

40

50

30

40

50 30 40 50

40 50 60

20 30 40 50

30

40

50

350 300

Radius (mm)

250 200 150 100 50 0 -50

x=17.7 mm x/d=0.1

x=39.4 mm x=92.5 mm x/d=0.52 x/d=0.22 the common core

x=181 mm x=358 mm x/d=1.02 x/d=2.02 the sawtooth shaped core

x=588 mm x/d=3.32

Figure 50. Profiles of particle diameters with different cores.

4. Conclusion (1) Profiles of air/particle mean axial velocities, mean tangential velocities, axial fluctuation velocities and tangential fluctuation velocities and the central recirculation zone are almost independent of as the sawtooth shaped and the common cores. (2) The peak of the particle volume flux of the burner with the sawtooth shaped core is closer to the burner center than that with the common core. The sawtooth shaped core make the central recirculation zone of burner possess more normalized particle number concentration and normalized particle volume fraction than the common core.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

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(3) With the sawtooth shaped core the particle mean radial velocities are smaller than the air mean radial velocities and the air mean radial velocities appear clear fluctuation in the primay air and secondary air main flow zone in the near the burner region. In the central recirculation zone, the air fluctuation velocities are clearly larger than those with the common cone. (4) Profiles of particle mean diameters are almost independent of as the sawtooth shaped and the common cores.

2.3.6. Influence of Division Cone Angles between the Fuel-Rich and the Fuel-Lean Ducts on Particle-Laden Flows and Combustion Near Swirl Burners In order to prevent the loss of the enriching effect caused by mixing fuel-rich and fuellean primary air/coal mixings before they are ejected into the furnace, we usually install a division cone between the primary air/coal mixings. A three-dimensional particle-dynamics anemometer (PDA) is used to study gas/particle flow characteristics with division cone angles of 43.2° and 0° [26]. The uncertainties and the particle material used in the experiment are introduced in 2.3.5 Part. No enricher was mounted in the RBC burner model (see Figure 51), and glass beads were fed only into the fuel-rich primary air/coal mixing duct. This simulates the extreme case in which particles in the primary air are all concentrated into the fuel-rich primary air/coal mixing. Except for the division cone structure, the two burner models were the same. In Model A, the division cone angle was 43.2° , the division cone diameter was φ83mm, and the distance from the division cone edge to the central cone edge was 13.9 mm. In Model B, the division cone was removed (equal to division cone angle of 0° ) and the diameter of the division annulus between the fuel-rich and the fuel-lean ducts wasφ71mm. The distance from the division cone edge to the central cone edge was 33mm. Operational parameters for the two models were the same. The primary air velocity was 15.5m/s, and the swirl secondary air axial velocity was 21.4m/s. The non-swirl secondary air velocity and central air velocity were both 0 m/s. The fuel-rich primary air/coal mixing particle mass concentration, which is defined as the ratio of particle mass flow rate to air mass flow rate, was 0.20kg (fuel)/kg (air), and that of the fuel-lean one was 0 kg (fuel)/kg (air). 1. Gas/Particle Flow Characteristics Gas/particle flow characteristics were measured in sections of x/d=0.22, 0.37, 0.52, 1.02, 2.02, 3.32. Figure 52 shows the profiles of air/particle axial mean velocities, and Figure 53 shows the profiles of 0 ~ 100 μ m particle volume fraction. In Figure 54, γ refers to the division cone angle. u=0 refers to the points between the central recirculation zone and the main flow zone at which the air axial mean velocity is 0m/s, and Qmax refers to the peak value of particle volume flux at the point near the burner center. Profiles of gas/particle tangential and radial velocities were similar at the two cone angles. Figure 52 shows that there were two peak values in the profiles of gas/particle axial mean velocities at both division cone angles. The peak zone near the burner center was the primary air/coal mixing flow zone, and another peak zone near the wall was the secondary air flow zone. As the jet stream developed, theprimary air entered the central recirculation zone from the cross section at x/d=0.37 with a division cone angle of 43.2° (see Figure 51a).

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Figure 51. Burner models (dimensions are in mm). a. with division cone angle of 43.2° b. with division cone angle of 0° . 1. central air 2. fuel-richprimary air and glass beads 3. fuel-leanprimary air 4. swirl secondary air 5. non-swirl secondary air.

Figure 52. Profiles of axial mean velocities for gas (a) and particles (b) at different division cone angles. ○ the division cone angle is 43.2° the division cone angle is 0° .

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

51

Figure 53. Particle volume flux profiles at different division cone angles. ○ the division cone angle is 43.2° the division cone angle is 0° .

Figure 54. Axial zero air velocity (u=0) line and Qmax points.

At x/d=1.02, theprimary air was completely incorporated in the central recirculation zone. The diameter of the central recirculation zone increased, and it continued to enlarge with the jet development. In the central zone, between the cross section of x/d=0.52 and that of x/d=1.02, axial mean velocities of the air were negative while those of particles remained positive. Therefore, numerous particles penetrated the central recirculation zone. This pattern of air/particle flow was also found in previous works [27-33]. With a division cone angle of 0° , there was no barrier from the division cone and the axial flow momentum was much greater. Therefore, the fuel-rich primary air/coal mixing mixed earlier with the fuel-lean one and, as a result, theprimary air flow zone near the center maintained a higher positive velocity and the diameter and length of the central recirculation zone stayed small (Figure 52a and Figure 54). It should be noted that, at the position of x/d=1.02 (x=181mm), the central recirculation zone completely disappeared and no particles penetrated the central recirculation zone. Figure 53 shows that profiles of particle volume fluxes were also similar at both division cone angles. Profiles of particle volume flux in cross sections from x/d=0.22 to 1.02 have two peak zones, resulting from burner structures and particle inertia. With the RBC burner, the

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diameter of the outer tube of the fuel-rich primary air/coal mixing duct was φ71mm (see Figure 51). The central cone diameter was φ63mm. When particles ejected into the test chamber from the fuel-rich primary air/coal mixing duct, particles in the outer annulus of the fuel-rich primary air/coal mixing duct were generally not influenced by the central cone and ejected directly into the test chamber. It is these particles that formed the peak zone of the particle volume flux near the jet axis. While particles in the inner zone of the fuel-rich primary air/coal mixing duct ejected into the test chamber at a certain angle to the burner central axis, due either to collision with the central cone or to the guidance of the central cone, these particles formed the other peak of the particle volume flux outside of the peak zone near the burner central axis. With jet development, the peak values were noticeable. Figure 54 shows that with a division cone angle of 43.2° , the fuel-rich and the fuel-lean primary air/coal mixings did not mix with each other before entering the test chamber through their own ducts. The position of the particle volume flux peak near the central axis is closer to the axis than that of the cone angle of 0° , and downstream from the cross section x/d=0.37(x=66mm) the particle volume flux peak zone is inside the central recirculation zone. In contrast, with a division cone angle of 0° , the fuel-rich and the fuel-lean primary air/coal mixings mix before entering the test chamber through their own ducts. Since particles in the fuel-rich primary air/coal mixing are easy to diffuse into the fuel-lean primary air/coal mixing, the position of the particle volume flux peak near the central axis is farther away from the central axis than that of the cone angle of 43.2° , and it is always in the outer part of the central recirculation zone.

2. Influence of Division Cone Angles on Combustion In boilers with swirl burners installed on walls, each swirl burner forms its own flame independently and generally does not influence the others. Using the central recirculation zone as the heat source, coal particles ignite on time and form stable flames. The size of the central recirculation zone has an important effect on ignition and combustion. With a division cone angle of 43.2° , the central recirculation zone is bigger and is expected to provide enough heat for PF ignition. The position of the particle volume flux peak near the central axis is closer to the central axis. Because the gas temperature is higher in the central recirculation zone, a zone of high temperature and high fuel concentration is formed. With the increase of PF concentration near the high temperature central recirculation zone, emissivity of the PF stream increases. Then radiative heat, absorbed from the central recirculation zone and the flame in the furnace, increases [34]. As a result, the temperature of the PF and air increases faster than usual. An experiment done by Horton et al (1977) [35] showed that increasing the PF concentration in a certain range could also increase the flame velocity. Downstream from the cross section at x/d=0.37 (x=66mm), the recirculation zone appears around the primary air flow zone, and the particle volume flux peak zone near the central axis gradually becomes incorporated in the central recirculation zone (see Figures 52 and 54). Therefore, a proper angle of division cone is advantageous for heating and ignition of PF particles. Coal quality frequently changes in China’s power plants, and boilers are prone to flame extinction even with a rated load when burning low-grade coals. The appropriate division cone angle will curtail flame extinction and insure stable boiler operation. PF particles igniting at the proper position also provide good conditions for burnout. The early stage of coal combustion takes place in a dynamic combustion zone. When the temperature

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53

rises, combustion speed increases. These conditions are advantageous to the burnout of coal particles. With a division cone angle of 0° , the size of the central recirculation zone is too small to provide enough heat for PF ignition. The position of the particle volume flux peak near the axis is farther away from the central axis than that with the division cone angle of 43.2° . Furthermore, it is always in the outer region of the central recirculation zone where the temperature is lower. No zone of high temperature and high fuel concentration is formed. That is disadvantageous to flame stabilization and burnout, especially in the case of low-grade coals.

Figure 55. Zero velocity line ( ) of the burner central recirculation zone and the stream border ( ). a. with PA, without secondary air b. With bothprimary air and secondary air.

Many factors influence NOx formation, such as coal nitrogen, coal type, particle diameter, swirl number, stoichiometric ratio, primary air ratio, temperature and residence time. There are many measures to abate NOx emission. The low NOx burner is an inexpensive one. By ensuring the residence time of the coal particles in the fuel-rich zone or the reducing atmosphere zone, the burner decreases fuel NOx formation. When division cone angle is 43.2° , the central recirculation zone is large. Downstream from the cross section at x/d=0.37 (x=66mm), the particle volume flux peak zone near the axis is inside the central recirculation zone, which is a low oxygen zone where the atmosphere is reducing and can decrease the formation of the fuel NOx. When the division cone angle is 0° , the particle volume flux peak zone near the axis is farther away from the central recirculation zone. In this area, the atmosphere is oxidizing, the temperature is low, and ignition takes place further from the burner nozzles. The stronger the oxidizing atmosphere, the more advantageous it is to the formation of fuel NOx; the farther away from the burner nozzles that ignition takes place, the moresecondary air that mixes in [28-31, 33, 36-48].

3. In-Situ Industrial Experiments The 670t/h boiler of ΕП 670–13.8–545KT type was made in the former Soviet Union. It is fired with PF and synchronized to a 200MWe steam generator. It is a dry-ash type furnace with a division wall. The boiler burns lean coal (defined in China as a coal with 10-20% volatile, dry-ash free matter) with low volatility [Table 6].

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Zhengqi Li Table 5. Experimental parameters Central air

Swirling secondary air

primary air

Non-swirling secondary air

Exit area (×10-3 m2)

2.98

6.69

11.41

3.35

Mass flow rate of particle (kg/s)

0.0

0.0124

0.0

0.0

Mass flow rate of air (×10-2 kg/s)

0.0

12.08

36.91

0.0

Particle loading

0.0

0.1

0.0

0.0

Air temperature

16 ℃

Particle swirl number

0.92

Air swirl number

0.26

Table 6. Design and burned coal composition Proximate analysis, wt% (as received) Fixed Moisture Ash Volatility carbon

Net heating value (kJ/kg)

Design coal

4.38

34.36

17.0

43.81

19661

Coal fired during test

5.60

27.46

13.20

53.74

22600

Ultimate analysis, wt% (as received) Carbon Hydrogen Nitrogen Oxygen Design coal Coal fired during test

54.44 58.07

2.03 2.74

0.77 0.97

2.38 4.35

Sulfur 1.36 1.01

The boiler is of T type arrangement with dual furnaces, and uses a bin system with cold moisture–laden exhaust air from pulverizers to convey the PF. The furnace is 17.76m in width and 8.8m in depth. Sixteen swirl burners are located on two opposite walls of the furnace, with eight burners on each side arranged vertically in two rows. The coal is low- grade and the quality changes frequently. The primary air/coal mixing temperature is 75℃, and the primary air ratio is 26%. Compared with the system in which PF is conveyed by hot air, the primary air/coal mixing temperature is lower, and theprimary air ratio is higher. Therefore, it is required that burners excel in flame stabilization. According to the above experimental results, burners with division cone angle of 43.2° are adopted.

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55

To verify the effectiveness of the division cone, we made in-situ measurements of the near-burner flow in the cold state. The uncertainty value in central recirculation zone size was 50mm. Figure 55a shows the flow structure without secondary air and with primary air only. The fuel-rich primary air velocity was 10.1m/s, and the fuel-lean primary air velocity was 10.8m/s. Figure 55b shows the flow structure with both secondary air and PA. Test velocities were determined by modeling. The fuel-rich primary air velocity was 10.1m/s, the fuel-lean primary air velocity was 10.8m/s, the swirl secondary air velocity was 12.1m/s, and the nonswirl secondary air velocity was 4.7m/s. db represents the outer diameter of the non-swirl secondary air nozzle, which was φ1246mm. A central recirculation zone was formed with the guidance of the central cone and the division cone even though no swirl secondary air was ejected. The diameter and length of the central recirculation zone, and the divergence angle were 0.48db (φ600mm), 0.56 db(700mm) and 97° respectively [Figure 55a]. With secondary air given, the size of the central recirculation zone became much larger [Figure 55b]. The diameter of the central recirculation zone was between 1.61db (φ2000mm) and 1.77db(φ2200mm), and the length was 2.41db(3000mm). The divergence angle was 98° . Coal analysis is shown in Table 6. With a 200MWe load, the boiler operated stably, with parameters such as steam temperature, steam pressure, etc., within normal range. The PF ignited properly and combustion was successful. The carbon loss was 2.36%, and the boiler efficiency was 90.02%. NOx (O2=6%) was 762 mg/Nm3. The boiler operated stably at 100MWe during the low load test. Boiler steam parameters were in normal range. Furnace pressure fluctuation was ±50Pa, which implies that combustion was stable in the furnace. The flame scanners showed a steady signal rather than an intermittent signal, and the boiler ran well. The low load test lasted four hours. After RBC burners were adopted, flame stability was greatly improved and no flame extinction occurred even with a wide variation in coal quality and boiler load.

4. Conclusion The influence of division cone angles between the fuel-rich and the fuel-lean ducts on gas/particle flow is significant. With an angle of 43.2° , the central recirculation zone is larger. The particle volume flux peak near the central axis is closer to the central axis than with an angle of 0° . Even at a certain distance from the burner, the particle volume flux peak zone is still in the central recirculation zone, and large masses of particles penetrate the central recirculation zone. Evidently, it is easy to form a high temperature and high fuel concentration zone. With a division cone angle of 0° , the central recirculation zone is smaller. The particle volume flux peak near the central axis is farther away from the central axis, and it is always in the outer part of the central recirculation zone. Therefore, it is not easy to form a high temperature and high fuel concentration zone. In-situ experiments on a 200MWe unit show that with a division cone angle of 43.2° , boiler efficiency is 90.02% when fired with lean coal, and NOx emission is 762mg/Nm3(O2=6%). The boiler operates stably with a load of 100MWe without auxiliary fuel oil.

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2.4. Influence of Run Parameters on Gas/Particle Flow 2.4.1. Influence of the Non-Swirling Secondary Air on Gas/Particle Flow and Coal Combustion 1. Gas/Particle Flow Characteristics Burner operational parameters have great effects on flow characteristics and burner properties [48-51]. The objective of our work is to investigate the influence of the nonswirling secondary air on gas/particle flow and coal combustion of radial biased combustion burners. To this end, the pilot and full scale experiments have been carried out. The data has value because of the support it lends to theoretical and numerical calculations [22]. A three–dimensional particle-dynamics anemometer was used in this study. The uncertainties and the particle material used in the experiment are introduced in 2.3.5 Part. The non-swirling secondary air ratio is the ratio of the non-swirling secondary air mass flow rate to the total secondary airflow rate. The following definition of the total air swirl number St is used:

St

∫ =

d0 / 2

0

d∫

( ρωu + ρ pω p Q ) r 2 dr

d0 / 2

0

( ρu 2 + ρ p u p Q )rdr

(20)

Figure 51a shows the radial biased combustion burner model. No enricher was mounted in it, and glass beads were fed only into the fuel-rich primary air/coal mixing duct. This simulates the extreme case in which particles in the primary air are all concentrated into the fuel-rich primary air/coal mixing. Table 7 shows experimental parameters. During the experiment, the total secondary air mass flow rate and primary air parameters were kept constant. The air mass flow rate was controlled within an accuracy of 5 %. Different nonswirling secondary air ratios were obtained by changing the non-swirling and swirling secondary air flow rates. The gas/particle flow characteristics were measured at sections of x/d = 0.1, 0.22, 0.37, 0.52, 1.02, 2.02, 3.32.

(1) The Total Air Swirl Number The total air swirl number, shown in Table 7, was calculated with the measured velocities at the section of x/d = 0.1. The total air swirl number decreased with the increase of nonswirling secondary air ratios. (2) Velocity The definition of the boundary of the air (the particle) jet and the divergent angle of the air (the particle) jet is shown in the 2.3.3 Part. Figure 56 shows jet borders with different nonswirling secondary air ratios, where B refers to the value of the jet boundary and Rns is the non-swirling secondary air ratio. Figure 56 shows that jet boundaries of the air and the particle were almost a straight line in the zone of x/d ≤ 0.52. Table 8 shows divergent angles of the air and the particle with different non-swirling secondary air ratios.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners… Table 7. Experimental parameters with different non-swirling secondary air ratio

Central air

Fuel-rich primary air

Fuel-lean primary air

Swirling secondary air

Nonswirling secondary air

10−3 m2)

2.98

3.19

3.16

11.41

3.35

Mass flow rate of particle (kg/s)

0.0

0.0124

0.0

0.0

0.0

Particle loading

0.0

0.2

0.0

0.0

0.0

Exit area (×

16 ℃

Air temperature Non-swirling secondary air ratio (%)

Mass flow rate of air ( ×

0

0.0

6.04

6.04

29.81

0.0

Total air swirl number 0.3

11.1

0.0

6.27

6.27

27.17

3.39

0.2

22.4

0.0

6.00

6.00

22.43

6.46

0.2

10−2 kg/s)

Table 8. Air and particle divergent angles Non-swirling secondary air ratio Air divergent angle Particle divergent angle

Figure 56. Jet borders.

0.0 %

11.1 %

22.4 %

100.8° 109.0°

45.5° 88.6°

44.3° 68.8°

57

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Figures 57 and 58 show profiles of the decay of the gas/particle axial maximum velocities and the gas/particle mean axial velocities with non-swirling secondary air ratios. There were two peak values in the profiles of gas/particle mean axial velocities near the burner. The peak zone near the burner center was the primary air/coal mixing flow zone, and another peak zone near the wall was the secondary air flow zone. At the section of x/d = 0.1, the peak value near the burner center was larger than the peak value near the wall. However, with the primary air/coal mixing diffusing into the secondary air, the peak value near the burner center gradually reduced. On the other hand, the peak value near the wall gradually increased. With the jet development, the secondary air diffused into the wall zone, the peak value near the wall also gradually reduced and its radial position gradually moved toward the wall. The experimental results show that the gas/particle borders of the jet greatly moved to the burner centerline with the increase of non-swirling secondary air ratios. When the non-swirling secondary air ratios increase from 0 % to 22.4 %, the air divergent angles decreased from 108° to 44.3° and the particle divergent angles decreased from 109° to 68.8° . The central recirculation zone apparently decreased with increasing the non-swirling secondary air ratios. For non-swirling secondary air ratio equaled zero, the central recirculation zone diameters are 25.9~28.5 mm in the zone of x/d = 0.1~0.37, and they become larger with the development of the jet. As the jet developed, the primary air entered the central recirculation zone from the cross section at x/d = 0.37. At x/d = 1.02, the primary air was completely incorporated in the central recirculation zone. In the central zone between the cross section of x/d = 0.52 and that of x/d = 1.02, mean axial velocities of the air were negative, while those of particles remained positive. Therefore, numerous particles penetrated the central recirculation zone. The pattern of air-particle flow was also found in previous works [27-33]. In the case that the non-swirling secondary air ratio was 22.4 %, the central recirculation zone diameters were 14.2~20.0 mm at x/d = 0.1~0.37, and the central recirculation zone was closed at x/d = 0.52. In this case we observed the smallest central recirculation zone. In addition, the larger was the non-swirling secondary air ratio, the slower was the rate of air-particle diffusion towards the furnace wall.

Figure 57. Decays of air-particle maximum axial velocities.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

59

Figure 58. Profiles of (a) the air and (b) the particle axial mean velocities with different secondary air ratio.

Figure 59 shows profiles of axial fluctuation velocities. There were two peak values in the profiles of gas/particle root mean square (RMS) axial fluctuation velocities. The profiles became flat in the downstream region. Figure 60 shows profiles of mean radial velocities. Although the primary air/coal mixing was non-swirling, due to the angle of the cones, mean radial velocities were larger in the primary air and secondary air flow zone (the radius r > 28.2 mm) at the section of x/d = 0.1. They were smaller in the wall zone. There was one peak value in the profiles. With the jet development, the mean radial velocities gradually decreased. With the increase of the nonswirling secondary air ratio, the particle mean radial velocity and maximum mean radial velocity (Figure 60 (b)) decreased in the main flow field. In the same section, the position of particle maximum radial velocity in the case of the 0 % non-swirling secondary air ratio was closer to the wall than that in the case of 11.1 % and 22.4 % non-swirling secondary air ratios. Therefore, particles diffused faster into the wall zone when the non-swirling secondary air ratio was low. Figure 61 shows profiles of radial fluctuation velocities. At the sections of x/d = 0.1 to 1.02, the radial fluctuation velocities were larger in the central recirculation zone and the primary air and the secondary air main flow zones and smaller in the wall zone. At the sections of x/d = 2.02、3.32, the profiles of radial fluctuation velocities became flat as the profiles of the mean radial velocities became flat.

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Figure 59. Profiles of (a) the air and (b) the particle axial fluctuation velocities with different secondary air ratio.

Figure 60. Profiles of (a) the air and (b) the particle radial mean velocities with different secondary air ratio.

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Figure 61. Profiles of (a) the air and (b) the particle radial fluctuation velocities with different secondary air ratio.

Figure 62. Profiles of (a) the air and (b) the particle tangential mean velocities with different secondary air ratio.

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Figure 62 shows profiles of mean tangential velocities. Because there was no central air and the primary air was non-swirling, the mean tangential velocities were smaller in the burner central zone (r ≤ 55.5 mm) and larger in the secondary air at the section of x/d = 0.1. The profiles became flat in the down stream region. Without the non-swirling secondary air, the maximum mean tangential velocities were the largest among the three non-swirling secondary air ratios. The profiles were almost independent of the non-swirling secondary air ratios. Figure 63 shows profiles of tangential fluctuation velocities. At the sections of x/d = 0.1 to 0.52, there were two peak values in the central recirculation zone and the secondary air flow zone. At the section of x/d = 1.02 to 3.32, the profiles of the tangential fluctuation velocities became flat as the profiles of the mean tangential velocities became flat.

Figure 63. Profiles of (a) the air and (b) the particle tangential fluctuation velocities with different secondary air ratio.

(3) Particle Concentration Figure 64 shows profiles of particle volume fluxes for particle size in the range from 0.5 to 100 μ m at different non-swirling secondary air ratios. The profiles of particle volume fluxes show that they had two peaks in the sections of x/d = 0.1 to 1.02, resulting from burner structures and particle inertia. The diameter of the outer tube of the fuel-rich primary air/coal mixing duct was 71mm (see Figure 51a). The central cone diameter was 63mm. When particles ejected into the test chamber from the fuel-rich primary air/coal mixing duct, particles in the outer annulus of the fuel-rich primary air/coal mixing duct were generally not influenced by the central cone and ejected directly into the test chamber.

Radial-Bias-Combustion and Central-Fuel-Rich Swirl Pulverized Coal Burners…

63

Figure 64. Profiles of particle volume fluxes with different secondary air ratio.

It is these particles that formed the peak zone of the particle volume flux near the jet axis. While particles in the inner zone of the fuel-rich primary air/coal mixing duct ejected into the test chamber at a certain angle to the burner central axis, due either to collision with the central cone or to the guidance of the central cone, these particles formed the other peak of the particle volume flux outside of the peak zone near the burner central axis. At the sections of x/d = 2.02 and 3.32, the profiles became flat. The profiles of particle volume fluxes were almost independent of the non-swirling secondary air ratios. The maximum particle volume flux near the burner center, increased with increasing of the non-swirling secondary air ratio. Therefore, the concentration of particles in the burner central zone increased with the nonswirling secondary air ratio. Figure 65 shows profiles of number concentrations for particle size in the range from 0.5 to 100 μ m, where Cn is the number concentration at a given point and Cnmax is the largest number concentration in the same cross section. At the sections of x/d = 0.1 to 1.02, there were also two peak values in the profiles. However, at the sections of x/d = 2.02 and 3.32, the profiles became flat. The profiles of number concentrations were similar to that of particle volume fluxes. The profiles were almost independent of the non-swirling secondary air ratios.

(4) Particle Diameter Figure 16 shows profiles of particle mean diameters for particle size in the range from 0.5 to 100 μ m. The particle mean diameter is the arithmetic mean diameter. Profiles of the

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particle mean diameter had two peaks in the sections from x/d = 0.1 to 1.02, whose positions were same as the position of particle volume flux peaks.

Figure 65. Profiles of number concentrations with different secondary air ratio.

Particle in the fuel-rich primary air/coal mixing duct were separated into two particle jets, when ejected into the test chamber. The larger the particle was, the larger inertia it had. As the primary air/coal mixing was non-swirling, particles had the characteristic of keeping the nonswirling jet. The finer particles of two particle jets were liable to diffuse to the secondary air or the central recirculation zone. On the other side, the larger particles were liable to flow in their former flow direction due to their larger inertias. Then, two particle mean diameter peaks were formed. With the jet development, the larger particles were also transported into the wall zone or the central recirculation zone, and the profiles became flats. In the wall zone, particles near the burner jet came from the wall zone of the down stream through the outside recirculation. Particle diameters were larger in the wall zone of the down stream. As a result, the particle mean diameters were large in the wall zone. The profiles were almost independent of the non-swirling secondary air ratios.

2. Mixing Characteristics of Jet The mixing characteristics of jet from the burner were measured on a single-phase test facility [14]. Figures 67 and 68 show the ratio of primary air and the maximum ratio of primary air of every section with different non-swirling secondary air ratio. With the increase of non-swirling secondary air ratio, the ratio of primary air in the burner central zone of every section increase and the maximum ratio of primary air decreases more slowly.

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Figure 66. Profiles of particle mean diameters with different secondary air ratio.

Figure 67. Ratio of primary air with different secondary ratio.

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It shows that the mixing of primary air, gas of central recirculation zone and secondary air is greatly delayed. It is disadvantageous to the heating of primary air/coal mixing, coal ignition and flame stability. But it is advantageous to the air-staged combustion and reducing the fuel NOx formation.

Figure 68. Maximum ratio of primary air with different secondary air ratio.

3. Industrial Experiment (1) Experimental Boiler Experiments were carried out on the 410-tph and 220-tph boilers of a power plant. Both boilers were retrofitted from oil to coal - firing. Eight burners are located on the front boiler wall, and they are grouped in two rows. A common big wind box is used for the eight burners. The wind box is divided into the top and the bottom wind boxes by the division plate located between the top and the bottom burners. There are dampers at wind box entrances to balance the flow entering the top and the bottom wind boxes. There are also dampers at the burner swirling and non-swirling secondary air entrances. Figure 69 shows the 410-tph boiler furnace, platen superheater and burner index. The membrane water-cooled wall, whose tube diameter and pitch were 60 mm and 80 mm, was used. The tube diameter of the platen superheater was 42 mm. The transverse pitch and the number of transverse rows were 720 mm and 16. The longitudinal pitch and the number of longitudinal rows were 48 mm and 42. The working substance temperature was 321 ℃ in the water-cooled wall. The working substance temperature in the platen superheater was 352 ℃ at the entrance and 437 ℃ at the exit. Figure 70 shows the full industrial-size burner. The axially vanes were set at angles of 65º with the airflow direction. Table 9 shows the design parameters of the burner.

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Figure 69. The furnace, burner index, and swirling secondary air direction on the 410-tph boiler (dimensions are in mm).

Figure 70. The full industrial-size burner on a 410-tph boiler (dimensions are in mm) (1)central air (2) fuel-rich primary air/coal mixture (3) fuel-lean primary air/coal mixture (4) swirling secondary air (5) non-swirling secondary air.

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Table 9. Design parameters of the burner on the 410-tph boiler

Centr al air

Fuel-rich primary air

Fuel-lean primary air

Swirling secondary air

Nonswirling secondary air

102.3

83.4

83.4

412.7

98.8

Mass flow rate of coal (kg/s)

1.376

0.459

Coal loading

0.74

0.25

90

90

320

320

Exit area (×

10−3 m2 )

Air temperature (℃)

320

Non-swirling secondary air ratio (%)

Swirl number

Mass flow rate of air (kg/s)

0

0.0

1.865

1.865

11.030

0.0

0.48

20.0

0.0

1.865

1.865

8.824

2.206

0.36

Table 10. Design parameters of the burner on the 220-tph boiler

Centr al air

Fuel-rich primary air

Fuel-lean primary air

Swirling secondary air

Nonswirling secondary air

86.0

45.2

45.2

203.3

69.2

Mass flow rate of coal (kg/s)

0.739

0.246

Coal loading

0.67

0.22

95

95

300

300

Exit area (×

10−3 m2 )

Air temperature (℃)

300

Non-swirling secondary air ratio (%)

Swirl number

Mass flow rate of air (kg/s)

0

0.0

1.106

1.106

6.074

0.0

0.39

20.0

0.0

1.106

1.106

4.859

1.215

0.31

Figure 71 shows the 220-tph boiler furnace, platen superheater and burner index. The membrane water-cooled wall, whose tube diameter and pitch were 60 mm and 80 mm, was used. The outside two tubes’ diameter of the platen superheater was 42 mm, and the other tubes’ diameter, 38 mm. The mean transverse pitch and the number of transverse rows were 766 mm and 12. The mean longitudinal pitch and the number of longitudinal rows were 49.3 mm and 38. The working substance temperature was 317 ℃ in the water-cooled wall. The

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working substance temperature in the platen superheater was 375 ℃ at the entrance and 445 ℃ at the exit. Figure 72 shows the full industrial-size burner. The axially vanes were set at angles of 65º with the airflow direction. Table 10 shows the design parameters of the burner.

Figure 71. The furnace, burner index, and swirling secondary air direction on the 220-tph boiler (dimensions are in mm).

Figure 72. The full industrial-size burner on a 220-tph boiler (dimensions are in mm) (1)central air (2) fuel-rich primary air/coal mixture (3) fuel-lean primary air/coal mixture (4) swirling secondary air (5) non-swirling secondary air.

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(2) Cold Flow Experimental Results Cold flow experiments were carried out on the 410-tph boiler. The similarity criteria are as follows: (1) self-modeling flows; (2) momentum ratios of the primary air coal-mixing to the secondary air maintained constant. The mass fluxes of different burners in the same wind box were kept equal. Burner swirling secondary air dampers were open, the non-swirling secondary air damper positions of the bottom burners (No. 1~4) were set to 0 %, and the nonswirling secondary air damper positions of the top burners (No. 5~8) were set to 100 %. Table 11 shows the experimental parameters. Figure 73 shows the measured flow field, where d0 is the burner diameter. The distance between two measurement traverses was 100 mm. We estimate that the uncertainties in establishing the location of the central recirculation zone border were 100 mm (0.10 d0). With the increase of the non-swirling secondary air ratio, the divergent angles decreased from 101° to 76° , and the central recirculation zone diameters decreased from 1.53 d0 to 1.21 d0. Table 11. Cold flow experimental parameters on the 410-tph boiler Non-swirling Secondary air damper (% open)

Non– swirling secondary air ratio (%)

Burner number

0 100

3.7 20.3

1~4 5~8

Centra l air

Fuel - rich primary air

Fuellean prima ry air

Mass flow rate of air (kg/s) 0.095 1.850 1.850 0.095 1.723 1.723 Air temperature (℃) 30 50 50

Figure 73. The jet border and the CRZ boundary on the 410-tph boiler.

Swirling secondary air

Nonswirling secondar y air

Swirl number

10.529 9.183

0.403 2.337

0.45 0.37

30

30

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(3) Reacting Flow Experimental Results In the reacting flow experiment, the boiler operational parameters such as the load, the furnace exit O2, the coal feed rate, the central air and the primary air flow rates were kept constant. Only damper positions of the swirling and the non-swirling secondary air jets were changed. Table 12 shows the properties of the coal-fired.

Table 12. Burned coal composition Proximate analysis, Wt % (as received) Moisture Ash 4.9 34.26 Ultimate analysis, Wt % (as received) Carbon Hydrogen 52.22 3.08 R200 PF fineness, % R90 46.09 3.0

Volatility

Fixed carbon

36.09

24.75

Net heating value (kJ/kg) 19720

Oxgen 4.14

Nitrogen 0.77

Sulfur 0.63

A thermocouple inside a water-cooled probe was used to measure the gas temperature in the burner zone. Gas temperatures of the No. 2 burner were measured along the burner axis in the 410-tph boiler. Figure 74 shows the results, where curves a and b corresponded to different positioning of the swirling and the non-swirling secondary air dampers respectively. In the 220-tph boiler, gas temperatures of the No. 4 burner were measured radially at x/d1 = 2.4 (x = 1920mm), where d1 = 798 mm was the burner diameter. Figure 75 gives the results. Figures 74 and 75 shows that in both boilers gas temperatures in the burner zone decreased with increasing the non-swirling secondary air ratio.

Figure 74. Temperature profiles on the burner axis.

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Figure 75. Radial temperature profiles in the section that is 2.4d (1920mm) away from the exit of burner.

In the 220-tph boiler, the swirling secondary air dampers of all the burners were set to 100 %, and the non-swirling secondary air dampers of all the burners were set in sequence to at 0 %, 50 % and 100 %. Along the axis of No. 2 burner axis, gas sample were taken using a water-cooled probe and subsequently analyzed. Measurement ranges for NO, NO2, CO and O2 of the instrument are 0-2000 ppm, 0-200 ppm, 0-4000 ppm and 0-21 %. The volume concentration of NOx is the sum of volume concentrations of NO and NO2. Resolutions of NO, NO2, CO and O2 are 1 ppm, 1 ppm, 1 ppm and 0.1 %. Figure 76 shows the measured O2, NOx and CO concentrations when the non-swirling secondary air was set to 0. The measures were taken inside the central recirculation zone. The O2 concentration was almost 21 % at the burner exit, since the flame had not been ignited and the cold air jet was given from the burner central tube. Further down zone the coal jet was heated and volatiles were given off. In the zone of x/d1 = 0.2~0.5, a large quantity of volatiles were given off and they burned rapidly. Consequently, oxygen was rapidly consumed. The volatile-N, given off during devolatilization, was oxidized to a greater, extended to NOx. The NOx formation rate rapidly increased, and it stayed relatively constant when x/d1 ≥ 0.4 . When the swirling jet mixed rapidly with the coal jet, the volatiles had enough oxygen for rapid combustion. Therefore, the CO concentration was at a low level. Down jet, at the central recirculation zone closure, the O2 concentration graduately increased due to the diffusion of O2 from the secondary air jet. It assured a good char burnout. Figures 77, 78 and 79 show O2, CO and NOx profiles at the burner axis for different positions of the non-swirling secondary air damper. Pickett et al. (1999) found that the velocity profiles for reacting flow showed similar trends and patterns as were observed in cold flow experiments [48]. Our cold flow experiments show that when the non-swirling secondary air ratio was increased the mixing of the fuel jet and the primary air jet was decayed.. Thus, as the non-swirling secondary air dampers were graduately opened, the

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oxygen flowing into the jet center decreased, and the O2 concentration on the burner axis decreased (see Figure 77).

Figure 76. Profiles of in situ NOx, CO, and O2 concentrations on the burner axis.

Figure 77. Profiles of in situ O2 concentration on the burner axis.

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Figure 78. Profiles of in situ CO concentration on the burner axis.

Figure 79. Profiles of in situ NOx concentration on the burner axis.

Our gas/particle flow experiments show that fuel fluxes in burner central zone increased with the non-swirling secondary air ratio. In this way, the fuel-rich combustion was promoted, and the NOx reducing environment was created. The specific air-particle flow pattern inhibited the formation of the fuel-NOx. Our observations are in agreement with the previous experiments [2, 4, 31, 36-47, 52]. The NOx formation in the burner zone apparently decreased when the non-swirling secondary air ratios increased (see Figure 79).

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Table 13 shows the influence of the position of non-swirling secondary air dampers on NOx emission and char burnout of the 220-tph boiler. The economizer exit oxygen shows that the increase of non-swirling secondary air had a little effect on the boiler exit oxygen, because opening of these dampers had little effect on the total amount of air supplied to the boiler. NOx emissions (O2 = 6 %) decreased from 359 ppm to 331 ppm, with the average exit CO concentration at 18.7 ppm. Thus, the gaseous incomplete combustion loss is negligible. The carbon-in-ash decreased substantially from 2.98 % to 2.03 %. The swirl number influences the amount of oxygen and particles and the particles residence time in the burner central zone, which have effect on the particle burnout in the burner region. Experiments with a single annular orifice (SAO) burner and a single central orifice (SCO) burner in a large scale laboratory combustor [33, 53] emerges that the quality of the burnout in the burner region, are primarily influenced by the particles residence time in the burner inner recirculation zone. The maximum particle burnout for both burners occur at a certain swirl number.

Table 13. The boiler NOx emission and the carbon in ash content on the 220-tph boiler Non-swirling secondary air damper, % open Economizer exit oxygen, % NOx (O2 = 6 %), ppm Carbon in ash content, %

0 3.5 359 2.98

50 3.5 337 1.96

100 4.0 331 2.03

Above and below this swirl number, the particle burnout starts to decrease, respectively. Increasing the swirl number creates a more compact and intense central recirculation zone within which combustion rates, excepting very high swirl values, are generally intensified. At very high swirl values, the particles will be “centrifuged out” of the central recirculation zone resulting in shorter residence times and a fall-out in burnout. With increase of the nonswirling secondary air ratios, the fuel fluxes increased in the burner central zone, where the gas temperature is higher than the gas temperature in the outside, resulting in a fall-out of carbon-in-ash.

4. Conclusion The radial bias swirl-stabilized burner for combustion of pulverized coal has been investigated by conducting both extensive measurement of gas and solid phase velocities, particle concentrations, particle diameters, and subsequent trials in industrial boiler. The following conclusions are drawn: (1) Up to a down jet distance of x/d = 0.52 (d - burner diameter), the gas-phase flow pattern coincides with the solid-phase flow. Jet borders of the air and particle are almost a straight line. Further down jet, the gas jet and the solid phase jet diverge. (2) Low NOx combustion is achieved by injecting coal particles into the swirl-induced central recirculation zone that is a well-known method. However, a substantial improvement to char burnout is achieved by dividing the secondary combustion air jet into a non-swirling and a swirling part and the primary air-coal mixing into a fuelrich and a fuel-lean primary air-coal mixings. The non-swirling jet directed the particles towards the down jet part of the central recirculation zone. When 20 % of

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2.4.2. Influence of the Central Air on Coal Combustion 1. Experimental Boiler The 670t/h boiler of ЕП 670-13.8-545KT type in a power plant was made in former soviet Union. It is fired with pulverized coal and synchronized to a 200 MWe steam generator. It is of dry-ash type furnace with a division wall. The boiler is of T type arrangement with dual furnaces, and it adopts bin systems with cold moisture-laden exhaust air from pulverizers conveying PF. The furnace is 17.76m in width and 8.8m in depth. Sixteen swirl burners are located on two opposed sidewalls of the furnace, with eight burners on each sidewall vertically in two rows. Volute burners were originally used. Eight burners of the bottom row were retrofitted with RBC burners before the industrial test [19]. 2. Cold Flow Experiment In cold flow experiment, co-axial airflow pattern was maintained to be in the second selfmodeling zone. Compared with reacting flow, momentum ratios were maintained same. In addition, the SA and the PA flowrates of each burner were kept to be equal, and dampers of the non-swirl SA were 100% open. Little tufts were used to trace flow near the burner. The uncertainty is 100mm (0.01db). Here, db represents outer diameter of the non-swirl SA nozzle of the burner, which is φ1080mm. Figure 80 shows the flow pattern, where Rc is the central air ratio.

Figure 80. Reverse-flow and jet boundaries of a full-scale burner.

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As ratio of the central air is 1.2%, the velocity of the central air is so little that the boundary of the reverse flow maintained unchanged. As ratio of the central air is 6.2%, the part of the reverse flow zone at the burner exit in the central zone of the burner is blown away. Instead, a zone, where the axial velocity is positive, is formed like a taper cone. Little tufts also measured the jet boundary, where axial velocity is zero, and the divergent angle of the jet was got. As ratios of the central air are 1.2% and 6.2%, divergent angles of the jet are 90° and 89° . It has a little change.

3. Reacting Flow Experiment At rated load, in-situ species and temperature were measured on axis. They are shown in Figures 81 and 82. A naked thermoelectric couple protected by a water-cooled gun was used to measure the gas temperature. In-situ species were sucked out by the water-cooled gun and continuously measured by an analyzer. Resolutions of O2 and CO are 0.1% and 1ppm. CO2 is calculated continuously according to the measured O2 concentration. The coal fired is shown in Table 14. As ratios of the central air increase from 0.8% to 4.9%, velocities of the central air increase from 4.0m/s to 24.5m/s, and gas temperature on axis decreases about 263℃. Especially as ratio of the central air is 4.9%, the gas temperature was only 927℃ at x/db=0.46(x=500mm). As ratio of the central air is0.8%, with increase of distance, O2 decreases quickly, and becomes to be about 4.5%. Meanwhile, CO2 and O2 increase instantly, and CO2 becomes to be about 14.5%. This makes the zone of the reverse flow become the zone of reducing atmosphere. On the contrast, as ratio of the central air is 4.9%, O2 increases quickly, and CO and CO2 maintain relatively small. At x=800mm(x/db=0.74), O2 decreases to be 18.9%. This makes the zone of the central air become the zone of oxidizing atmosphere.

Figure 81. In-situ gas temperature along axis of burner.

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Figure 82. In-situ species along axis of burner.

Table 14. Fired coal composition on the 670-tph boiler Proximate analysis, Wt % (as received) Moisture

Ash

9.20 33.68 Ultimate analysis, Wt % (as received) Carbon Hydrogen 45.88 3.23

Volatile matter 23.20

Fixed carbon 33.92

Net heating value (KJ/kg) 18343

Oxygen 6.38

Nitrogen 0.92

Sulfur 0.71

At 160MWe load, the furnace exit O2 kept to be constant. Then, NOx emission of boiler was measured with damper of the central air at different positions. Each case lasted about four hours. The coal fired is shown in Table 14. MSI compact analyzer made in German was used. Resolutions of NO and NO2 are 1ppm. The volume concentration of NOx is the sum of volume concentrations of NO and NO2. As position of the damper of the central air were changed from 0% to 100% open, NOx emission of boiler (Via O2=6%) increased from 216ppm to 226ppm. The increased value was only 10ppm. Effect of the central air on NOx formation can be explained as follows. With the increase of ratio of the central air, recirculated-mass flowrate decreases and gas temperature of the zone of the reverse flow decreases. It is advantageous to reduce the formation of thermal NOx. On the other side, with the increase of ratio of the central air, O2 increases and CO decreases. Thus, the oxidizing atmosphere is enhanced around the PF combustion zone. This is advantageous to the formation of fuel NOx. Therefore, the central air has a relatively small effect on NOx formation.

3. Conclusion (1) The central air will change the type of flow pattern. When the ratio of the central air is small, the part of the reverse flow zone at the burner exit in the central zone of the

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burner is blown away. A zone, where the axial velocity is positive, is formed like a taper cone. (2) As ratio of the central air increase, gas temperature greatly decreases and the oxidizing atmosphere is enhanced in the central zone of burner. NOx emissions of boiler increase slightly.

2.4.3. Influence of Air Supply on Nox Formation and Coal Burnout 1. Effect of the Burner Primary Air Ratio on Flow And Mixing Characteristics of Jet Experiments were carried out on a single-phase test facility to investigate the effect of the burner primary air ratio on flow and mixing of jet. The size ratio of the burner model to utility burner in a 670-tph coal-fired boiler is 1: 3. During the experiments, the fuel-rich primary air flux, total air flux, ratio of swirling secondary air to non-swirling secondary air (80%:20%) are constant. The burner primary air ratio is the ratio of the burner primary air flux to the burner total air flux. It was changed by changing the proportion between primary air and secondary air in the experiments. The primary air was heated to 60℃ and the other airflows were at ambient temperature. The profiles of air temperature near the burner outlet were measured. The maximum ratio of primary air shows the mixing of primary air and secondary air. With the increase of the burner primary air ratio, the primary air velocity and the air axial momentum of jet increase, but the total swirl number decreases because the burner primary air of the radial biased combustion burner is non-swirling. Figures 83, 84 and 85 show experiment results, where r1 is the burner primary air ratio. With the increase of the burner primary air ratio, the secondary air ratio declines, the divergent angle of jet, the length and diameter of central recirculation zone decreases sharply. Those phenomenons also appear in the air cold experiment of the utility burner. Otherwise, with the increase of the burner primary air ratio, the ratio of primary air and the maximum ratio of primary air at the measured points increase. It shows that the mixing of primary air and secondary air decreases.

Figure 83. Effect of the burner primary air ratio on central recirculation zone and divergent angles of jet.

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Figure 84. Effect of the burner primary air ratio on characteristics of mixing of primary air and secondary air.

Figure 85. Effect of the burner primary air ratio on the maximum ratio of primary air.

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2. Effect of Stoichiometric Ratio on the Nox Formation and Coal Burnout (1) Experimental Boiler Experiments were carried out on a 220-tph boiler of a power plant (see 2.4.1 Section). In cold air experiments, the results of measured velocities at the burner outlet show that the secondary air fluxes of the burners from the same air box are uniform. The stoichiometric ratio of the burner is the ratio of air mass supplied by burner to the theoretical air mass which is used for coal to burn out completely. It can be calculated according to the excess air coefficient at the furnace outlet, the secondary air mass fluxes of up and bottom air boxes and the primary air mass flux, which were measured in the experiments. There is no central air during the experiments. While the primary air mass flux is constant, the secondary air supply to up and bottom burners is changed by adjusting the opening of dampers in the air boxes. The total air mass flux and stoichiometric ratio of a burner are changed respectively. In the reacting low experiment, the boiler operational parameters such as the load, the furnace exit O2, and the coal feed rate were kept constant. The boiler load was 195.5tph. The secondary air dampers of burners are 0% open. Table 15 shows the characteristics of the coal used in the experiments. Table 15. Burned coal composition on the 220-tph boiler Proximate analysis, Wt % (as received) Moisture

Ash

3.20 36.38 Ultimate analysis, Wt % (as received) Carbon Hydrogen 52.55 3.16

Volatile matter 20.43

Fixed carbon 39.99

Net heating value (KJ/kg) 19881

Oxygen 3.62

Nitrogen 0.60

Sulfur 0.50

Notes: R90=39.12%，R200=5.35%.

(2) Effect of Stoichiometric Ratio on the Nox Formation of the Burner Near the burner region, gases were sampled by a water-cooled gun. Figures 86-89 show profiles of gas components on the central axis of the burner [19, 54], where db is the diameter, Ф798mm, and SR is the stoichiometric ratio of the burner. Cold air test in the boiler show that the central recirculation zone of jet with the length of 1.29 db and diameter of 0.65 db, begins at the central cone of the burner. In the experiments, components of gases were measured in the central recirculation zone. Figure 86 shows that with the jet developing, the Nox concentration on the central axis of the burner increases in the measured zone and rises sharply ahead of x=0.2 db where the fuel Nox is mainly formed. With SR=1.02, the NOx mean concentration is the largest and larger at the burner outlet. As SR is more or less than 1, the more or the less the SR is, the more the NOx mean concentration decreases. With SR1, the supplied secondary air is larger and the primary air ratio is less. The divergent angle of jet, the diameter and length of central recirculation zone increase. It is advantageous to the coal ignition in time. Figure 89 shows with the increase of SR, the distance from the position of the minimum O2 concentration to the burner decrease. Therefore, the position of coal ignition is closer to the burner outlet. It makes the temperature of the fuel-rich primary air/coal mixture rise fast. The rapid increase of coal temperature makes the volatile release fast. Then, the large volatile concentration is formed. Because the fuel-rich primary air/coal mixture is non-swirling and the particle inertia is large, the quantity of pulverized coal entering the central recirculation zone increases with the increase of central recirculation zone diameter. The combustion of those pulverized coal reduces the O2 concentration in the central recirculation zone and increases the CO concentration (see Figs. 87 and 89). It is advantageous to reducing the fuel-volatile NOx [28]. Furthermore, the rapid ignition of pulverized coal and the larger central recirculation zone increase the time of pulverized coal in the reducing atmosphere. It reduces the fuel NOx formation. So, the NOx mean concentration decreases in the central zone of the burner.

Figure 90. Profiles of NOx concentration of No. 4 burner with different SR at x =2.4 db (1920mm).

Figure 90 shows profiles of NOx concentration, where x is the distance from the measured points to the burner outlet along the jet flow direction. The NOx concentrations of No. 4 burner were measured by a water-cooled gun through the monitor port at the side wall near

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85

the burner. In Figure 90, r is the distance from sampling point to the burner axis. As SR increases from 0.79 to 1.02 or decreases from 1.66 to 1.02, the NOx concentration declines. The results are in concordance with the above analysis. In conclusion, when the stoichiometric ratio of burner is more or less than 1, the oxidative atmosphere decreases and the reducing atmosphere increases in the center of burner region so that the NOx formation reduces.

(3) Effect of Air Supply of Up and Bottom Burners on Nox Emission and Coal Burnout With different openings of dampers in the up and bottom air boxes, the O2 concentration at the outlet of economizer is at the range from 3.0% to 3.5%, the furnace gas temperature is 1291-1359℃ at the elevation of 6100mm and 1374-1443℃ at the elevation of 8300mm. It shows that the different air supply of up and bottom burners has no influence on the total air supply and normal coal combustion in the furnace. Figure 91 shows the effect on the unburned combustible loss (q4) and the NOx emission in flue gas, where SR1 is the stoichiometric ratio of up burners and SR2 is the stoichiometric ratio of bottom burners. As the SR1 and the SR2 are farther from 1, the NOx emission is less and the unburned combustible loss is larger. The unburned combustible loss with SR1>1 and SR2 Φ + (am ) and Φ − (ai ) < Φ − (am ) ⎫ ⎫ ⎪ I ⎪ + ⎪⎪ + − − if ⎪ai P am (a i outrank a m ) ⎨Φ (ai ) = Φ (am ) and Φ (ai ) < Φ (am ) ⎬ ⎪ ⎪ ⎪ + ⎪⎪ + − − ⎪ ⎩Φ (ai ) > Φ (am ) and Φ (ai ) = Φ (am ) ⎭ ⎪ ⎪⎪ ⎪⎪ ⎨ I ⎬ Φ + (ai ) = Φ + (am ) and Φ − (ai ) = Φ − (am ) ⎪ ⎪ai I am (a i is indifferent to a m ) if ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ I ⎪⎩ai R am (a i and a m are incomparable) otherwise ⎪⎭

(7)

where PI, II, and RI stand for preference, indifference and incomparability. Finally ai outranks am if:

Φ + (ai ) ≥ Φ + (am ) and Φ − (ai ) ≤ Φ − (am )

(8)

Equality in Φ+ and Φ- indicates indifference between the two compared alternatives. Under the Promethee I method some actions remain incomparable, in the case that a complete preorder is required that eliminates any incomparable items, then Promethee II can give a complete ranking as follows [31]:

Φ net (ai ) = Φ + (ai ) − Φ − (ai )

(9)

The net flow is the difference between the out-flow and the in-flow.

4. EVALUATION OF ALTERNATIVE ENERGY PROJECTS USING PROMETHEE 4.1 The Proposed Energy Options 4.1.1. Photovoltaic Photovoltaic conversion technology (PV) was initially developed in the late 1950s as part of the space programme which required a reliable and inexhaustible source of energy. PV has been known since the end of the last century but the first commercial application was achieved in 1954; by Bell laboratory researchers in the United States when they perfected the first photovoltaic cell using monocrystalline silicon. Nowadays, its use is spreading very rapidly, in part due to advances in technology, even to terrestrial applications such as fuel for isolated users or for installations in buildings linked up to a pre-existing electricity grid.

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The growth in the PV market certainly represents one of the long term strategic objectives for future worldwide energy policy and poses a research challenge in the field of RES. In general, commercial competitiveness is the element that most heavily affects the extent of the spread of installations exploiting RES as this is heavily affected by the cost of building the installations and their operating and maintenance costs. The production costs of technologies that exploit solar energy are unfortunately still extremely high but these could be reduced in the future if production volumes improve. From a theoretical point of view, cost reductions can come about when a combination of improvements is seen: in technological performance of production together with the optimisation of production cycles to enhance levels of the amounts produced. The crucial parameter for shifting the production cost curve is represented by capacity to innovate that is generated by research activity. Thus cost reductions can be achieved by steady market growth together with research efforts and spinoffs from other high-tech sectors of industry, such as micro-electronics, nanotechnology, the car industry and space sector. As has happened in other technological sectors, new products will come onto the market thereby allowing further cost reductions. The world PV market has seen considerable development over recent years, with a recorded annual growth rate of more than 30% and reached a total installed capacity of over 5000 MW in 2005 [33]. According to data from the report Marketbuzz, 2006 [34], despite the high price of silicon, new installations worldwide in 2005 amounted to 1,460 MW; a growth of 34% compared to 2004. 57% of the installations were in Germany alone leading it to exceed 1,500 MW of total power [33]. Market conditions differ substantially from country to country and this is due to the different energy policies implemented by different governments, whether there are support programmes for renewable sources and the differing degree of liberalisation of the electricity market. It is interesting to note that the specific legal provisions and regulations adopted by each country affect the effectiveness of any measures adopted to develop this sector. In some states where the rules do not include a system of charges to cover expenditure then the market impact is fairly marginal. In other countries, where prices are sufficiently high, effectiveness can be limited by allowing incentive tariffs for too short a period of validity or by bureaucracy and administration that is overly complicated and labyrinthine. In Europe, the markets in Germany and Spain have been highly dynamic and production of cells and modules has grown more rapidly than in other member states. Undoubtedly, this is attributable not only to a model of incentives that is clear, transparent and streamlined, but also to stable political and socio economic conditions that have favoured the creation of a secure and trustworthy climate for investors. Such a scenario has not only persuaded private and commercial investors to install PV plants but has, above all, boosted investments in R&D and has created the conditions to expand production capacity of cells and modules of industries in this sector[35].

4.1.2. Wind Power Nowadays, wind energy is without a doubt the most mature and commercially competitive of the new renewable sources and represents the segment of the market enjoying the highest growth rate in the entire energy sector. The expansion and the commercialisation of wind turbines has always coincided with their technological development. Now the market and technologies are specialising in order to maximise production under all conditions. Thus, there are now wind turbines specifically designed for offshore applications, to handle variable windspeeds and direction, turbines for turbulent winds and for small scale applications (from

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farms on the American flatlands to villages in developing countries). Modern wind turbines have modular features, they are reliable and can be installed on site in a very short space of time, they are built to operate continuously, are low maintenance and require few personnel over their lifespan lasting more than 20 years [36]. Areas for research and development (R&D) are numerous and concern mainly the use of innovative materials in order to build increasingly larger systems at contained prices and to increase the efficiency and reliability of the system. The geographical area that is most widely equipped with wind power installations is Europe, accounting for around 74% of the total, with 50% of this being the German Wind Park. Spain, Denmark and Italy are the other countries in Europe with significant wind energy installations whilst in the rest of the world the USA takes the lead with 14% of the total, India accounts for 6%, while China and Japan the figure is around’1.5%. Whilst Germany is the undisputed world leader in the sector, the USA has shown an upward but inconstant trend in wind power development and India has also shown marked development in recent years [36]. Electricity production from wind power in the EU amounted to 69 TWh in 2005 with Germany (27.2 TWh) and Spain (21 TWh) accounting for more than 50% of this. Denmark came in third place (6.6 TWh), followed by Italy, Holland and the UK with production of around 2 TWh [33]. The industry that produces wind generators is highly concentrated, with the four leading firms accounting for around 70% of total power in Europe in 2005: the Danish company Vestas with 35%, the German Enercon with 14.4%, Gamesa from Spain with 13.4%, and the US business GEWind with 12.4% [33]. The costs of generating electricity from wind sources have come down steadily and visibly over the last 15 years as a result of the increased efficiency of wind turbines as well as their lower cost due not only to economies of scale that have been seen in the sector but also as a result of research and new technologies available, especially in the processes involved in manufacturing the various component parts of the aerogenerators.

4.1.3. Solar Chimney The thermal solar chimney is a recently developed technology patented by “Schlaich Bergermann und Partner” which uses a large cylindrical tower that is able to exploit energy from the sun to produce electricity. The system comprises a glass collector, a chimney and wind turbines. It works on the basis of the following principle: a large mass of cold air enters freely underneath a large glass roof (glass collector) that is open around its periphery. Solar radiation heats this air until it reaches a temperature in excess of 35°C, thus creating an artificial greenhouse. The hot air tends naturally to move towards the centre of the collector where a cylindrical tower made of cement is located. This mass of hot air rises (hot air being less dense and thus lighter than cold) up the chimney tower thereby supplying a natural convective force (see Figure 4) [37] [38]. This flow of air, which rises at a speed of 14-16 m/s, is captured by a set of wind turbines located at the chimney base which convert kinetic energy from the wind induced by solar heat into mechanical energy and then into electricity. 4.1.4. Parabolic Solar Trough The technology relating to linear parabolic troughs is at a more advanced stage than other types of concentrated solar power technologies (parabolic dish and solar tower). Systems

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using linear parabolic mirrors called SEGS (Solar Electric Generating Systems) are to date the most developed commercially speaking, typically ranging in size from 30 to 80 MWatt.

Figure 4. Solar chimney principle [37].

Figure 5. Diagram of a parabolic trough power plant with two-tank molten salt storage[41].

These devices first appeared in 1984 when the LUZ Company installed a 14 MW solar energy power plant (SEGS I) in southern California, using parabolic trough solar collectors and supplementing it with natural gas as fuel to allow the system to work during periods

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when there is little sun or while closed down for maintenance. A parabolic trough power plant with heat storage is made up of three basic parts [39] [40] [41]: 1) the solar field fitted with a circuit for heat transfer; 2) a system for storing heat; 3) a power block comprising a turbine, a generator and a cooling system (see Figure 5). This technology uses a curved mirror system to concentrate solar radiation onto a high thermal performance absorbent pipe laid along the focal line of the concentrators inside of which flows a fluid for heat transfer. In such an installation, the solar field has a modular structure composed of linear parabolic collectors linked in series laid out in parallel rows up to several hundred metres in length. The fluid that carries the heat absorbed from the sun is generally a mineral oil and is pumped through receiver pipes to a power plant. Here a heat exchanger converts the heat into steam which is then sent to a turbine to produce electricity.

4.1.5. Solar Tower The technology relating to Solar power towers is commercially at a less advanced stage than linear parabolic trough collectors. Despite this, a number of experimental stations have been tested on the field in a variety of sites scattered all over the world in the last 15 years. These have demonstrated their project feasibility and the economic potential of this technology. The plant consists of a set of mirrors, called heliostats, that track the movement of the sun on a double axis and which reflect solar energy onto a receiver (heat exchanger) mounted on the top of a tower positioned at the centre of the array of mirrors. A fluid transfers the heat from the receiver to a steam generator that drives a turbine. The heat transfer medium can be: steam/water, molten salts, liquid sodium or air. If gas or compressed air is used then extremely high temperatures can be reached thus achieving a very high level of efficiency [42]. As for other renewable energy sources, one of the limitations solar energy has to overcome is the inconstancy of the energy available which means that storage systems are a highly important feature that affect the advancement and spread of the technologies developed. A storage system has to guarantee energy supply even when no solar energy is available (at night or when the sky is overcast or cloudy). The energy produced by thermal solar installations may not necessarily be limited only to hours of sunshine and by cloud movements. The central receiver can heat the fluid, for example molten salts, which also serve as energy storage. The hypothetical installation consists of a type of solar power tower like the ones known as PS10 with a power 11 MWe which are to be built in Spain. The PS10 solar tower according to designers should be capable of producing 23 million kWh of electricity annually, that is enough energy to satisfy the demand of 10.000 families. The project envisages the use of saturated steam as Heat Transfer Fluid (HTF). The system combines a field of 624 heliostats (each with a surface area of 121 m2) produced by the Spanish firm Solucar, for a total reflective surface area of 75,216 m2, a 100 metre high tower, a receiver that is able to produce saturated steam at 40 bar and 250 C° and heat storage system for steam with a capacity of 15 MWh able to supply 50 minutes of plant operation time at 50% load [43].

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Figure 6. Flow diagram of the PS10 solar tower power plant [44].

4.1.6. Dish - Stirling Dish – Stirling systems have been tested mainly in the United States and in Europe since the mid 1980s and the results have been highly encouraging. In these systems the conversion of solar energy into electricity is particularly efficient with a net average annual yield rate ranging between 18 and 23%, higher than any other solar energy system, and have attained a record rate of 29% for a brief time. The dish concentrator reflects solar rays onto a concave receiver positioned at the focal point of the concentrator. Solar radiation is absorbed by the receiver which heats a gas (helium or hydrogen) in the Stirling engine up to a temperature of around 650°C [44]. The heat from the sun is converted into mechanical energy by the Stirling engine and this mechanical energy is subsequently converted into electricity by a generator directly connected to the engine. Optimal functioning requires that the concentrator is perfectly orientated towards the sun, therefore it is mounted on a two-axis tracking system that allows the concentrator to be aligned vertically and horizontally (see Figure 7).

Figure 7. Scheme of the Dish/Stirling system [44].

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The alignment towards the sun is controlled by a tracking sensor of the sun or by a special software that instantaneously and continuously calculates the position of the sun [45] [46]. The choice of installing a Stirling engine is dictated by the fact that the Stirling cycle is the most efficient thermodynamic cycle for transforming heat into mechanical energy and electricity, thus its extraordinary properties make it suitable for this type of application. The size of individual installations can vary between 5 and 50 kWatt. The most important feature of these systems is their modularity allowing installations of any size or power to be built. The beauty of this technology is that its size can be adjusted to fit user needs: from a few kW, for use in remote sites or islands, up to hundreds of kW for “distributed generation” uses connected to the electricity grid. Unfortunately, the high unit costs reflect the fact that these systems have not reached a high level of technological maturity. The technology is still at the prototype stage of development and the industrialisation that would allow it to be commercially exploited on a large scale is not foreseen in the short term.

4.1.7. Biomass The case studied envisaged the construction of a 10 MWe installation using a steam boiler fuelled by energy crops, in particular by “Miscanthus” which is capable of producing 38,6 MWth with a 25% yield. A fluid bed combustion system was chosen over a oven grid combustion system despite its higher cost because it offers greater assurances in terms of environmental performance. Environmental performance levels are important because this system can reduce SOx by as much as 90% to stable gas residues and NOx as a result of the lower temperature for combustion. The phase to remove pollutants concludes with the extraction of particulate from smoke by the use of filters.

4.2. Sets of Criteria: Identification and Selection The criteria are the tools that enable alternatives to be compared from a specific viewpoint. Undoubtedly, selecting criteria is the most delicate part in formulating the problem before the decision maker, and thus it is requires the utmost care and attention. The number of criteria is heavily dependent on the availability of both quantitative and qualitative information and data. Here 14 criteria were selected; 9 of these technical-economic and 5 socio-environmental. Quantitative measures apply to 8 of the criteria while the remaining 6, being qualitative in nature, were scored by applying impact scales from either 1-4 or 1-5.

Economic and Technical Criteria These criteria refer to the costs that must be borne in order to realize the various projects included in each strategy and to guarantee the supply of energy. These factors are of special interest to State authorities. •

Investment costs. This includes all costs relating to the purchase of mechanical equipment, technological installations, to construction of roads and connections to the national grid, to engineering services, drilling and other incidental construction work. This criterion is measured in Euros;

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•

•

•

•

•

•

•

193

Operating and maintenance costs. This includes all the costs relating to plants, employees’ wages, materials and installations, transport and hire charges, and any ground rentals payable. This criterion is measured in Euros; Levelized electricity cost (LEC). This measures the production cost per kWh of the electricity generated by the plant expressed as Euro cents. This parameter is important and useful for assessing how commercially competitive the system is compared with conventional energy production technologies; Financial risk. This identifies the degree of financial risk attached to the technological options proposed and is measured using the following scale of values: − Low risk=1 − Medium=2 − High=3 − Very high=4 Primary energy saving. This refers to the amount of fossil fuel currently used by power plants to produce electricity that could be saved. It is measured in ton/per annum; Maturity of technology. Measures the degree of reliability of the technology adopted as well as how widespread the technology is at both national and European level. This is appraised using a qualitative judgment transformed into the following fourpoint scale [6]: − Technologies at theoretical level=1 − Technologies tested in laboratory= 2 − Technologies only performed in pilot plants and/or under construction =3 − Technologies requiring further improvements to increase their efficiency levels=4; − Commercially mature technologies on the market=5; Continuity of power supply: This criterion indicates whether the energy supply is subject to interruptions (e.g. PV does not work at night, wind generators cannot function when there is no wind, etc.) and thereby affects the stability of the electricity grid. This case is also evaluated qualitatively and expressed via the following fourpoint scale: − Highly discontinuous activity =1 − Moderately discontinuous activity =2 − Slightly discontinuous activity =3; − Stable and continued activity (except when the plant undergoes maintenance)=4; Storage capacity : this criterion indicates whether any storage systems are fitted that are able to ensure continuity of electricity supply (e.g. in the absence of solar radiation or other factors ) and is calculated on the basis of the number of hours of autonomy provided; Realization time. This measures the time to realize and put into operation the plants designed. It is expressed in number of months.

Environmental and Social Criteria These criteria refer to protection of the environment and to the principle of sustainability:

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•

•

•

•

Sustainability of Climate Change: This refers to the amount of CO2 emissions avoided as a result of the production of the proposed plants. It is measured in tons/per annum. Sustainability of other impacts: This criterion takes into account other impacts: the visual nuisance that may be created by the development of a project in a specific area or any noise disturbance and odours arising from productive activity of plants, the potential risk to eco-systems caused by the production operations of the various projects included in the strategies. This is also measured qualitatively and translated into the following five-point scale [6]: − Extremely high impact=1 − High impact=2 − Moderate impact=3 − Slight impact=4 − No impact =5. Social acceptability. Expresses the index of acceptance by the local population regarding the hypothesized realization of the projects under review. The following four-point qualitative scale was applied: − The majority of inhabitants are against the installation of any plant whatsoever regardless of where it is =1 − The opinion of the population regarding the installations is split =2 − The majority accepts the installations provided they are located far from residential areas =3 − The majority of inhabitants are favourably disposed towards the installations =4 Contribution to local development. This criterion estimates the global social and economic effects that may be felt in the areas affected by the initiatives. The potential effects are: the creation of new jobs, new supply chain businesses, emerging energy sector businesses, industrial districts etc. The following rating scale was applied: − Impact on local economy rated weak =1 − Impact on local economy rated moderate (some permanent jobs)=2 − Impact on local economy rated medium-high (jobs + supply chain businesses)=3 − Impact on local economy rated high (strong impetus to local development, creation of small industrial districts)=4 Land use. This criterion quantifies the area occupied by the plants and not available for possible alternative uses (i.e. agriculture or other economic activities). It is measured in m2 of land used.

4.3. The Evaluation Matrix Table 1 shows the matrix containing the alternatives and how these perform with respect to the evaluation criteria selected. The options proposed are the following: wind power, a medium sized installation made up of 15 turbines of 600 kW each giving a total power of 9 MW (W.1) and a larger 30 MW installation using 15 turbines of 2000 kW each (W.2). The two photovoltaic options considered are a 5MW installation (PV.1) and one of 15 MW (PV.2).

Table 1. Evaluation matrix Criteria Alternatives

A.1 Investment costs

A.2

O&M cost

A.3

LEC

A.4 Financial risk

B.1 Primary energy saving

B.2 Maturity of technology

B.3 Continuity of power supply

B.4

B.5

Storage

Realization

capacity

time

C.1

C.2

Sustainabilty

Other

of climate

environmental

change

impacts

C.3 Social acceptability

C.4 Contribution to local development

C.5 Land use

Euro (000)

Euro (000)

c/Euro

qualitative

ton/y

qualitative

qualitative

hours

months

ton/y

qualitative

qualitative

qualitative

Km2

W.1

Wind 600kW

10,800

324

0.07

low

10,180

commercial

slight disc.

0

18

17,720

slight

accept

moderate

0.02

W.2

Wind 2000kW

45,000

1,350

0.065

low

35,650

commercial

slight disc.

0

36

62.000

high

majority no

moderate

0.065

30,000

450

0.6

middle

3,431

further impr

high disc.

0

24

5,900

slight

favorable

moderate

0.09

75,000

1,125

0.5

high

10,290

further impr

high disc.

0

36

17,910

moderate

split

medium-high

0.27

153,000

11,800

0.17

very high

34,000

theory

10

36

59,160

high

majority no

medium-high

6.831

99,000

6,800

0.15

very high

21,000

theory

10

36

36,540

high

majority no

medium-high

4.153

117,000

2,879

0.106

middle

70,000

pilot

3

36

121,800

moderate

split

weak

1.459

91,000

2,670

0.12

middle

55,900

pilot

high disc.

0

24

97,200

moderate

split

weak

0.855

36,000

2,175

0.22

middle

12,000

pilot

high disc.

0.5

36

20,880

slight

split

moderate

0.372

180,000

8,035

0.38

very high

26,300

lab

high disc.

0

48

45,700

moderate

accept

moderate

0.84

40.000

1,025

0.9

low

6,690

commercial

slight disc.

0

24

19,400

high

majority no

medium-high

0.06

PV.1

PV.2

SCh.1

SCh.2

PST.1

PST.2

STo

DS

Biom

Photovoltaic 5MW Photovoltaic 15MW Solar Chimney 30MW Solar Chimney 15MW Parabolic solar trough 50MW Parabolic solar trough 50MW Solar Tower 11MW Solar DishStirling Biomass 20MW

middle disc. middle disc. middle disc.

Table 2. Thresholds

A.1

A.2

A.3

A.4

B.1

B.2

B.3

B.4

B.5

C.1

C.2

C.3

C.4

C.5

Min/Max

Minimize

Minimize

Minimize

Minimize

Maximize

Maximize

Maximize

Maximize

Minimize

Maximize

Maximize

Maximize

Maximize

Minimize

Weight

0.8

0.8

0.9

0.7

0.9

0.4

0.7

0.4

0.4

0.9

0.9

0.5

0.5

0.8

V-Shape

V-Shape

Linear

Linear

Linear

Linear

Linear

V-Shape

V-Shape

Linear

Linear

Linear

Linear

Linear

-

-

5%

0.5

5%

0.5

1

-

-

5%

0.5

0.5

0.5

5%

10%

10%

15%

1

10%

1

1.5

2

10

10%

1

1

1

10%

Percent

Percent

Percent

Absolute

Percent

Absolute

Absolute

Absolute

Absolute

Percent

Absolute

Absolute

Absolute

Percent

Preference function Indifference threshold Preference threshold Threshold unit

Table 3. Preference flows Actions W.1 W.2 PV.1 PV.2 SCh.1 SCh.2 PST.1 PST.2 STo DS Biom

Φ+ 0.6531 0.5524 0.4385 0.3218 0.2752 0.2582 0.449 0.4503 0.407 0.276 0.4035

Φ0.181 0.238 0.404 0.484 0.511 0.555 0.371 0.363 0.407 0.559 0.412

Net flow Φ(a) = Φ+ (a) − Φ− (a) 0.473 0.315 0.034 -0.16 -0.24 -0.3 0.078 0.088 0 -0.28 -0.01

Ranking 1 2 5 8 9 11 4 3 6 10 7

Table 4. Weight stability interval

Criteria A.1 A.2 A.3 A.4 B.1 B.2 B.3 B.4 B.5 C.1 C.2 C.3 C.4 C.5

Figure 8. Partial ranking.

Weight 0.8 0.8 0.9 0.7 0.9 0.4 0.7 0.4 0.4 0.9 0.9 0.5 0.5 0.8

Absolute values Min 0.621 0.384 0.799 0 0.766 0.03 0 0.027 0.302 0.69 0.846 0.399 0 0.489

Max 1.067 1.039 1.476 0.815 1.316 0.458 0.801 0.501 0.485 1.342 1.471 0.826 0.59 0.912

Weight 8.33% 8.33% 9.38% 7.29% 9.38% 4.17% 7.29% 4.17% 4.17% 9.38% 9.38% 5.21% 5.21% 8.33%

Relative values (%) Min 6.59% 4.18% 8.41% 0.00% 8.09% 0.33% 0.00% 0.30% 3.18% 7.35% 8.86% 4.20% 0.00% 5.27%

Max 10.81% 10.56% 14.50% 8.39% 13.14% 4.74% 8.25% 5.16% 5.01% 13.36% 14.46% 8.32% 6.08% 9.39%

Figure 9. Final ranking.

Figure 10. Partial ranking (equal weight for criteria).

Figure 11. Final ranking (equal weight for criteria).

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The options using solar chimney technology relate to one installation with a power capacity of 30 MW (SCh.1) installed on a site where solar radiation amounts to 1,800 kWh/m2/y (typical of the southern Mediterranean), and the other of 15MW (SCh.2) which is designed to be installed in areas with solar radiation of 2,300 kWh/m2/yThe proposed options for linear parabolic trough collectors are a 50MW installation (PST.1), fitted with an energy storage system and the other of the same capacity but without any storage system (PST.2), The remaining options proposed are one using solar power tower technology with a power capacity of 10MW (STo), an installation containing parabolic dish-stirling concentrators (DS) and a steam boiler of 20 MW fuelled by biomass (energy crops) (Biom).

Figure 12. GAIA plane.

The performance data relating to items measured quantitatively were extrapolated from published findings in the literature. The data evaluated qualitatively are the outcome of assessments and estimates. Before looking at the results in detail it is important to clarify a number of points regarding the data reported in the matrix. The costs relating to investment and maintenance, the industrial cost of production per kWh and the level of energy production, have been calculated based on data extracted from the following publications:

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[41] [43] [44] [45] [46] [47] [48] [49]. The criterion relating to financial risk based on market estimates shows a very high risk profile for options SCh.1, SCh.2 and DS reflecting the fact that the technologies are still in the preliminary study phase or in the experimental phase in the laboratory, thus their economic and financial profile is highly uncertain. The continuity of energy supply is an extremely important criterion to ensure that the grid is stable and not liable to black-outs. Nearly all the technologies analysed, with the exception of those equipped with a storage system, are subject to a fair degree of discontinuity, either because of differences between daytime and night or varying meteorological/climatic conditions. Environmental impact is probably the most delicate item of the whole assessment. The most significant impact is found for the option using biomass due to emissions of some pollutants (not harmful), and for the solar chimney options as result of the large area of land required to build the installation on, as well as the great visual impact created by the tower. It is also reasonable to assume that the wind energy installation will also be visually intrusive because the wind turbines are so large in size. These also constitute a potential collision hazard to birds in flight. The alternatives relating to parabolic trough and solar tower technologies apart from occupying a certain amount of land do not give rise to any other significant changes to the environment therefore their environmental impact are judged to be moderate. The greatest uncertainties attach to the evaluation of social acceptability and their contribution to local development as these could be strongly affected by prejudices that bias the results. Generally speaking, the population views installations using renewable sources favourably, but only if they are built far from human settlements and do not cause any nuisance to the population. The level of acceptance will therefore depend greatly on the exact locations of the installations. Estimates relating to these two criteria come from a survey carried out on a sample of the population that did not show react positively to the proposed projects.

4.4. Main Results Two distinct rankings of alternatives are computed and displayed. The first is PROMETHEE I which gives a partial ranking. It is based on strongly established preferences so some actions remain incomparable under this method. Figure 8 graphically illustrates the positions of each alternative in the partial ranking and it is immediately apparent that the best performers are W.1, W.2 and PST.2. The first two of these also come out top in the final ranking and thus attest to the credibility of both the method and to the findings. Clearly, wind power is at a more commercial and competitive stage of development compared to other new energy technologies and the results obtained are comforting in that they are consistent with a well-consolidated fact. Next in the ranking come the options PST.1, PV.1, STo, Biom, and PV.2. The lowest ranked are the options SCh.1 and SCh.2 which are shown to be incomparable with the DS option. These technologies are still heavily penalised by high costs of investment and maintenance. The highly innovative status of these projects means that technologically they are still very immature and this greatly affects the economic-financial risks attached to these initiatives. Figure 9 shows the results from PROMETHEE 2 which gives a complete ranking: all actions are ranked from the best to worst leaving no incomparable pair of actions. This

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information is easier to use than partial ranking but does reflect less reliable preferences. Table 4 gives the weights assigned to the criteria together with the weight stability intervals that give for each criterion the limits within which its weight can be modified without changing the PROMETHEE II complete ranking. The results of multi-criteria analysis hinge on the weightings allocated and thresholds set. As stated earlier, the weights express the importance of each criterion and obviously may deeply influence the final outcome of the entire calculation procedure. For some authors, the problem of how to determine the weights to assign is still unresolved since the different outranking methods do not lay down any standard procedure or guidelines for determining them. In order to examine any changes in the final outcome after correcting the weights previously assigned to the various criteria a sensitivity analysis was performed by assigning an equal weight to all the criteria selected. This calculation revealed that the position in the ranking did change for some options although the overall structure of the ranking from the previous analysis was more or less unchanged. The options PV.1 and Biom moved up from 5th and 7th place to 3rd and 4th and emerged as incomparable with each other. The options PST.1 and PST.2 slid from 2nd and 3rd place down to 5th and 6th place and were also found to be incomparable (see Figure 10 and 11). The GAIA (geometrical analysis for interactive aid) plane provides the decision maker with a comprehensive graphical image of the decision problem and it is complementary to the multicriteria analysis. This tool provides clear graphical information regarding the conflicting characters of the criteria and about the weights on the final decision. Figure 12 illustrates this analysis: actions are represented as triangles and criteria as lines. The direction of the PI axis (the line that joins the two dots) identifies the compromise solution, in our case the axis is moving towards alternative W.1 and W.2. By examining a GAIA graph it is possible to see where the action lies in relation to the criteria, measure how intensely the criteria affect each action and thereby identify the criteria that are in line with or conflict with the various alternatives.

CONCLUSION Figure 8 graphically illustrates the positions of each alternative in the partial ranking and it is immediately apparent that the best performers are W.1, W.2 and PST.2. These are followed by the options PST.1, PV.1, STo, Biom, and PV.2. The bottom ranking positions are occupied by the options SCh.1 and SCh.2, which are shown to be incomparable with the DS option. A sensitivity analysis was performed by equally weighting all the criteria and comparing the ranking obtained with the original. The two rankings were found to be similar although the position of some options shifted slightly; with the options PV.1 and Biom moving up from 5th and 7th place to 3rd and 4th and emerged as incomparable with each other whilst PST.1 and PST.2 moved down from 2nd and 3rd place to 5th and 6th and were also found to be incomparable with each other. This work has therefore attempted to test the soundness and strength of multicriteria analysis as a means to serve energy planners as an unambiguous tool for decision making. DSS are well-suited to dealing with highly structured problems or semi-structured ones (for which it is not possible to produce an ideal solution). DSS help the user in all phases of the

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decision making process by using relevant information in order to make choices that can be documented and are transparent. Assessment procedures and energy planning may appear complex because of the number and diversity of the items to evaluate, the uncertainty of data and conflicts between interested parties. The decision making process of an energy project is the closing link in the process of analysing and handling different types of information: environmental, technical, economic and social. As this work demonstrates, multicriteria analysis can provide a technical-scientific decision making support tool that is able to justify its choices clearly and consistently.

ACKNOWLEDGMENT I wish to thank Dr. Maria Cristiana Laurà and Ms Susan H. Parker B. A., A.C.A. for their precious help and cooperation.

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In: Leading-Edge Electric Power Research Editor: C. M. O’Sullivan, pp. 205-238

ISBN: 978-1-60021-981-8 © 2008 Nova Science Publishers, Inc.

Chapter 4

GAS TURBINES AND ELECTRIC DISTRIBUTION SYSTEM Francisco Jurado∗ University of Jaén, Department of Electrical Engineering 23700 Alfonso X, nº 28, EPS Linares (Jaén), Spain

ABSTRACT Lately, the use of gas turbines following the deregulation of the electricity supply industry has become greater quickly. The motivation for modeling the gas turbines and their controllers is determinant to the interpreting of their impacts on distribution systems. The model predictive control (MPC) is used to damp the oscillation when the power distribution system is subjected to a disturbance. MPC is selected because it can explicitly handle the nonlinearities, and constraints of many variables in a single control formulation. The IEEE 13 node power distribution system is employed to demonstrate the effectiveness of MPC to damp the oscillations of gas turbines. Among fossil fuels, gas is the most quickest, with a growth rate nearly double that of coal and oil. The electricity generation field is the leading market for gas. The natural gas business has a great interaction with the electricity market in terms of fuel consumption and energy conversion. On the other hand, the transmission and distribution activities are very similar with the natural gas transportation through pipelines. The power losses in gas and electric systems are compared. It is also demonstrated that the electricity system results more convenient for longer distances of gas wells from electricity consumption area.

Keywords: Distribution networks, gas turbines, modeling, power loss, predictive control.

∗

E-mail: [email protected] Telephone: +34-953-648518 Fax: +34-953-648586.

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NOMENCLATURE Ao a, b, c cpa cpg cps

compressor exit flow area valve parameters specific heat of air at constant pressure (J/(kg K)) specific heat of combustion gases (J/(kg K)) specific heat of steam (J/(kg K))

Dk

internal diameter of pipe between nodes (m)

e1 f Fd

Fk

valve position vector of mass flow rates through branches fuel demand signal Fanning friction coefficient

fk

flow rate through compressor (m3/s)

f kij

pipeline flow rate (m3/s)

G

Hk KI KP kf kLHV LHV

Lk ma N

NP Pc pcin pcout

gas specific gravity horsepower required to pump gas down PID parameter PID parameter fuel system gain constant factor which depends on LHV lower heating value (MJ/kg) pipeline length between nodes (m) polytropic index rotation speed of the turbine (rad/s) branches in the system

PDi

compressor power consumption (W) air pressure at compressor inlet (Pa) air pressure at compressor outlet (Pa) real power required at the ith bus

PGi

real power generated at the ith bus

PL Pm PT pTin pTout rc t T Tcout Tis

real power loss mechanical power delivered by turbine (W) total mechanical power delivered by turbine (W) pressure of combustion gases at turbine inlet (Pa) pressure of combustion gases at turbine outlet (Pa) pressure ratio (outlet/inlet) time (s) mechanical torque delivered by turbine (Nm) outlet air temperature (K) temperature of injected steam (K) average gas temperature (K)

Tka

Gas Turbines and Electric Distribution System

Tki

compressor suction temperature (K)

T0

standard temperature (K)

TTin U(t) Vi YT , UT w wa wf wg wis

wL

turbine inlet gas temperature (K) control signal voltage magnitude at the ith bus finite time Fourier transforms vector of gas injections at each node air mass flow into the compressor (kg/s) fuel mass flow (kg/s) turbine gas mass flow (kg/s) injection steam mass flow (kg/s) vector of gas demands

wS

vector of gas supplies

Yij

Za

magnitude of the i-jth element of the bus admittance matrix average gas compressibility factor

Z ki

gas compressibility factor at compressor inlet

⎛ cp ⎞ ⎟ ⎝ cv ⎠

specific heat ratio ⎜

γij Δh25 ΔhIC ΔhIT ΔN δi

ηk

angle of the i-jth element of the bus admittance matrix specific enthalpy of reaction at reference temperature of 25ºC (J/kg) isentropic enthalpy change for a compression from pcin to pcout (J/kg) isentropic enthalpy change for a gas expansion from pTin to pTout (J/kg) rotation speed deviation of the turbine (rad/s) phase angle of the voltage pipeline efficiency overall compressor efficiency compressor efficiency

ηT ηtrans η∞c

overall turbine efficiency transmission efficiency from turbine to compressor compressor polytropic efficiency

πi

pressure at node i (Pa)

πj

pressure at node j (Pa)

πic

compressor suction pressure (Pa)

π jc

compressor discharge pressure (Pa)

π0

standard pressure (Pa)

ρi τf

inlet air density fuel system time constant (s)

ε

ηc

207

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1. INTRODUCTION Gas turbines can offer solutions to today’s energy situation as a supplement or support function to the conventional central generation and power system (Willis and Scott, 2000). Complimentary answers are needed to meet projected growth in new load and peak demand while providing power system stability, security and end-user power quality solutions. Distributed generation (DG) integration into the existing power grid can enhance asset utilization without demanding major capital investment in new large generation or energy delivery facilities. Synchronous machine stability surveys have been a discipline of interest for many years. Much of the work produced has been based on steam- or hydro-turbine generating units. Power system stability is normally associated only with large utility systems. However, with DG operating in parallel with the utility, stability has become an issue that is crucial to preserve critical functions (Jurado and Carpio, 2005). Compared to the transmission system, the distribution system has several important characteristics. The power of DG is relatively small compared to the capacity of the substation. The substation is stiff enough to keep the frequency constant, thus can be conceived as an infinite bus. Model predictive control (MPC) is a control strategy that uses a model of the system to predict the response over a future interval, called the costing or prediction horizon (Maciejowski, 2002; Richalet, 1993; Qin and Badgwell, 2000). The application of MPC to control the gas turbine was introduced in (van Essen and de Lange, 2001; Vroemen et al., 1999). Model based control schemes are highly related to the accuracy of the process model. Evans concentrated on testing the gas turbine using small amplitude multisine signals and frequency domain techniques to identify linear models of high accuracy at a range of different operating points (Evans et al., 2000). The implementation of an efficient method for computing low order linear system models of gas turbines from time domain simulations is presented in (Jurado and Cano, 2004). This method is the Box-Jenkins algorithm for calculating the transfer function of a linear system from samples of its input and output. The fact that the dynamics of these models change with operating points evidenced that the gas turbine is nonlinear, so the need was evident for a more accurate nonlinear modeling of the gas turbine. The work was formulated further by Chiras who used Nonlinear AutoRegressive Moving Average with eXogenous inputs (NARMAX), to represent the global dynamics of the gas turbine. It was showed that both models were suitable for representing engine dynamics throughout its operating range (Chiras et al., 2002). The Hammerstein model is a special kind of nonlinear systems which has applications in many engineering problems (Narendra and Gallman, 1996). A frequency domain identification approach for Hammerstein models is proposed in (Jurado, 2005). By exploring the fundamental frequency, the linear part and the nonlinear part can be identified. The power system stability mostly depends on the excess kinetic energy stored in the generators during the fault duration period. The efficient control of the extra energy can be the most direct method to reach the system stabilization. There are various types of energy storage devices which can be applied to control the surplus energy. However, all of the energy storage devices have the common shortcomings that they are very costly and require highly advanced control strategy for the operations.

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It is attractive if the excess kinetic energy could be controlled through the adjustment of the fuel control valve. The MPC is designed for this purpose. In this work, the MPC is used to prevent the transient instability in power distribution system with DG. The unbundling of the electricity sector and the force of competition have developed new technologies of the generation and the delivery of electricity which mean less pollutant, higher efficiency, and less costly means of supplying the load. These technologies often apply to conventional (coal, oil, gas, hydro, nuclear) and unconventional (solar, wind, fuel cells, microturbines) sources of energy. The possible alternative of replacing coal and oil burning plants with natural gas plants could greatly meliorate the sustainability of forests, waters, and farmlands, which are negatively affected by acid deposition. Natural gas is produced primarily at remote sites and transmission pipelines, distribution pipelines, vast underground storage facilities, and compressors are fabricated to deliver the natural gas from wellheads to power generating sites and end users. Despite the strong worldwide growth in demand for natural gas, the reserves continue to rise, thanks to the improvements in gas exploration technologies. The exploitable proven reserves are of around 150 tera cubic meters (Tm3), with a residual life, at present consumption rate, of around 65 years; considering another 200 Tm3 of potential discoveries, gas has a future potential nearly twice that of oil. The electricity generation field is the leading market for gas. This extraordinary growth in the electricity sector is driven by various factors (DOE, 2001 a; DOE, 2001 b): (1) Of all forms of energy consumption, electricity is becoming the most important for final consumers, with an ever increasing penetration rate: today around 37% total. (2) The new technologies for combined cycle gas turbines plants allow a very high efficiency (close to 60%), low emissions and environmental impact, very attractive investment costs and short completion times. (3) The ever increasing environmental concerns for power plant emissions and the explosion of Independent Power Producers in a deregulated market that is quickly changing the rules of the game. A great number of pipeline systems are under study and for the medium/long term very long and high capacity pipelines are being considered (e.g. Yamal - Europe and Turkmenistan- Europe), analyzing also new technology solutions (high pressure pipelines) to reduce the gas transmission costs. This paper compares power losses between gas and electricity distribution systems, of interest when natural gas is needed for electricity generation. The article is organized as follows. In Section 2, general principles of gas turbines are explained. In Section 3, the Hammestein model is introduced. The MPC is formulated in Section 4. Section 5 presents a review of the components of gas system. Some basic concepts of the gas steady-state equations are presented in Section 6. Section 7 describes electric power losses. Section 8 depicts some simulation results and discussion. Finally, conclusions are presented in Section 9.

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2. GAS TURBINE MODEL The plant model is a physics based component level model (CLM) of this gas turbine configuration. This model is very detailed, high-fidelity, and models each component. A thorough introduction to the gas turbine theory is provided in (Cohen et al., 1998). There exist a large number of publications on the modeling of gas turbines. The model complexity varies according to the intended application. A detailed first principle modeling based upon fundamental mass, momentum and energy balances is reported in (Fawke et al., 1972). These models describe the spatially distributed nature of the gas flow dynamics by dividing the gas turbine into a number of sections. Throughout each section, the thermodynamic state is assumed to be constant with respect to location, but varying with respect to time. Mathematically, the full partial differential equation model is reduced to a set of ordinary differential equations, which are facilitated easily within a computer simulation program. For a detailed model, a section might consist of a single compressor or turbine stage. Much simpler models result if the gas turbine is decomposed into just three sections corresponding to the main turbine components, i.e. compressor, combustor and turbine, as in (Hussain and Seifi, 1992). Instead of applying the fundamental conservation equations, as described above, another modeling approach is to characterize the gas turbine performance by utilizing the real steady state engine performance data, as in (Hung, 1991). It is assumed that transient thermodynamic and flow processes are characterized by a continuous progression along the steady state performance curves, which is known as the quasi-static assumption. The dynamics of the gas turbine, e.g. combustion delay, motor inertia, fuel pump lag etc. are then represented as lumped quantities separate from the steady-state performance curves. Very simple models result if it is further assumed that the gas turbine is operated at all times close to the rated speed (Rowen, 1983). This model was utilized in a simulation of an island grid (Sharma, 1998). System simulation based on dynamic coupling equations is widely assumed and introduced (Schobeiri et al., 1994; and Garrard, 1996) with respect to aero engines and (Botros et al., 1991; Botros, 1994) with regard to compressor stations. For control purposes, fast simulation is demanded and the model configuration is kept as simple as possible. Air at the atmospheric pressure enters the gas turbine at the compressor inlet. After compression of the air to achieve the most favorable conditions for combustion, the fuel gas is mixed with the air in the combustion chamber. Then, the combustion takes place and the hot exhaust gases are expanded through the turbine to produce the mechanical power. In terms of energy conversion, the chemical energy present in the combustion reactants is transferred to the gas stream during combustion. This energy - measured in terms of gas enthalpy- is then converted into the mechanical work by expanding the gas through the turbine. Thus the excess mechanical power available for application elsewhere, after accounting for the power required to drive the compressor, is derived ultimately from the combustion process. Without combustion, assuming 100 percent efficient compressor and turbine operation, the power developed by the turbine would be exactly matched by the power required to drive the compressor. The main modeling assumptions are as follows:

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(1) air and combustion products are treated as perfect gases (2) specific heats are assumed constant for combustion products, air and injected steam (3) flow through nozzles is described by a one dimensional adiabatic uniform polytropic process (4) energy storage and transport delay in the compressor, turbine and combustion chamber are relatively small, thus steady state equations are applied. (5) inlet kinetic energy of gas flows into the compressor and turbine are treated as negligible (6) air mass flow through the compressor is controllable via inlet guide vanes. The compressor is described by one dimensional steady flow nozzle equation for a uniform polytropic compression. This can be expressed as (Jurado and Cano, 2004): 1/2 ma +1 ⎞ ⎤ ⎡ ⎛ ⎛ ⎞ 2ma 2/ ma m ⎟⎥ ⎢ wa = Ao ⎢⎜ − rc a ⎟ ⎥ ρ p ⎜r ⎜ η ( m − 1) ⎟⎟ i cin ⎜ c ⎜ ⎟ ⎠ ⎢⎣⎝ ∞c a ⎝ ⎠ ⎥⎦

(1)

Compressor power consumption equation is given by

Pc =

wa ΔhIC

(2)

ηcηtrans

Combustion energy equation is expressed as

w g c pg (TTin − 298 ) + w f Δ h25 + wa c pa (298 − Tcout ) +

(3)

+ wis c ps (298 − Tis ) = 0

Power delivery equation is written as

PT = ηT wg ΔhIT

(4)

Pm = PT − Pc

(5)

Figure 1 shows the block diagram of the gas turbine. The concept of the gas turbine control system, which is applied in this paper, is based on the Speedtronic Mark 4 description as presented in (Rowen, 1988). The fuel flow out from the fuel systems results from the inertia of the fuel system actuator and of the valve positioner. The fuel system actuator equation is:

wf =

kf e τ f s +1 1

(6)

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Francisco Jurado The valve positioner equation is:

e1 =

a Fd bs + c

(7)

The turbine torque function is given by:

T = k LHV ( w f − 0.23 ) + 0.5( Δω )

(8)

where the input variable to the fuel system is Fd. The output variable from the fuel system model is wf . A single gas turbine does not require the digital setpoint feature. The kLHV factor depends on the LHV. The kLHV and 0.23 factors cater for the typical turbine power/fuel rate characteristic, which rises linearly from zero power at 23 % fuel rate to rated output at 100 % fuel rate.

Figure 1. Block diagram of gas turbine control model.

Equation (8) allows the turbine torque to be calculated algebraically. This torque is used in the equations which model the mechanical system:

Pm = TN

(9)

In this paper, input variable to the turbine is wf and output variable from the turbine is N.

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3. HAMMERSTEIN MODEL Since MPC is a model-based control an internal model is needed to predict the future responses of the plant to control inputs. As the CLM is a very large and complicated model, a new model is developed to be used in the MPC. Following the model structure proposed in (Jurado, 2005), a Hammerstein model of a gas turbine is applied that meets the above specifications. The model is designed to replicate both transient and steady state performance. Consider the Hammerstein model shown in Figure 2, where u(t), v(t), y(t) and yf(t), are the system input, noise, output and filtered output, respectively. x(t) denotes the unavailable internal signal. These are continuous time signals. u(iTs) and yf(iTs) denote the sampled input and sampled filtered output signals respectively with the sampling interval Ts. The filter is a lowpass filter at the designer’s disposal.

Figure 2. Hammerstein model.

The goal of the frequency domain identification is to apply inputs of the form,

u ( t ) = A cos (ωk t ) ,

ωk ≠ 0,

t ∈ [ 0, T ]

(10)

and then, to determine a pair of the estimates fˆ (.) and Gˆ ( s ) based on the finite sampled inputs and filtered outputs u(iTs) and yf (iTs) so that

fˆ (.) → f (.) ,

Gˆ ( s ) → G ( s )

(11)

in some sense. Note that the continuous time model Gˆ ( s ) , not its discretized model, is our interest.

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4. MODEL PREDICTIVE CONTROL 4.1. Theoretical Background Model Predictive Control (MPC) refers to a class of control algorithms in which a dynamic process model is used to predict and optimize system performance. MPC is rather a methodology than a single technique. The methodology of controllers belonging to the MPC family is characterized by the following strategy illustrated in Figure 3.

Figure 3. Strategy of model predictive controller.

As shown in Figure 3, in MPC, the future outputs (gas turbine speed) for a determined prediction horizon Hp are predicted at each instant k using a prediction model. These predicted outputs yˆ ( k + j ) , j = 1,..., H p depend on the state of the model at the current time k (given, for instance, by the past inputs and outputs) and on the future control signals

u (k + j) .

The control signal (mass flow) change only inside the control horizon, Hc, remaining constant afterwards,

u ( k + j ) = u ( k + H c − 1) , j = H c ,..., H p

(12)

The set of control signals is calculated by optimizing a cost function in order to keep the process as close as possible to the reference trajectory (gas turbine speed reference),

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ω ( k + j ) , j = 1,..., Hp. This criterion usually requires a quadratic function of the errors between the predicted output signal and the reference trajectory. The control effort is included in the objective function in most cases. An explicit solution can be obtained if the criterion is quadratic, the model is linear and there are no constraints. Otherwise an iterative optimization method has to be used. In practice all systems are subject to restrictions. The actuators have a limited field of action, as in the case of valves. Constructive reasons, safety or environmental ones can cause limits in the system variables such as fuel flow or maximum temperatures and pressures. All of them lead to the introduction of constraints in the MPC problem. Usually, input constraints like

umin ≤ u ( k + j ) ≤ umax , j = 1,..., Hc

(13)

Δumin ≤ Δu ( k + j ) ≤ Δumax , j = 1,..., Hc − 1

(14)

are hard constraints in the sense that they must be satisfied. Conversely, output constraints can be viewed as soft constraints because their violation may be necessary to obtain a feasible optimization problem:

ymin ≤ y ( k + j ) ≤ ymax , j = j1 ,..., H p

(15)

where j1 represents the lower limit for output constraint enforcement.

4.2. Linear Model Based Predictive Control The basic idea is to use the linear model to predict the future system behavior. This model is used throughout the entire prediction horizon. Even if this model is very accurate at the linearization point, its accuracy decreases over the prediction horizon. As a consequence, there may be a significant prediction error at k + H p .

4.3. Hammerstein Model Based Predictive Control Due to the relatively simple block-oriented structure, the application of Hammerstein models in MPC is more straightforward than the application of the general Nonlinear AutoRegressive Exogenous (NARX) or NARMAX models. In this section, the Hammerstein model is implemented in MPC by inverting the static nonlinear model element f(u), as indicated in Figure 2. As the remaining part of the prediction model is the linear dynamic part of the Hammerstein model, the MPC optimization can be solved by quadratic programming. The combination of the inverse static nonlinear model element and the nonlinear system results in a transformed dynamical system. This system is linear if the system is of the

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Hammerstein type. As the inversion of the single-input single-output and multiple-input single-output static nonlinear model element is a straightforward analytical procedure, the computational demand of the controller is quite comparable to the linear generalized predictive control (GPC). This is a significant advantage compared to other nonlinear models which require the use of nonlinear programming or linearization techniques. In order to cope with the model-plant mismatch and also with disturbances (load changes), the internal model control (IMC) scheme (Garcia and Morari, 1982) is used. The resulting scheme is depicted in Figure 4.

Figure 4. Hammerstein model based predictive control scheme.

4.4. Optimization

{

}

In general, the GPC algorithm computes the control sequence Δu ( k + j ) , j = 1,..., Hc , such that the following quadratic cost function is minimized:

J ( H p1 , H p 2 , H c , λ ) =

H p2

∑

j = H p1

Hc

(ω ( k + j ) − yˆ ( k + j ) ) +λ ∑ Δu 2 ( k + j − 1) 2

j =1

(16)

Here, yˆ ( k + j ) denotes the predicted system output, ω ( k + j ) the modified setpoint that is assumed to be known in advance, H p1 is the minimum costing horizon, H p 2 is the maximum costing or prediction horizon, H c is the control horizon, and

λ

is the move

suppression coefficient.

5. COMPONENTS OF GAS SYSTEM Delivering the natural gas from a gas wellhead to end customers is comprised by a tremendous segment of the gas industry, which implicates gas wells, transmission and

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distribution pipelines, underground storages, compressors, and valves (Tobin, 2001; California Energy Comm., 2001). Gas Well: A gas well is usually located at sites which are far from load centers. Gas wells can be sorted into offshore and onshore. Transmission Pipelines: Transmission pipelines tackle the duty of transporting natural gas from wellheads or producers to local distribution companies or directly to large commercial and industrial users. Distribution Pipelines: Distribution pipelines generally allow the final link in the natural gas delivery chain. Distribution pipelines, which constitute the largest section in the natural gas system, deliver natural gas from city gate stations, underground storage facilities, and other gas supply sources to local industrial, as well as commercial and residential, customers. These pipelines work at a lower pressure level than transmission pipelines and offer different pressure services for different customers by adjusting the associated pressure regulators. for example, pipelines connected to gas-burning power plants require high-pressure services. Nevertheless, residential customers would need low-pressure gas for appliances. Underground Storage: Unlike electric power systems, which must uninterrupted monitor the entire system and adapt to changes instantaneously as electricity demand fluctuates, the gas industry can inject gas into certain underground storage facilities during off-peak periods for mitigating the high demand during peak hours and maintaining a steady flow through other pipelines when contingencies occur. Compressor: A compressor operates similar to step-up transformers in electric power systems. As gas is carried through a pipeline, its pressure would drop. Thus, the compressor must be an essential component in natural gas systems to maintain the desired pressure level in the transmission and distribution pipelines. Other compressors can be installed along pipelines (ordinarily at 50–100-mi intervals). The optimized location of compressors in pipeline planning could diminish the operation cost dramatically, improve the market competition, and assure a reliable gas supply to customers. Valve: A valve is a protective device which serves similar to breakers, fuses, and switches in electric power systems. It can insulate faulted sections and maintain the operation of other components in natural gas systems by holding a desired pressure level.

6. GAS STEADY-STATE EQUATIONS The steady-state flow of gas in a pipeline may be represented by equations that vary according to the gas working pressure and friction. These factors influence the gas flows that can vary from small values, in low-pressure distribution systems, to vary 1arge values, in high-pressure transportation systems. The effects of friction are difficult to measure and are the main reason for variations in the flow equations. The friction factor is not a constant for a given section of a pipeline and it is dependent on the roughness of the internal pipe surface, gas velocity, gas density, gas viscosity and the internal diameter of the pipe. After defining the gas operating conditions, the problem of static simulation is to estimate the values of pressure at the nodes and the flows in the individual pipes for known values of sources pressures and of gas consumption in the nodes. The pressures at the nodes and the

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flow in the pipes first satisfy the flow equation, and together with the values of loads and values of sources must accomplish the similar Kirchhoff’s laws for the electrical systems. The above general flow equation implies assumptions which are: 1. 2. 3. 4.

Isothermal flow due to insignificant temperature changes. Negligible kinetic energy change and constant compressibility across the pipe. Validity of the Darcy friction loss relationship across the pipe. Constant friction coefficient along the pipe length.

Under normal conditions, either an isothermal or an adiabatic approach is assumed. For the case of slow transients caused by fluctuations in demand, it is adopted that the gas in the pipe has sufficient time to reach thermal equilibrium with its constant-temperature environment. Likewise, when rapid transients were under consideration, it was assumed that the pressure changes occurred without any delay, allowing no time for heat transfer to take place between the gas in the pipe and the surroundings. Sometimes, this supposition of a process having a constant temperature or is adiabatic is not valid. The Darcy–Weisbach equation for the friction loss in pipes yields better precision than other equations such as Hazen–Williams because the friction factor it involves is determined as a function of both the relative pipe wall roughness and the Reynolds number. The others, like Hazen–Williams, Manning, and Scobey assume that the flow is in the rough pipe zone and neglect the effect of Reynolds number. Many methods of meshed gas flow simulation may be used, such as, the Newton-nodal method, Hard-Cross nodal method, Newton-loop method and Hard-Cross loop method. The Newton-loop method has a respectable convergence compared with the other ones (Gay, 1971; Gay and Preece, 1975; Cochran, 1996). Three basic types of entities are considered for the modeling of natural gas transmission network: pipelines, compressor stations, both of which are symbolized by branches, and interconnection points, represented by nodes (Osiadacz, 1989; Wong and Larson, 1968; Olorunntwo, 1981; Wu et al., 2000; Carter, 2001).

6.1. Flow Equation For isothermal gas flow in a long horizontal pipeline, say number k, which begins at node i and ends at node j, the general steady-state flow rate is often expressed by the following formula (Osiadacz, 1989) derived from energy balance:

f k = f kij = Sij × 6.18* 10 where

−6

T0 π0

Sij

(π

2 i

)

- π 2j Dk5

Fk GLk Tka Z a

(17)

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219

⎧⎪+1 if πi - π j > 0 ⎨ ⎪⎩ -1 if πi - π j < 0

Sij

In equation (1), the friction factor Fk depends on the flow region (laminar flow, mixed or transition flow, or fully turbulent flow), For fully turbulent flow (Reynolds number» 4000) region in a high-pressure network, Weymouth suggested that the friction factor Fk varies as a function of the diameter Dk ( Weymouth, 1942)

Fk =

0.1089 (18)

1

Dk 3

In terms of field units, equation (17) becomes

(

f k = f kij = 8.41* 10 −7 Sij M k Sij πi2 - π 2j

)

(19)

where 8

Mk = ε

1.22* 10 −10 T0 Dk 3 π0 GLk Tka Z a

As suggested in equation (19), the gas flow can be found once πi and π j are known for given conditions. Equation (19), known as Weymouth flow equation, is most acceptable for large diameter ( ≥ 0.254 m) lines with high pressures.

6.2. Compressor Modeling During transportation of gas in pipelines, the gas flow loses a part of its initial energy due to frictional resistance which results in a loss of pressure. To compensate the loss of energy and to move the gas, compressor stations are established in the network. The key factor to establish the representation of the centrifugal compressor is the horsepower consumption, which is a function of the amount of gas that flows through the compressor and the pressure ratio between the suction and the discharge. After empirical adjustment to describe for deviation from ideal gas behavior, the actual adiabatic compressor horsepower equation (Olorunntwo, 1981) at T0 = 15.5 °C (= 288.65 K) and π0 = 101.00819 kPa becomes

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Francisco Jurado

H k = H kij

⎡ ⎢⎛ π jc = 0.0155 Bk f k ⎢⎜ ⎜π ⎢⎣⎝ ic

⎞ ⎟⎟ ⎠

⎛ α −1 ⎞ Z ki ⎜ ⎟ ⎝ α ⎠

⎤ ⎥ − 1⎥ ⎥⎦

(20)

where

Bk =

1972.47Tki ⎛ α ⎞ ⎜ ⎟ ηk ⎝ α −1 ⎠

6.3. Conservation of Flow The mass-flow balance equation at each node can be expressed in a matrix form as

( A+U ) f

+ w − Tτ = 0

(21)

where if branch k enters node i, ⎧+1, ⎪ Aik = ⎨ -1, if branch k leaves node i, ⎪ 0, if branch k is not connected to node i. ⎩ ⎧+1, if the kth compressor has its outlet at node i, ⎪ U ik = ⎨ -1, if the kth compressor has its inlet at node i, ⎪ 0, otherwise. ⎩ ⎧+1, if the kth turbine gets gas from node i, Tik = ⎨ 0, otherwise. ⎩

The matrix A, known as the branch-nodal incidence matrix (Osiadacz, 1989), corresponds the interconnection of pipelines and nodes. In addition, it is defined the matrix U, which describes the connection of compressors and nodes. The vector of gas injections w is found by

w = wS − wL

(22)

Thus, a negative gas injection means that gas is taken out of the network. The matrix T and the vector τ represent where gas is withdrawn to power a gas turbine to operate the compressor. Therefore if a gas compressor, say k, between nodes i and j, is driven by a gasfired turbine, and the gas is tapped from the suction pipeline i, the following representation is obtained:

Tik = +1,

T jk = 0,

and τ k = amount tapped

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221

Conversely, if the gas were tapped at the compressor outlet, it would have Tik = 0,

T jk = +1,

and τ k = amount tapped

Analytically, τ k can be approximated as

τ k = αTk + βTk H kij + γ Tk H kij2

(23)

where H k = H kij is the horsepower demanded for the gas compressor k in equation (20).

6.4. Power Losses During transportation of gas in pipelines, the gas stream loses a part of its initial energy due to frictional resistance which results in a loss of pressure. The losses of gas distribution system can be indicated as

∑

NP k=1

(

f k πi - π j

)

(24)

where

N P branches in the system.

7. ELECTRIC POWER LOSSES Differences between natural gas and electricity systems are established as follows. • •

•

•

Electricity displaces at the speed of light, while natural gas travels 40–60 mi/h. Electricity is not a storable article of commerce. So the contingency-constrained network flow operation could forbid transmission systems from employing their maximum capacity. Consequently, the value of a transmission line may not necessarily be reflected in its current flow. The possibility to store gas in tanks and in pipelines palliates this problem for gas. Natural gas utilities typically trust on the natural gas storage to increase supplies flowing through the pipeline system and to meet the total natural gas demand. Economies of scale are very great in electric power transmission projects. It is much cheaper to install the required capacity of a transmission line initially than to retrofit the line later. However, gas pipelines are normally operated at a lower pressure and the pressure is elevated later to obtain additional capacity. Natural gas pipeline flows can be operated independent of the gas network constituents.

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Electric power systems may suffer significant losses. The losses depend on the line resistance and currents and are usually referred to as thermal losses. While the line resistances are fixed, the currents are a complex function of the system topology and the location of generation and load. Consider the well known power flow equations, with complex power Si = Pi + jQi , injected at bus i as (Grainger and Stevenson, 1994)

Pi = Vi ∑ j =1 YijV j cos (δ i − δ j − γ ij )

(25)

Qi = Vi ∑ j =1 YijV j sin (δ i − δ j − γ ij )

(26)

n

n

In this article, only the real power injections as they relate to electric losses are of concern. The system losses can be expressed as

PL = ∑ i =1 PGi −∑ i =1 PDi n

(27)

n

8. RESULTS 8.1. Identification of the Gas Turbine Model A second-order term is sufficient to model the static nonlinear behavior of the engine and the linear part is a second order transfer function. The noise v(t) is a random signal uniformly distributed and the input is

u ( t ) = A cos (ωi t ) ,

A = 1,

i = 1, 2,3

with ω1= 0.6, ω2=1.2, ω3=6 and Ti= 100((2π)/ωi). For the input frequency ωi, the sampling interval is set to be π/(50ωi). No lowpass filter is used in simulations, i.e., y(t) = yf(t). Thus, the estimates of fˆ (.) and Gˆ ( s ) are given by

f ( u ) = 0.002u2 + 0.816u + 3.458

G (s) =

0.066 ( s + 0.428 ) ( s + 0.932 )( s + 0.429 )

which are very close to the true but unknown f ( u ) and G ( s ) . The true (solid line) and the estimated (circle) nonlinearities are shown in Figure 5. They are basically indistinguishable.

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Figure 5. True (solid) and the estimated (circle) nonlinearities.

8.2. Model Based Predictive Control The gas turbine-generator is natural gas operated, with the rating of 1 MW. The systems are modeled dynamically (MATLAB®, 2007). The nominal operating conditions of the gas turbine and generator considered in this paper are given in Table 1 (Brown Energy Systems, 2007). Permanent magnet generator provides rapid response to varying loads and constant excitation under all conditions. The gearbox is created to ensure optimal performance. The control system displayed in Fig. 4 is applied to enable the control of speed of the gas turbine. A future control trajectory is generated as a possible solution by the optimizer based on the Hammerstein model using proposed method. At each sampling instant, only the first predicted input signal from the obtained control trajectory is applied to control the gas turbine. The load is the major disturbance affecting the gas turbine. The amount of mass flow can be controlled according to the load. Hammerstein MPC is employed to illustrate the performance of the gas turbine. The MATLAB implementation of quadratic programming is used (Coleman and Branch, 2004). The MPC parameters are selected according to the tuning rules given in (Soeterboek, 1992). Minimum prediction horizon H p1 is always set to the model time-delay d. There is no reason for choosing it smaller because the d-1 first predictions depend on past control inputs only and cannot be affected by the first action u(t). From another point of view, it is not recommended to select it bigger because this can lead to unpredictable results. For the gas turbine, it is set to l (sampling period) and not tuned. A rule of thumb is that the prediction horizon H p 2 should be taken close to the rise time of system (Clarke et al., 1987). Nevertheless, often it is not possible to choose it this long since the calculation time required by MPC is too demanding. Commonly it is tuned through empirical observation.

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Francisco Jurado Table 1. Operating point data

Power output Efficiency (LHV) Fuel Mass flow Turbine speed Exhaust temperature Permanent magnet generator Type Rated Capacity (kVA) Rated Capacity (kW) Speed Voltage Current Gearbox

GAS TURBINE 1000 kW 25 % Natural gas 0.07 kg/s 100 r/s 950 F Synchronous, brushless 1500 kVA, 3 phase 1200 kW, PF 0.8 30 r/s 480/460 V 1806/1884 A 100 r/s -30 r/s

From repeated tests on the gas turbine, H p 2 is set around 30 (sampling periods) for the best control performance for both small and large random step changes. The sample time is limited to 0.1 s. Soeterboek advises H c is equal to the number of output lag terms (Soeterboek, 1992). If

H c is made longer, the control performance is slightly ameliorated and the calculating time is also increased. Based to the simulation results, it is set to 2 (sampling periods), which is the same as the number of output lag terms. The purpose of the move suppression coefficient λ is to punish large changes in the process input and reduce actuator wear. It is usual to set λ as a constant in the range [0, 1]. For the gas turbine, to achieve the best control performance, it is set to 0.05. With the MPC variables set to H p1 =1, H p 2 =30, H c =2 and λ =0.05, the controller results in the system responses are shown in Figures 6 and 7. The responses are for large random step changes. The results depict that a fast rise rime is attained, with almost no overshoot, evidencing proposed method offers a near optimal performance for both small and large random step changes. This result illustrates the capabilities of the MPC controller to track a reference trajectory.

8.3. Simulation Results The IEEE 13 node test feeder (Kersting, 2001) is a 4.16 kV short and highly loaded feeder. Also, its overhead and underground lines, shunt capacitor banks and spot and distributed loads provide a useful distribution system model.

Gas Turbines and Electric Distribution System

Figure 6. Performance of MPC on several large random set point changes. Comparison between rotational speed and speed reference.

Figure 7. Performance of MPC on several large random set point changes. Mass flow for set point changes.

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Francisco Jurado

This node test feeder is used as the test system to investigate the dynamic characteristics of the distribution system with two gas turbines and the effectiveness of the MPC on the stability of distribution system. Figure 8 shows this test system.

Figure 8. One line diagram of IEEE 13 node feeder with gas turbines.

Figure 9. Rotor speed deviation of GT1.

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227

The two gas turbines are connected at nodes 652 and 634, the initial active power of gas turbine GT1 is 1 p.u., and initial active power of gas turbine GT2 is 0.5 p.u. To investigate the dynamic behavior of the gas turbines under large disturbance conditions, a three-phase fault is applied on node 671 for 400 ms. At t = 0 s, the fault occurs at node 671 and the fault is cleared at t= 0.4 s. Figures 9 and 10 show the responses of rotor speed of the gas turbines to the three-phase fault when the MPC is applied in the governor-gas turbine system. GT1 has a greater oscillation than that of the GT2. This is because the initial power of GT1 is higher than that of GT2. The simulation results demonstrate that MPC can effectively damp the oscillation of both gas turbines, thus the MPC can ameliorate the dynamic characteristic of the whole distribution system.

Figure 10. Rotor speed deviation of GT2.

8.4. Distribution Systems A series of comparisons between gas and electricity systems has been performed. It is of no use to consider a generic possible substitution of gas pipelines with electric lines: pipelines are clearly the basic solution for multipurpose gas utilization (electricity generation, industrial and domestic uses, petrochemicals, etc.), particularly when very large gas flow rates are involved. The same reliability/availability assumptions for the delivered electricity have been taken into account for both gas and electricity systems. This hypothesis is secure on the basis of the actual performance of both gas and electricity systems.

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Francisco Jurado

The IEEE 37-bus test system shown in Figure 11, which can be considered as a distribution system, is applied to verify the method presented. A gas turbine is integrated into electric distribution network. While a gas well is incorporates into the gas distribution system. The electric power losses are obtained from the results of power flow studies using MATLAB®. Figure 12 shows the electric power losses. In Figure 13 the power losses are reported as a function of the node (bus) and pipeline in the gas distribution system. The simulations have been developed by NEPLAN®. The total power loss of the electricity distribution system reaches 34 kW, while the total power loss of the gas distribution system is only 2.131 kW. Lengths of electric lines and equivalent gas pipelines are reported in Table 2.

Figure 11. IEEE 37- bus test system.

16000 14000 12000 10000 8000 6000 4000 2000 0 L1-2 L2-3 L3-4 L4-5 L5-6 L6-7 L7-8 L8-9 L9-10 L10-11 L11-12 L12-13 L12-26 L9-25 L25-35 L25-36 L7-24 L6-23 L6-22 L3-14 L14-15 L15-16 L16-33 L33-34 L16-30 L30-32 L15-28 L28-29 L30-31 L3-17 L17-18 L17-27 L4-19 L19-20 L20-21 L20-37

Power loss (W)

IEEE-37 Bus

Line

Figure 12. Power losses of the IEEE 37-bus test system. Electricity distribution system.

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IEEE-37 Bus

Power loss (W)

300 250 200 150 100 50 0 1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

31

33

35

Location-Bus Nº

Power loss (W)

IEEE- 37 Bus 800 600 400 200 0

Pipeline

Figure 13. Power losses of the IEEE 37-bus test system. Gas distribution system.

Table 2. Lengths of electric lines and gas pipelines. IEEE 37-bus test system Pipeline L-1-2 L-2-3 L-3-4 L-4-5 L-5-6 L-6-7 L-7-8 L-8-9 L-9-10 L-10-11 L-11-12 L-12-13 L-12-26 L-9-25 L-25-35 L-25-36 L-7-24 L-6-23 L-6-22 L-3-14

Length (m) 643,8 334 459 208 69,6 111,3 111,3 195 222,7 139 139 139,2 69,6 181 69,6 445 111,3 0,001 208 125

37

230

Francisco Jurado Table 2. (Continued)

Pipeline L-14-15 L-15-16 L-16-33 L-33-34 L-16-30 L-30-32 L-15-28 L-28-29 L-30-31 L-3-17 L-17-18 L-17-27 L-4-19 L-19-20 L-20-21 L-20-37

Length (m) 181 27,8 208,8 97,4 320,1 41,7 27,8 180,9 264,5 139 111,3 83,5 83,5 97,4 97,4 69,6

Figure 14 shows the next system studied. It represents a six-bus 25 kV distribution network with lines ranging in length from 16 to 32 km. Node 1 is the reference bus. It is assumed that nodes 1 and 2 are connected to gas turbines. They are voltage controlled buses.

Figure 14. IEEE 6- bus test system.

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Figure 15 displays the electric power loss and Figure 16 the power loss in the gas distribution system. The total power loss of the electricity system extends to 495 kW, whereas the total power loss of the gas system reaches 126.6 kW. Lengths of electric lines and equivalent gas pipelines are described in Table 3. The computation of losses is illustrated by means of Tables 4 and 5. IEEE-6 Bus

Power loss (kW)

200 150 100 50

L1-6

L1-5

L3-4

L4-5

L5-6

L2-1

L2-3

0

Line

Figure 15. Power losses of the IEEE 6-bus test system. Electricity distribution system.

IEEE- 6 Bus

Power-Loss (kW)

100 80 60 40 20 0 3

2

1

6

5

4

Location-Bus Nº

IEEE-6 Bus

Power loss (kW)

50 40 30 20 10

Pipeline

Figure 16. Power losses of the IEEE 6-bus test system. Gas distribution system.

L-1-6

L-1-5

L-3-4

L-4-5

L-5-6

L-1-2

L-2-3

0

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Francisco Jurado Table 3. Lengths of electric lines and gas pipelines. IEEE 6-bus test system Pipeline L-1-2 L-2-3 L-3-4 L-4-5 L-5-6 L-1-6 L-1-5

Length (km) 16 16 16 16 16 17,6 32

Table 4. Lengths of electric lines and gas pipelines. IEEE 30-bus test system Pipeline L1-2 L1-3 L2-4 L2-6 L2-5 L4-6 L3-4 L5-7 L8-28 L6-28 L6-8 L7-6 L12-14 L12-15 L12-16 L14-15 L16-17 L15-18 L18-19 L19-20 L10-20 L10-17 L10-21 L10-22 L21-22 L15-23 L23-24 L22-24 L24-25 L25-27 L27-29 L27-30 L29-30

Length (km) 71,5 91,9 132,8 204,3 204,3 102,2 51,2 26,6 204,3 204,3 106,2 30,6 61,3 34,7 28,6 34,7 53,1 32,7 36,8 16,3 53,1 38,8 46,9 40,8 18,4 40,9 51,1 47,0 20,4 30,6 30,6 61,3 57,2

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Table 4. (Continued) Pipeline L25-26 L6-9 L6-10 L4-12 L27-28 L9-10

Length (km) 51,1 0 0 0 0 0

Table 5. Computation of losses. Electricity distribution system

L1-2 L1-6 L1-5 L2-1 L2-3 L3-2 L3-4 L4-3 L4-5 L5-4 L5-6 L5-1 L6-5 L6-1 Losses (kW)

Line flow (kW) 15 8.140 5.620 -15 7.913 -7.760 3.754 -3.718 -3.518 3.550 -2.988 -5.524 3.009 -7.983 495

The power losses depend on the ambient temperature as formulated in Sections 6 and 7. Figure 17 displays the power losses for different temperatures in this gas distribution system.

Figure 17. Power losses for different temperatures. Gas distribution system.

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Francisco Jurado

8.5. Subtransmission System The proposed method is tested on the IEEE 30-bus test system shown in Figure 18, which can be considered as a meshed subtransmission system. The system has 30 buses (mainly 132- and 33-kV buses) and 41 lines. Six gas turbines are considered to be connected to the system.

Figure 18. IEEE 30- bus test system.

Gas compressors stations constitute a major part of the operational plant on each transmission system. Their purpose is to restore the gas pressure reduction induced by frictional pressure losses. The compressors are driven mostly by gas turbines which use natural gas as fuel, taken directly from the transmission pipelines. The compressor unit comprises three main components, a gas generator, a power turbine and a centrifugal gas compressor. The maximum shaft powers of the units range from 5.5 MW to more than 20 MW. At each compressor station, there are installed between two and three centrifugal compressors, driven by gas turbines. In this paper, the compressors are installed at 60 mi intervals. Figure 19 displays the electric power loss and Figure 20 the power loss in the gas distribution system. The total power loss of the electricity system corresponds to 17.86 MW,

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235

however the total power loss of the gas system represents 202.34 MW. Lengths of electric lines and equivalent gas pipelines are depicted in Table 6. IEEE-30 Bus

5 4 3 2 1 0 L1-2 L5-7 L7-6 L6-8 L27-28 L27-29 L29-30 L27-30 L27-25 L25-26 L1-3 L3-4 L4-6 L4-12 L14-15 L12-14 L16-17 L12-16 L24-22 L23-24 L15-23 L21-22 L10-21 L22-10 L20-10 L19-20 L6-10 L2-4 L12-13 L9-10 L9-11 L6-9 L18-19 L15-18 L12-15 L24-25 L17-10 L2-6 L2-5 L8-28 L6-28

Power loss (MW)

6

Line

Figure 19. Power losses of the IEEE 30-bus test system. Electricity distribution system.

50 0

Power loss (MW)

100

IEEE-30 Bus

1

3

5

7

9

11

13

15

17

19

21

23

25

27

29

Location-Bus Nº IEEE-30 Bus

Pip eline

Figure 20. Power losses of the IEEE 30-bus test system. Gas distribution system.

It is quite clear that in distribution systems, due to better efficiency of gas system, the losses are larger in case of electricity system. For the shortest lengths here considered (IEEE 37-bus test system) the losses are the lowest ones. For the longest lengths (IEEE 30-bus test system), the losses are larger in case of gas system. Also for the shorter distances the gas pipeline feeding a local power plant in the consumption area is an interesting and attractive option to be considered with respect to a distribution of electricity.

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N-1 N-2 N-3 N-4 N-5 N-6

fk (m3/s) 0 0 0.345 0.625 0.431 0.431

Δπ (mbar) 0 0 653.62 2255.12 724.23 3.7705

Losses (kW) 0 0 13.529934 84.6421707 18.7454865 9.75931083 126.676902

CONCLUSIONS In this article, a Hammerstein model of a gas turbine plant and its MPC has been presented. The model is suitable for use in power system stability studies. A MPC is designed for the gas turbine in order to improve system dynamic performance. The proposed model was tested on a simple distribution system. The simulation results with and without MPC are compared. It was observed that the proposed model with MPC improves the dynamic performance of the system. Due to the continuous developments of gas turbines, combined cycle power plants allow a very high efficiency, low emissions and very attractive investments cost. This and environmental concerns are enhancing gas consumption for electric power generation. The performed comparison between gas and electricity systems, of interest when natural gas is needed for electricity generation, highlights that in the investigated cases the power losses are larger in case of electricity transmission over short distances than in case of gas transport and electricity generation close to final users. However for longer distances the use of electric transmission systems is an attractive option to a gas pipeline feeding a power plant located into the consumption area.

REFERENCES Botros K.K., Campbell P.J., Mah D.B., 1991. Dynamic simulation of compressor station operation including centrifugal compressor and gas turbine. Journal of Engineering for Gas Turbines and Power-Transactions of the ASME, Vol. 113, No. 2, pp. 300–311. Botros K.K., 1994. Transient phenomena in compressor stations during surge. Journal of Engineering for Gas Turbines and Power-Transactions of the ASME, Vol. 116, No. 1, pp. 133–142. Brown Energy Systems, 2007. New 1 MW Multi-Fuel Gas Turbine Generator. Available: http://www.brownmarine.com/tg01.htm. California Energy Comm., 2001. Natural gas infrastructure issues, Sacramento, CA. Carter R., Goodreau M., Rachford H., 2001. Optimizing pipeline operations through mathematical advances. Pipeline and Gas Journal, Vol. 228, No. 10, pp. 51-53.

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Chiras N., Evans C., Rees D., 2002. Global nonlinear modeling of gas turbine dynamics using NARMAX structures. ASME Journal of Engineering and Power, Vol. 124, No. 4, pp. 817-826. Clarke D.W., Mothadi C., Tuffs P.S., 1987. Generalized predictive control. Part I. The basic algorithm, Automatica, Vol. 23, No. 2, pp. 137-148. Cochran T.W., 1996. Calculate pipeline flow of compressible fluids. Chemical Engineering, Vol. 103, No. 2, pp. 115-122. Cohen H., Rogers G.F.C., Saravanamuttoo, H.I.H., 1998. Gas turbine theory. 4th ed. Longman, England. Coleman T., Branch M.A., Grace A., 2004. Optimization Toolbox, Version 2.2. The MathWorks, Inc., Natick, MA. DOE, 2001 a. Annual energy outlook 2002 with projections to 2020. Energy Inf. Admin. (EIA). DOE, 2001 b. U.S. natural gas markets: Recent trends and prospects for the future. Energy Inf. Admin. (EIA). Evans C., Rees D., Borrell A., 2000. Identification of aircraft gas turbine dynamics using frequency-domain techniques. Control Engineering Practice, Vol. 8, No. 4, pp. 457-467. Fawke A.J., Saravanamuttoo H.I.H., Holmes M., 1972. Experimental verification of a digital computer simulation method for predicting gas turbine dynamic behaviour. Institution of Mechanical Engineers Proc. Vol. 186, No. 27, pp. 323–329. Garcia C.E., Morari M., 1982. Internal model control: 1. A unifying review and some new results. Ind. Eng. Chem. Process Design and Development, Vol. 21, No. 2, pp. 308-323. Garrard D., 1996. ATEC: The aerodynamic turbine engine code for the analysis of transient and dynamic gas turbine engine system operations, part 1: Model development. ASME paper 96-GT-193. Gay B., 1971. Middleton P. Solution of gas network problems. Chemical Engineering Science, Vol. 26, No. 1, pp. 109-123. Gay B., Preece P.E., 1975. Matrix methods for the solution of fluid network problems. Trans. of the Institution of Chemical Engineers, Vol. 53, No.1, pp. 12-15. Grainger J.J., Stevenson Jr. W.D., 1994. Power system ana1ysis. New York: McGraw-Hill, Inc. Hung W.W., 1991. Dynamic simulation of gas-turbine generating unit. IEE Proc.-C Generation Transmission and Distribution, Vol. 138, No. 4, pp. 342-350. Hussain A., Seifi H., 1992. Dynamic modeling of a single shaft gas turbine. Proc. of the IFAC Symposium on Control of Power Plants and Power Systems, Munich, Germany, Pergamon Press, pp. 43-48. Jurado F., Cano A., 2004. Use of ARX algorithms for modelling micro-turbines on the distribution feeder. IEE Proceedings Generation Transmission and Distribution, Vol. 151, No. 2, pp. 232-238. Jurado F., Carpio J., 2005. Enhancing the distribution networks stability using distributed generation. The International Journal for Computation and Mathematics in Electrical and Electronic Engineering (COMPEL), Vol. 24, No. 1, pp. 107-126. Jurado F., 2005. Modelling micro-turbines using Hammerstein models. International Journal of Energy Research, Vol. 29, No. 9, pp. 841-855. Kersting W.H., 2001. Radial distribution test feeders. Proc. IEEE/PES Summer Meeting, Vol. 2, pp. 908 –912.

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Maciejowski J.M., 2002. Predictive Control with Constraints. Prentice Hall, London. MATLAB®, 2007. Version 7. The Mathworks Inc., Natick, MA. Narendra K., Gallman P., 1996. An iterative method for the identification of nonlinear systems using a Hammerstein model. IEEE Trans. Automatic Control, Vol. 11, No. 3, pp. 546–550. NEPLAN®, 2005. Power Systems Engineering, Erlenbach, Switzerland. Olorunntwo F.O., 1981. Natural gas transmission system optimization. PhD thesis, The University of Texas at Austin. Osiadacz A.J., 1989. Simulation and Analysis of Gas Network. Houston: Gulf Publishing Company. Qin S.J., Badgwell T.A., 2000. An overview of nonlinear predictive control applications, in Allgöwer, F., and Zheng, A. (Eds.), Nonlinear Model Predictive Control. ser. Progress in Systems and Control Theory, MA: Birkhäuser, Vol. 26, pp. 369–392. Richalet J., 1993. Industrial applications of model based predictive control. Automatica, Vol. 29, No. 5, pp. 1251–1274. Rowen W.I., 1983. Simplified Mathematical Representations of Heavy-Duty Gas Turbines. ASME Journal of Engineering for Power, Vol. 105, No. 4, pp. 865-869. Rowen W.J., 1988. Speedtronic Mark IV control system. Alsthom Gas Turbine Reference Library, AGTR 880. Schobeiri M.T., Attia M., Lippke C., 1994. GETRAN: A generic, modularly structured computer code for simulation of dynamic behavior of aero- and power generation gas turbine engines. Journal of Engineering for Gas Turbines and Power-Transactions of the ASME, Vol. 116, No. 3, pp. 483–494. Sharma C., 1998. Modeling of an Island Grid. IEEE Trans. Power Systems, Vol. 13, No. 3, pp. 971-978. Soeterboek A.R.M., 1992. Predictive Control; A Unified Approach. Prentice-Hall, Upper Saddle River, NJ. Tobin J., 2001. Natural gas transportation-infrastructure issues and operational trends. Energy Inf. Admin. (EIA)/Natural Gas Div. van Essen H.A., de Lange H.C., 2001. Nonlinear model predictive control experiments on a laboratory gas turbine installation. Journal of Engineering for Gas Turbines and PowerTransactions of the ASME, Vol. 123, No. 2, pp. 347-352. Vroemen B.G., van Essen H.A., van Steenhoven A.A., Kok J.J., 1999. Nonlinear Model Predictive Control of a Laboratory Gas Turbine Installation. Journal of Engineering for Gas Turbines and Power-Transactions of the ASME, Vol. 121, No. 4, pp. 629–634. Weymouth T.R., 1942, Problems in natural gas engineering. ASME Trans., Vol. 34, pp. 185234. Willis H.L., Scott W.G., 2000. Distributed Power Generation: Planning and Evaluation. Marcel Dekker, New York. Wong P.J., Larson R.E., 1968. Optimization of natural-gas pipeline systems via dynamic programming. IEEE Trans. Automatic Control, Vol. 13, No. 5, pp. 475-481. Wu S., Rios-Mercado R.Z., Boyd E.A., Scott L.R., 2000. Model relaxations for the fuel cost minimization of steady-state gas pipeline networks. Mathematical and Computer Modelling, Vol. 31, No. 2-3, pp. 197-220.

In: Leading-Edge Electric Power Research Editor: C. M. O’Sullivan, pp. 239-263

ISBN: 978-1-60021-981-8 © 2008 Nova Science Publishers, Inc.

Chapter 5

MICRO CCHP: FUTURE RESIDENTIAL ENERGY CENTER R. Z. Wang and D. W. Wu Institute of Refrigeration and Cryogenics, Shanghai Jiao Tong University, China

ABSTRACT Combined cooling, heating and power (CCHP) system, as a distributed energy system, can work all the year and provide cooling/hot-water/power in summer, heating/hot-water/power in winter and hot-water/power in other seasons. In CCHP systems, the total energy efficiency increases to over 85%, while the average energy efficiency of conventional fossil fuel fired electricity generation systems is around 40%. The energy efficiency promotion of CCHP systems results in emission reduction compared to the conventional methods of generating heat and electricity separately. And as a distributed energy resource, CCHP systems also increase in the reliability of the energy supply. With the overall development of CCHP systems and related technologies, the utilization of micro CCHP systems in the residential sector is emerged as a growing potential. The article focuses on the micro CCHP systems for single-family applications (around 10 kW) and multi-family or residential district applications (under 200 kW). The status quo of micro CCHP systems is briefly presented and diverse combinations of technologies existing in applications or experimental units are listed through comprehensive literature review. Various technologies available or under development are introduced, such as reciprocating internal combustion engine, micro-turbine, fuel cell, Stirling engine, absorption chiller, adsorption chiller and so on. Afterward, the tendency and issues of micro CCHP systems are discussed. The review shows that micro-CCHP applications are entering into average families as a next-generation residential energy supply center.

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INTRODUCTION The conventional way to cover electricity, heating and cooling demands is to purchase electricity from the local grid, generate heat by burning fuel in a boiler and obtain space cooling power from diverse air-conditions. But in a CCHP system, byproduct heat that can be up to eighty percent of total primary energy in combustion-based electricity generation is recycled for different uses. CCHP, generally, is defined as combined production of electrical and useful thermal energy from the same primary energy source [1]. In some literatures, CCHP systems are also named as Tri-generation and BCHP (Building Cooling Heating and Power) systems.

Figure 1. Energy Flow of Traditional mode.

Figure 2. Energy Flow of CCHP System.

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Recent development of CCHP systems, to large extent, is related to the emergence of DER (Distributed/Decentralized Energy Resources) - a novel technical concept in the region of energy supply. DER is defined as an electricity generation system located in or near users, providing electrical and thermal energy synchronously to meet the demands of local users in priority. The CCHP systems discussed in this article is in the category of DER, and they are different from traditional CHP systems, which, mostly, are large-scale centralized power plants without cooling power generated. Distributed CCHP systems share some advantages [2,3,4,5] compared to traditional energy supplies, along with their developing tendency and promising prospect. First of all, overall energy efficiency is dramatically improved, ranging from 70% to more than 90% compared to up to 40-45% of typical centralized power plants. With the primary energy saving, vast cost reductions including fuel costs, transmission and distribution savings can be achieved. Secondly, environment benefits derive from emission reduction. This benefit can be viewed from two aspects sorted by different kinds of prime movers. Some prime movers with new technologies like fuel cells, micro-turbines do expel much less emissions including NOx, CO2 than the traditional technologies taken by centralized power plants do. However, other prime movers equipped in CCHP systems with smaller capacity as their same kinds of large counterpoints in centralized power plants, emit a bit more amount of NOx and CO2 per kW electricity generated. Nevertheless, energy efficiency promotion of CCHP systems should be taken into account at this time. Burning much less fuel to meet same demands results significant emission reduction, which surely exceeds the impact of emission augment caused by slight decrease of small-scale prime mover convert efficiency. Last but not the least, CCHP systems increase in the reliability of the energy supply. Obviously, generation/distribution system malfunctions, terrible weather and terrorism are fatal threatens leading to disruptions of centralized power plants. A smaller, more flexible and dispersed system as distributed CCHP unit is possible to avoid these threatens being realities, and then limited influences and fast recovery could be achieved if these situations unluckily happened. A study following the 11th September attacks suggested that a system based more on distributed generation plants may be five times less sensitive to systematic attack than a centralized power system [6]. Distributed CCHP systems are suitable for various industrial, institutional, commercial and residential applications, and the capacities of these systems range widely from less than 1 kW in domestic dwellings to more than 1 MW in some industries or university campuses. Since last decade, many CCHP applications have been set up in hospitals, university campuses, commercial complexes, hotels, clubs, leisure centers, office buildings and residential districts. With the progress of some critical technologies such as fuel cells and micro-turbines, more and more literatures focus on small, even micro level CCHP systems recently, which are especially for single- family (1-5kW) or multi-family residential buildings and small residential districts, ranging from 5kW up to 200kW.

MICRO RESIDENTIAL CCHP SYSTEMS Different prime movers with heat recovery equipments and further connecting with different cooling or dehumidification options can result in various kinds of CCHP systems,

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but only several modes of combination are adopted in recent commercial market, other promising possibilities still remain in laboratory to overcome their disadvantages in technology or economy. Reciprocating engines, micro-turbines, electrical chillers and absorption chillers are currently predominant for the maturity and stability of these technologies. Reciprocating internal combustion engines plus absorption or electrical (engine driven) chillers are popular for small scale utilizations. Jacket cooling fluids, lubricating oil systems, and engine exhaust are three heat recovery options which can produce hot water using exchangers, for heat demands and other cooling and dehumidification usages, seen in Figure 3. This kind of configuration has existed for quite a few years, and there are many applications ranging form 100kW up to 1MW in industrial, institutional, commercial sectors. But distributed CCHP applications with engines below 100kW for residential dwellings can be seldom found until recent years. In 2001, M.A. Smith [8] analyzes a micro CCHP system with an engine and a heat pump in his articles. The rated capacity of the engine is as low as 1.5kW and with the help of a heat pump the heat generated is around 4.5kW. Miguez [9, 10] also illustrates design and performance of a CCHP system with engine (9.6kW) and heat pump equipment. In 2004, a micro CCHP system at Shanghai Jiao Tong University with novel adsorption chiller generating cooling power, which uses heat recovered from a gas engine of 12kW rated capacity, was experimented [11]. Commercial compact micro CCHP products also can be found in US and Europe now. The ‘ecopower’ micro CCHP unit of Marathon Engine Co. fueled by natural gas or propane gas, generates 2-5kW electricity and up to 13.8kW thermal power at the max. temperature of 75 . Typical applications of these units are single and multi-family homes [12].

Figure 3. Schematic of reciprocating engine heat recovery [6, 7].

Recently, a household size CCHP unit based on a small-scale diesel engine generator set (9.5kW) is reported [13]. An absorption refrigerator driven by the exhaust heat from the

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engine is as the cooling option of this system. It is point out in the paper that the CO2 emission per unit (kWh) of useful energy output from this micro CCHP system is dramatically reduced compared to that of conventional generations.

Figure 4. Schematic diagram of micro-turbine [6, 7].

Another popular prime mover, micro-turbines are classified into two categories: combustion turbine and Rankine cycle turbine according to literatures. Capstone Turbine Co. is the leading competitor in the field of micro combustion turbines. Current production microturbines range in net power output from 30 to 250kW. Their low maintenance and clean exhaust make them a reliable choice for base load CCHP applications. Integrating hot water heat recovery into the micro-turbine package has proven cost effective, and a growing number of commercial installations are saving money using this technology [14]. Absorption chillers and desiccant dehumidifiers driven by recovery heat of micro combustion turbines are employed to meet cooling demands of users. This configuration of CCHP systems is applied in many locations, especially in the US, where turbine-based units have become serious competitors with engine-based units in the CCHP market. But micro turbine unit is not applied separately for single dwellings. In most cases, several modularized minimal units of 30kW can be combined together to fit user’s electricity profiles, while they still share flexibility in operation. The capacity of most applications with micro combustion turbines, especially in hotels, hospitals and university campus buildings, are beyond the micro level. One obvious drawback of this technology is prominent high initial investment cost, which prevents its popularities in residential sector. Average residential building owners maybe cannot afford to this advanced technology. In the other category, some micro Rankine cycle turbines combined with solar collection are reported. Both W. Yagoub [15] and S.B. Riffat [16] introduced a solar energy-gas driven micro-CHP system. Solar energy collector of 25kW thermal capacity, supplemented by a condensing gas boiler, was used to drive a 1.5 kW Rankine cycle micro-turbine generator

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unit. The literatures of micro CCHP units with Rankine cycle micro-turbine are limited. This type of turbine is not a popular choice in the residential building field till now.

Figure 5. STM 4-120 power unit packaged DG system [17].

Among the newly emerged prime mover technologies, the Stirling engine is viewed as a promising prime mover in small commercial and residential applications for their low emissions, fewer moving parts, low noise, small-scale availability and relatively low byproduct heat. The Stirling engine CCHP systems are also suitable for modular installation as micro combustion turbines. And due to temperature limitations of the engine components, heat recovery from coolant systems account for almost 50% of the heat input. This results in a significant amount of heat suitable for space heating, cooking, potable hot water, and low temperature processes. The possible cooling and dehumidification options for Stirling engines are absorption chillers, dehumidifiers and adsorption chillers. There has also been research on the feasibility of CCHP driven by Stirling engines [18]. Currently, only a few commercial Stirling engine units can be found in the CCHP market. The most active company developing Stirling engine distributed generation technology is STM Power, Inc. STM has conducted field tests of this application with their 25kW model, STM 4-120 [17], which is the first commercialized Stirling engine in the world. The new prototype, STM’s 55kW engine, can produce 92kW, while a 3kW engine will produce 6kW. Other companies in this industry reported systems under development that range from 55 watts to 3,000 watts. Sigma co. is developing a 3 kW electrical output and 9 kW thermal output engine for a single-family dwelling. The electrical efficiency of the unit is reported to be 25% [19]. And SOLO, a German Company has developed Stirling engine CHP unit fueled by natural gas. The unit generates electrical power of 2–9 kW and thermal power of 8–24 kW and has an overall efficiency of 92–96% [20]. In Canada, a project was initiated to integrate a prototype micro CCHP unit into a residential house that would provide electricity and heat to the house, and supply surplus electricity back to the grid [21]. This Stirling engine based micro CCHP unit, fuelled by natural gas, had an electrical output of 736W and a thermal output of 6.5kW. In

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general, Stirling engine is an ideal technology for residential micro CCHP systems, but it also has the disadvantage as micro turbine has, its high capital cost. Though still on the brink of market entry, fuel cells are the focus of interest as the prime mover technology for micro CCHP systems to serve a variety of residential buildings in the future. Fuel cells are electrochemical devices that convert the energy of a chemical reaction directly into electricity and heat. They are similar in principle to primary batteries except that the fuel and oxidant are supplied to the cell, rather than stored internally. They are silent in operation, modular without moving parts, which are perfect merits for residential sector, but there is limited experience to validate potential applications. Moreover, fuel cells micro CCHP systems carry high capital costs and higher project risk due to unproven durability and reliability. At the beginning of this century, several first prototype systems were reported by utility companies, heating appliance manufacturers and RandD centers in Europe, Japan, and the US. After that, further steps towards industrialization and product development were reported. Yasuhiro Hamada et al. tested the performance of a 1kW polymer electrolyte fuel cell as a residential energy system, of which the electrical efficiency and heat recovery efficiency are 42.5% and 49.2% respectively [22]. Afterward, Tokyo Gas Co., Ltd. marketed the first domestic polymer electrolyte fuel cell with 1kW capacity and 31% generation efficiency in 2005 [23]. G. Gigliucci et al. [24] introduced a PEM fuel cell CHP system supplied by HPower in Italy. The system converts natural gas into electricity and heat, at nominal conditions, 4kW of electric power and 6.8kW of thermal power: the former is delivered to local loads using electric load following capability; the latter is delivered to the experimental area hydraulic refrigeration circuit. S. Giddey et al. [25] describes the design and assembly of a 1kW PEM stack tested, analysis of the results and problems encountered during operation. The electrical efficiency of the stack varies from 39 to 41%. The recoverable combined heat and power efficiency of the stack is 65% without external thermal insulation and 80% with external thermal insulation. A PEMFC system is studied by Charles-Emile Hubert [26]. Five units were installed from November 2002 to May 2003 and have been operated in real life conditions. They deliver up to 4kW of AC power and about 6kW of heat. P. Koenig et al. [27] analyzes a prototype PEMFC CHP system for decentralized energy supply in domestic applications. The complete system supplies 2kW electricity and approximately 4 kW heating power at 60 for domestic hot water and space heating. The tests include steady state measurements under different electrical and thermal loads as well as an analysis of the dynamic behavior of the system during load changes. While polymer electrolyte fuel cell keeps developing, solid oxide fuel cell remains one of the most promising options for distributed CCHP applications, with the prospect for incredibly high electrical generation efficiency. Several companies are keen on developing SOFC technology for the residential CCHP market since 1995. Fuel Cells Bulletin [28] reports that a Home Energy Centre, provided by the Baxi Group, supplied all the heat and electricity for a new four-bedroom house in Scotland. The Home Energy Centre is based around a PEM fuel cell combined with a natural gas reformer. It provides 1.5kW of electricity and 18kW of heat, sufficient for all of an average domestic property’s heat requirements and up to 75% of its electrical power needs, all year round. At the same time, some other researchers try to make SOFC applications more suitable for modern family dwellings. A.D. Hawkes et al. [29] point out that SOFC-based micro CCHP applications have a low heat-topower ratio and may benefit from avoidance of thermal cycling.

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Figure 6. A solid polymer fuel cell system [29].

They also find that these units suit to slow space heating demands, where the heating system is on constantly during virtually all of the winter period. R.J. Braun et al. [30] evaluate five different SOFC system designs in terms of their energetic performance and suitability for meeting residential thermal-to-electric ratios. Effective system concepts and key performance parameters are identified. The results indicate that maximum efficiency is achieved when cathode and anode gas recirculation is used along with internal reforming of methane. System electric efficiencies of 45% and combined heat and power efficiencies of 88% are described. In the near future, fuel cell based residential micro-CCHP systems will compete with traditional energy supplies. Some literatures assess energy utilization, emission aspect or economic feasibility of this type of micro CCHP in the residential sector. V. Dorer [31] establishes a methodology for assessing the performance of SOFC and PEMFC systems in terms of primary energy demand and the CO2 emissions by transient computer simulations. Adam Hawkes [32] explores the performance of a hypothetical SOFC system under UK market conditions at that time. He indicates that the optimized result of a household SOFC micro CHP system depends on system size, energy import prices, electricity export price, stack capital costs or an improvement in stack life time. Another financial analysis of SOFC units is carried out by Kari Alanne et al. [33], to evaluate the sensitivity of the maximum allowable capital cost with respect to system sizing, acceptable payback period, energy price and the electricity buyback strategy of an energy utility. Based on this financial analysis, micro (1–2kW) SOFC systems seem to be feasible in the considered case.

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STATUS AND DEVELOPMENT OF MICRO CCHP TECHNOLOGIES A typical CCHP system consists of five basic elements: prime mover; electricity generator; heat recovery system; thermally activated equipment (or other cooling options) and management and control system. Among them, prime movers obviously play a critical role; they are the keystones of CCHP systems and, to some extent, they determine possibilities and availability of other related technologies. (Seen in Table 2). Although steam turbine and combustion turbine are two crucial technologies for centralized CHP plants, there is almost no small capacity product in the residential level. For micro CCHP systems, the options of prime movers can be reciprocating internal combustion engine, micro-turbine, stirling engine and fuel cell. All the options can be selected by users to meet dissimilar demands and limitations from site to site: especially local heat and electricity profiles, regional emissions and noises regulations and installation place restrictions. Thermally activated equipment is another essential part of a CCHP system to provide cooling or dehumidification. Commercialized thermally activated technologies include absorption chillers and desiccant dehumidifiers. Moreover, novel adsorption chillers approaching commercial stage can be another choice for micro CCHP systems. Some existed systems also apply electric chillers or engine-driven chillers integrated with prime movers to fulfill cooling demands, which combined with thermally activated technologies to be called cooling options of CCHP system in some literatures.

Prime Movers Reciprocating Internal Combustion Engines [1,6,7,17,34-36] Two types of internal combustion engines are currently in use; spark ignition engines, which are operated mainly with natural gas; and compression ignition engines, which can use diesel fuel, as well as other petroleum products, such as heavy fuel oil or biodiesel. In addition to fast start–up capability and good operating reliability, relatively high efficiency at partial load operation gives users a flexible power source. Reciprocating engines are by far the most commonly used power generation equipment under 1 MW. Although they are a mature technology, reciprocating engines have obvious drawbacks. Relatively high vibrations require shock absorption and shielding measures to reduce acoustic noise. A large number of moving parts with frequent maintenance intervals, increase maintenance costs and strongly offset fuel efficiency advantages. Moreover, high emissions, -particularly nitrogen oxides-- are the underlying aspect of this technology and need to be improved. Major manufacturers around the world continuously develop new engines with lower emissions; at the same time, emissions control options, such as selective catalytic reduction (SCR), have been utilized to reduce emissions. Micro-Turbines [1,7,17, 34,35,37] Micro-turbines extend combustion turbine technology to smaller scales. They are primarily fuelled with natural gas, but they can also operate with diesel, gasoline or other similar high-energy fuels. Research on biogas is ongoing. Micro-turbines have only one

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moving part; they use air bearings and do not need lubricating oil, although they have extremely high rotational speed, up to 120000 rpm. A striking characteristic is their flexibility that small-scale individual units can be combined readily into large systems of multiple units. Additionally, there are environmental advantages, such as low combustion temperatures assuring low NOx emissions levels and less noise than an engine of comparable size. This technology has been commercialized for only a few years and is offered by a small number of suppliers. The main disadvantage at this stage is its high costs compared with engine. Other flaws include relatively low electrical efficiency and sensitivity of efficiency to changes in ambient conditions.

Stirling Engines [1,17,34,35] Compared to conventional internal combustion engines, Stirling engines have an external combustion device in which the cycle medium--generally helium or hydrogen--is not exchanged during each cycle, but remains within the cycle while the energy driving the cycle is applied externally. Stirling engines can operate on almost any fuel (gasoline, alcohol, natural gas or butane), with the external combustion that facilitates the control of the combustion process and results in low air emissions, low noise and more efficient process. In addition, fewer moving parts compared to conventional engines limit wear on components and reduce vibration levels. Stirling engine is still in its development. High cost also prevents popularization of this technology. Nevertheless, the promising prospects of stirling engines stimulate further research, especially for CCHP applications. Small size and quiet operation mean that they will integrate well into residential or portable applications. Some literature indicates the possibility of using a solar dish to heat the Stirling engine, thus eliminating the need for combustion of a fuel. Fuel Cells [1,6,7,34,35,37,38] Fuel cells are quiet, compact power generators without moving parts, which use hydrogen and oxygen to make electricity; at the same time, provide heat for a wide range of applications. In general, fuel cells show high electrical efficiencies under varying load, and which results in low emissions. Besides transportation sector, power generation is another promising market. Five major fuel cell technologies listed below have the most attractive prospects. A detail comparison of the characteristics of these fuel cells appears in Table 1. Proton Exchange Membrane Fuel Cell (PEMFC) Also known as Polymer Electrolyte Membrane Fuel Cell, PEM fuel cells are quite simple and can be made very small to adjust to variable power demands. They are easier to start up and they apply solid electrolyte that reduces corrosion. At the same time, the low operating temperature requires the use of an expensive platinum catalyst, which limits the cogeneration potential. As for the fuel sources, this fuel cell technology is highly sensitive to fuel impurities and hydrogen storage; delivery and reforming technology has yet to evolve. With relatively low quality heat, PEM fuel cell is unlikely to be widely used for high voltage stationary power generation; but small–scale domestic CCHP applications --the simplest thermal load of which is hot water-- would be considerable.

Table 1. Characteristics of fuel cells [17,34,35] PEMFC

AFC

PAFC

MCFC

SOFC

Charge Carrier Type of Electrolyte

H+ ions Polymeric membrane

H+ ions Phosphoric acid solutions

CO3= ions Phosphoric acid (Immobilized liquid)

Typical Construction

Plastic, metal or carbon

OH- ions Aqueous potassium hydroxide soaked in a matrix Plastic, metal

Carbon, porous ceramics

Catalyst Oxidant

Platinum Air or O2

Platinum Purified Air or O2

Fuel

Hydrocarbons or methanol

Operational Temperature Size Range Electrical Efficiency Primary Contaminants

50-100°C 3-250kW 30-50% CO, Sulfur, and NH3

Clean hydrogen or hydrazine 60-80°C 10-200kW 32-70% CO, CO2, and Sulfur

Platinum Air or Oxygen- Enriched Air Hydrocarbons or alcohols 100-200°C 100-200kW 40-55% CO>1%, Sulfur

High temp metals, porous ceramic Nickel Air

O= ions Stabilized zirconia ceramic matrix with free oxide ions Ceramic, high temp metals Parasites Air

Clean hydrogen, nature gas, propane, diesel 600-700°C 250kW-5MW 55-57% Sulfur

Natural gas or propane 600-1000°C 100kW-10MW 50-60% Sulfur

Table 2. Characteristics and parameters of prime movers in CCHP systems [1,17,34,35,37,47,48] Diesel engines Capacity range

*

Micro-turbines

String Engines

Fuel Cells

15-300kW Gas, Propane, Distillate Oils, Biogas

1kW-1.5MW Any (Gas, Alcohol, Butane, Biogas)

Efficiency electrical (%)

35-45

25-43

15-30

~ 40

5kW-2MW Hydrogen and fuels containing hydrocarbons 37-60

Efficiency overall (%)

65-90

70-92

60-85

65-85

85-90

Power to Heat Ratio

0.8-2.4

0.5-0.7

1.2-1.7

1.2-1.7

0.8-1.1

Output heat temperature (℃)

*

*

200-350**

60-200

260-370

Noise

Loud

Loud

Fair

Fair

Quiet

CO2 emissions (kg/ MWh)

650

500-620

720

672***

430-490

NOx emissions (kg/ MWh)

10

0.2-1.0

0.1

0.23****

0.005-0.01

Availability (%)

95

95

98

N/A

90-95

Part load performance

Good

Good

Fair

Good

Good

Life cycle (year)

20

20

10

10

10-20

Average cost investment ($/kW)

340-1000

800-1600

900-1500

1300-2000

2500-3500

Operating and maintenances costs ($/kWh)

0.0075-0.015

0.0075-0.015

0.01-0.02

N/A

0.007-0.05

Fuel used

5kW-20MW Gas, Propane, Distillate Oils, Biogas

Spark ignition engines 3kW-6MW Gas, Biogas, Liquid Fuels, Propane

Up to a third of the fuel energy is available in the exhaust at temperatures from 370-540ºC; other rejected heat is low temperature, often too low for most processes. (Jacket cooling water at 80 to 95ºC, lube oil cooling at 70ºC and intercooler heat rejection at 60ºC, all difficult to use in CHP). ** 650ºC without recuperator. *** Stirling Engine Emission Characteristics / STM 4–260. Gas-Fired Distributed Energy Resource Technology Characterizations.

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Alkaline Fuel Cell (AFC) Alkaline fuel cells are the first fuel cells used on spacecrafts and space shuttles. The technology shares obvious merits, such as low operating temperature, rapid start–up time, readily available non-precious metal electrodes, and high efficiency, up to 70%. However, the primary disadvantage is the tendency to absorb carbon dioxide, converting the alkaline electrolyte to an aqueous carbonate electrolyte that is less conductive. Thus, the fuel input should be restricted to pure hydrogen, which limits applications to those in which pure hydrogen are available. If the CO2 is removed from fuel and oxygen streams, the operating costs are much greater. Although the attractiveness of AFC has declined substantially with the interest and improvements in PEMFC technology, recent developers still believe that it can be used for many applications, such as stationary power generation, but also mobile applications including both marine and road vehicles. Phosphoric Acid Fuel Cell (PAFC) Phosphoric acid fuel cells are the most mature of the technologies in commercial production, although its costs remain uncompetitive with other non-fuel cell technologies. Hydrogen is still the ultimate fuel for the reaction in the phosphoric acid fuel cell, but various fuels, including natural gas, LPG and methanol, can be used as raw input converted by a reformer. Other advantages are its resistance to fuel impurities, and the ability to use a less expensive catalyst. The drawbacks of this fuel cell include a lower efficiency than other fuel cell technologies and corrosive liquid electrolyte. In the near future, with lower operating temperatures, PAFC would be ideal for small and mid–size power plants, replacing large electrical generators and other types of CCHP utilities in hospitals, hotels and airports. Molten Carbonate Fuel Cells (MCFC) A molten carbonate fuel cell uses a molten carbonate salt mixture as its electrolyte. The composition of the electrolyte varies, but usually consists of lithium carbonate and potassium carbonate, which is chemically aggressive and puts strain on the stability and wear of the cell components. As a result, MCFC is more expensive than either SOFC or PEMFC in terms of capital cost. Fuel reforming of MCFC occurs inside the stack and tolerates impurities; therefore, this technology may use a variety of fuels. In addition, the high operating temperature allows for combined heat and power generation and high fuel–to–electricity efficiency. Nevertheless, the long start–up time to reach operating temperatures, and poorer flexibility in output, make MCFC ideally suited to base load power generation where continuous operation is necessary, such as heavy industries and national electrical grid networks. Solid Oxide Fuel Cell (SOFC) Due to all–solid–state ceramic construction, solid oxide fuel cells share important characteristics, such as stability and reliability. A variety of hydrocarbon fuels can be used, like gasoline, methanol and natural gas. As another asset, the high operating temperature makes internal reforming possible and removes the need for a catalyst, which also produces high grade waste heat suited well to CCHP applications. But the high temperature also creates some difficulties: expensive alloys for components are required, quit a long time is needed for the electrolyte to heat. Start-up time is less of an issue for stationary and continuous

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applications. They generally achieve around 60% efficiency in a 5MW plant, compared to around 30% for a traditional gas turbine. The last critical problem that prevents its commercialization is the comparatively high costs of SOFC.

Thermally–Activated Technologies An important difference between CCHP systems and conventional cogenerations is that CCHP systems –including some cooling or dehumidification components– provide not only electricity and heating but also cooling capacity for space or process. These cooling or dehumidification options can employ advanced thermally–activated technologies as well as traditional technologies. But recent research indicates that thermally–activated technologies are favored, as the overall efficiency of CCHP systems is promoted with applications of these thermally–activated technologies. In addition to high efficiency, other benefits such as low emissions and cost reduction are also achieved with thermally–activated technologies. Major thermally–activated technologies include absorption chillers, adsorption chillers and desiccant dehumidifiers. These cooling and dehumidification systems can be driven by steam, hot water or hot exhaust gas derived from prime movers. However, waste heat from various prime movers falls into different temperature ranges; at the same time, cooling and dehumidification systems have their own suitable working temperature. As a result, optimal matching of recoverable energy streams with thermally driven technologies is shown in Table 3. Table 3. Recoverable energy qualities with matching technologies [39] Power Source Solid Oxide Fuel Cell Micro-turbine Phosphoric Acid Fuel Cell

Temp.

Stirling Engine

~ 90°C

IC Engine

~ 80°C

PEM Fuel Cell

~ 60°C

~ 480°C ~ 320°C ~ 120°C

Matching Technology Triple-effect/ Double-effect absorption Triple-effect/ Double-effect absorption Double-effect/ Single-effect absorption Single-effect absorption, adsorption or dehumidification Single-effect absorption, adsorption or dehumidification Single-effect absorption, adsorption or dehumidification

Absorption Chillers [7,39,40,41] Absorption chillers are one of the commercialized thermally–activated technologies widely applied in CCHP systems; they are similar to vapor compression chillers, with a few key differences. The basic difference is that a vapor compression chiller uses a rotating device to raise the pressure of refrigerant vapors, while an absorption chiller uses heat to compress the refrigerant vapors to a high–pressure. Therefore, this “thermal compressor” has no moving parts. Depending on how many times the heat supply is utilized; absorption chillers can be divided into single–effect, double–effect and triple–effect. The parameters and traits of different absorption chillers can be viewed in Table 4.

Table 4. Characteristics of absorption technologies [49] System

Operating Temp. ( ) Heat source Cooling

Working Fluid

Cooling Capacity (ton)

COP

Current Status

Single effect cycle

80–110

5–10

LiBr/ water

10-1500

0.50.7

Large water chiller

Single effect cycle

120–150

150

High–temperature waste heat

Space cooling, refrigeration

Solar energy, low–temperature waste heat

Low temperature, ice making

Solar energy, low–temperature waste heat

Low temperature, ice making

95

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The most common working fluids for absorption chillers are water/NH3 and LiBr/water, although there are 40 refrigerant compounds and 200 absorbent compounds available in theory [49]. Lithium–bromide/water absorption chillers play a predominant role in the absorption chiller market in Asia-Pacific countries like China, Japan, Korea, and in the US. In contrast, ammonia/water absorptions chillers are more popular in Europe.

Adsorption Chillers [42-45] Adsorption–cooling technology is a novel, environmentally–friendly and effective means of using low–grade heat sources. Unlike conventional vapor compression systems which require a mechanical compressor assembly, this new technology uses a thermally–driven static sorption bed, saving as much as 90% of the required input power typically used to drive a mechanical compressor. The system takes advantage of the ability of certain sorbent material, stored in a sorption bed, to soak up a relatively large quantity of refrigerant vapor at some low temperature and pressure. At this stage, cooling capacity is achieved in the evaporator because of the evaporation of the refrigerant. The refrigerant is subsequently released to the condenser at a higher pressure simply by applying heat to the sorbent bed. To increase the efficiency and provide continuous cooling, more than one sorption bed is often used. A heat regeneration fluid also can be used to increase system efficiency by transferring heat from a hot to a cold bed. As a critical part of this technology, the characteristics of various adsorbent–adsorbate working pairs are listed in the Table 5. Since there are no moving parts, except for valves, the sorption system is considerably simpler, requiring no lubrication and thus, little maintenance. Other advantages include quiet operation and modularity so it is readily scalable for increased heating and cooling capacity by additional beds. Furthermore, any heat source, such as waste heat or renewable energy, can be used, so energy saving can be potentially significant. Desiccant Dehumidifiers [3,40,41] Desiccant dehumidifiers can work in concert with sorption chillers or conventional air conditioning systems to significantly increase overall system energy efficiency by avoiding overcooling air and precluding oversized capacity to meet dehumidification loads. The desiccant process involves exposing the desiccant material (such as silica gel, activated alumina, lithium chloride salt or molecular sieves) to a moisture-laden process air stream, retaining the moisture of the air in desiccant and regenerating desiccant material via a heated air stream. System capacity is often expressed in volume of airflow or in moisture removal rate. Table 6 shows some specifications. Table 6. Performance of desiccant dehumidification systems [41] Flux (m3/min)

Thermal Input (W/m3/min)

Maximum Latent Removal (W/m3/min)

40-140 140-280 280+

300-1000 300-1000 300-1000

300-600 300-600 300-600

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Dehumidification technology is divided into two major types, solid desiccant dehumidifiers and liquid desiccant dehumidifiers; both are useful for the mitigation of indoor environmental quality and security problems and for humidity control in buildings. Dehumidification technology in the commercial sector remains a young technology with a premium price. Yet, commercial desiccant technologies have not been designed for integration into CCHP systems.

Other Options Although thermally–activated technologies indicate the trend in cooling and dehumidification options in CCHP systems, electric vapor-compression refrigeration systems still play an important role for their maturity and reliability. Therefore, quite a few CCHP systems in research and practical utilization still employ these conventional technologies as their cooling options. Nonetheless, it is unwise for a CCHP system to drive chillers using electricity generated by prime movers, since smaller prime movers have lower efficiency than larger types used in power plants. Engine–driven chillers emerge to substitute electric chillers in CCHP units, avoiding the losses in energy conversion. Engine–driven chillers, including reciprocating types, centrifugal types and screw types, are conventional chillers driven by an engine, in lieu of an electric motor. An advantage of engine–driven chillers is better variable speed performance, which improves partial–load efficiency. Engine–driven chillers can also operate in a CCHP system for hot water loads when the waste heat produced by the engine is recovered.

RESEARCH TENDENCY OF MICRO RESIDENTIAL CCHP Development of distributed CCHP systems has been undergoing for almost two decades, while many related technologies have been employed and ameliorated in this field through comprehensive researches. Micro residential CCHP systems for single- or multi-family buildings as new applications emerge in recent years. The technologies used in distributed CCHP systems are miniaturized and improved to better fit for micro residential utilizations [50,51,52,53]. Besides that, some novel technologies are also invented as new alternatives. Except for detail improvement or invention in specific technologies, new design methods for the whole micro CCHP systems are introduced in some literatures. Andrew Wright et al. [54] describe exploratory analyses of domestic electricity-profiles recorded at a high time resolution of 1 min on eight houses, while most load data are available at half-hour intervals. It is included that for dwellings with micro CCHP, a better understanding of electricity profiles is important for the economic analysis of systems, and to examine the effects of widespread onsite generation on local electricity-networks. In Adam Hawkes’s paper [55], it is also indicated that coarse temporal precision profiles of 1-h demand blocks in heat and power demands become questionable for applications where demand exhibits substantial volatility such as for a single residential dwelling—an important potential market for the commercialization of small-scale fuel cells. Total CO2 emissions reduction is overestimated by up to 40% by the analyses completed using coarse demand data for a given micro CCHP

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unit. The economic difference is also significant at up to 8% of lifetime costs. H. Lund [56] presents the Danish experience with methodologies and software tools, which have been used to design investment and operation strategies for almost all small CCHP plants in Denmark during the decade of the triple tariff. Moreover, the changes in such methodologies and tools in order to optimize performance in a market with fluctuating electricity prices are discussed. For existed CCHP systems, various criteria are employed to compare different systems and obtain the best configuration of a typical application. Some simulation models are also presented in some papers to optimize operating performance, system configuration and control strategy. Aiying Rong [57] illustrates a long-term planning model based on hourly load forecasts to obtain cost-efficient operation of a CCHP system. This linear programming model with a joint characteristic for three energy components minimizes simultaneously the production and purchase costs of three energy components, as well as CO2 emissions costs. S.M. Ameli [58] presents the works which have been done and yielded results about the requirements of developing integrated distributed energy evaluation software. The comprehensive software package is for designing, optimizing and monitoring of distributed energy systems based on micro-turbine, fuel cell and internal combustion engine driven systems. A.D. Peacock et al. [59] employs a 50 dwelling data set of heat and power demands to investigate the implementation of various penetrations of micro CHP system on the resultant electrical load profile using two control methodologies: heat-led and a proposed method for modulating the aggregate electrical load. And they point out that further improvements in the modulating capability of this control approach may be realized if prime movers capable of rapid start-up, shut-down and cycling can be developed. A.D. Hawkes et al. [60] investigate cost effective operating strategies for three micro CCHP technologies; Stirling engine, gas engine, and solid oxide fuel cell. In this paper, central estimates of price parameters are used, which is shown that the least cost operating strategy for the three technologies is to follow heat and electricity load during winter months, rather than using either heat demand or electricity demand as the only dispatch signal. Least cost operating strategy varies between technologies in summer months. In another paper, A.D. Hawkes et al. [61] develop a techno-economic modeling of a solid oxide fuel cell stack for micro CHP system. Some literatures of internal combustion engine for residential sector also can be found. Hycienth I. Onovwiona et al. [62] present a parametric model that can be used in the design and techno-economic evaluation of internal combustion engine based CCHP systems for residential use. The model, which is suitable to provide system performance information in response to a building’s electrical and thermal demands, and is capable of simulating the performance of these systems in 15-min time steps. After focusing on the design, assessment and simulation of typical novel micro residential CCHP systems, some researchers begin to consider the future scenario of micro CCHP systems in residential sector, the relationship between distributed micro residential CCHP systems with centralized power plants and comparison in emission, economy and social cost–benefit between them. Samuel Bernstein [63], H. Lund [64], Ineke S.M. Meijer [65], Jeremy Cockroft [66], discuss various aspect of the development situation of residential CCHP applications in US, Lithuania, Netherlands and UK, while Neil Strachan [67] and Francesco Gulli [68] provide a brand-new view point toward distributed CCHP systems including micro residential CCHP units, which deserves to investigate further.

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CONCLUSION Micro residential CCHP systems share some important advantages with distributed CCHP systems: high overall energy efficiency ranging from 70% to more than 90%; less prime energy consumption; emission reduction and increase in the reliability of the energy supply. The electricity, heating and cooling demands of a family can be covered by micro CCHP systems simultaneously and independently, which is similar to larger systems, although larger systems have already well proven in quite a few applications of industrial, institutional and commercial buildings. A review of the current applications, demonstrations and experimental systems for residential single- or multi-family buildings has been presented. The prime movers applied in these systems include small traditional reciprocating internal combustion engine as well as micro-turbines, novel Stirling engine and fuel cells, especially PEMFC and SOFC. These technologies are suitable for residential applications to meet electricity demands. Although most systems listed are just cover the electricity and heat demands, some applications are also combined with certain thermally activated cooling options, such as small absorption chiller or adsorption chiller, to meet the space cooling demands in summer. In general, micro residential CCHP systems include both these two types of family energy supply centers. The review of micro CCHP applications illustrate that such kind of energy supply for families develops rapidly in recent years and many of them are in the edge of market entry. A further review analyzes the technologies used in micro residential systems comprehensively. Generally speaking, reciprocating engine based micro CCHP systems are the most realistic and reliable product for single- or multi- family buildings in current market. Micro turbine based systems are also feasible in technology, but too expensive to be afforded. Stirling engine based systems become an important competitor in the market, because of their versatility in fuel and other advantages compare with internal combustion engines. However, several obstacles need to be surmounted, especially high initial investment cost. In long views, fuel cell based micro CCHP systems are the most promising technologies. SOFC and PEMFC systems are in the beginning stage of commercialization. With technology improvement and large scale production, the capital costs of fuel cells will reduce rapidly in next decade. It is also critical to develop micro scale thermal initialized cooling technologies for single-family dwellings. Most demonstrations and applications of micro CCHP systems are actually only micro CHP (combined heating and power) systems. But in many places of the world, cooling demand of a family dwelling in summer is even larger than heating demand in winter. Consequently, micro scale absorption chiller, adsorption chiller and desiccant dehumidifiers are also an important part of future residential energy center. Research issues indicate the future tendency and direction of micro residential CCHP systems. Development of specific CCHP technologies is always the most critical research content, which is to find ways to overcome drawbacks of certain technology, pursuit higher energy efficiency, diminish emissions, reduce the capital cost and follow users’ electricity and heat profiles more precisely and economically. At the same time, new design methods for the whole micro CCHP system are necessary to be built up. While basic equipment technologies are available, a core thought is needed to optimize CCHP configuration, control strategies and economical operation. Besides that, various criteria are discussed to better estimate and compare different existed micro CCHP systems, which also stimulate and assist the

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development of the core designing thought. Similarly, simulation models of typical technologies or the whole systems also help to optimize operating performance, system configuration and control strategy. Diverse research results of the future scenario of micro residential CCHP systems indicate that distributed micro residential CCHP system is, to some extent, a controversial energy supply mode. The relationship between distributed micro residential CCHP systems with centralized power plants is necessitated to be further compared in energy, emission, economy, environment and social benefit aspects. Despite of some discussion, it is believed that micro residential CCHP systems would be the next generation of family energy supply center in decades.

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[48] Decentralized Generation Technologies - Potentials, Success Factors and Impacts in the Liberalized EU Energy Markets, Final Report. COGEN Europe (www.cogen.org). (October 2002). [49] A Review of Absorption Refrigeration Technologies. Renewable and Sustainable Energy Reviews, 5, 343-372. Pongsid Srikhirin, Satha Aphornratana, Supachart Chungpaibulpatana. (2001). [50] Absorption chiller crystallization control strategies for integrated cooling heating and power systems. International Journal of Refrigeration 1-8, (Article in press). Xiaohong Liao, Reinhard Radermacher. (2007). [51] The influence of feedstock drying on the performance and economics of a biomass gasier–engine CHP system. Biomass and Bioenergy, 22, 271 – 281. J.G. Brammer, A.V. Bridgwater. (2002). [52] The influence of operating temperature on the efficiency of a combined heat and power fuel cell plant. Journal of Power Sources, 122, 37–46. S.F. Au, S.J. McPhail, N. Woudstra, K. Hemmes. (2003). [53] Thermodynamic and economic performance of the LiBr–H2O single stage absorption water chiller. Applied Thermal Engineering, 26, 2103–2109. Tomasz M. Mroz. (2006). [54] The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations. Applied Energy, 84, 389–403. Andrew Wright, Steven Firth. (2007). [55] Impacts of temporal precision in optimisation modeling of micro-Combined Heat and Power. Energy, 30, 1759–1779. Adam Hawkes, Matthew Leach. (2005). [56] Optimal designs of small CHP plants in a market with fluctuating electricity prices. Energy Conversion and Management, 46, 893–904. H. Lund, A.N. Andersen. (2005). [57] An efficient linear programming model and optimization algorithm for trigeneration. Applied Energy, 82, 40–63. Aiying Rong, Risto Lahdelma. (2005). [58] Integrated distributed energy evaluation software (IDEAS) Simulation of a microturbine based CHP system. Applied Thermal Engineering, (Article in press). S.M. Ameli, B. Agnew, I. Potts. (2005). [59] Controlling micro-CHP systems to modulate electrical load profiles. Energy, 32, 1093– 1103. A.D. Peacock, M. Newborough. (2007). [60] Cost-effective operating strategy for residential micro-combined heat and power. Energy, 32, 711–723. A.D. Hawkes, M.A. Leach. (2007). [61] Techno-economic modelling of a solid oxide fuel cell stack for micro combined heat and power. Journal of Power Sources, 156, 321–333. A.D. Hawkes, P. Aguiar, C.A. Hernandez-Aramburo, M.A. Leach, N.P. Brandon, T.C. Green, C.S. Adjiman. (2006). [62] Modeling of internal combustion engine based cogeneration systems for residential applications. Applied Thermal Engineering, 27, 848–861. Hycienth I. Onovwiona, V. Ismet Ugursal, Alan S. Fung. (2007) [63] MICRO-CHP: U.S. market potential and complex challenges. www.energyint.com. Samuel Bernstein. (2004). [64] Implementation strategy for small CHP-plants in a competitive market: the case of Lithuania. Applied Energy, 82, 214–227. H. Lund, G. Siupsinskas, V. Martinaitis. (2005).

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In: Leading-Edge Electric Power Research Editor: C. M. O’Sullivan, pp. 265-284

ISBN: 978-1-60021-981-8 © 2008 Nova Science Publishers, Inc.

Chapter 6

SENSITIVITY CALCULATION IN REAL TIME TRANSMISSION NETWORK AND ENERGY MARKETS Jizhong Zhu∗ AREVA T & D Corporation 10865 Willows Rd. NE Redmond, WA 98052, USA

ABSTRACT The calculations of the several sensitivities such as loss sensitivity, voltage sensitivity, generator constraint shift factor, and area based constraint shift factor become very important in energy management system (EMS) and energy markets. This chapter focuses on the analysis and implementation details of the above-mentioned sensitivities calculations in the practical transmission network and energy markets. The power operator uses them to study and monitor market and system behavior and detect possible problems in the operation. These sensitivities calculations are also used to determine whether the on-line capacity as indicated in the resource plan is located in the right place on the network to serve the forecasted demand. If the congestion or violation exists, the generation scheduling based on the sensitivities calculations can determine whether or not a different allocation of the available resources could resolve the congestion or violation problem. This chapter also comprehensively discusses how to compute and use the sensitivities under the different references such as the market-based reference, and the energy management system based reference. The calculation results of the several sensitivities are illustrated using the IEEE 14 bus system and AREVA T & D 60-bus system.

∗

E-mail: [email protected]

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Jizhong Zhu

I. INTRODUCTION The electric power industry is being relentlessly pressured by governments, politicians, large industries, and investors to privatize, restructure, and deregulate. Despite the changes with different structures, market rules, and uncertainties, an energy management system (EMS) control center must always be in place to maintain the security, reliability, and quality of electric service [1]. It means that EMS in the open energy market must respond quickly, reliably and efficiently to the market changes. In order to achieve the optimal objective in energy markets, the calculations of the several sensitivities such as loss sensitivity, voltage sensitivity, generator constraint shift factor, and area based constraint shift factor become very important. This chapter focuses on the analysis and implementation details of the above-mentioned sensitivities calculations in the practical transmission network and energy markets. The power operator uses them to study and monitor market and system behavior and detect possible problems in the operation. These sensitivities calculations are also used to determine whether the on-line capacity as indicated in the resource plan is located in the right place on the network to serve the forecasted demand. If the congestion or violation exists, the generation scheduling based on the sensitivities calculations can determine whether or not a different allocation of the available resources could resolve the congestion or violation problem. In the early energy market, the transmission losses are neglected for reasons of computational simplicity, but are recently addressed in the Standard Market Design (SMD) [2-4]. The loss calculation is considered for the dispatch functions of SMD such as locationbased marginal prices (LMP). Loss allocation does not affect generation levels or power flows; however it does modify the value of LMP [5]. The early and classic loss calculation approach is the loss formula – B coefficient method [6], which is replaced by the more accurate inverse Jacobian transpose method [7]. Numerous of loss calculation methods have been proposed in the literature and can be categorized into pro-rata [8], incremental [9], proportional-sharing [10], and Z-bus loss allocation [11]. The calculation of loss sensitivity is based on the distributed slack buses in the energy control center [6, 11-13]. In the real-time energy markets, LMP or economic dispatch is implemented based on market-based reference, which is an arbitrary slack bus, instead of the distributed slack buses in the traditional energy management system. Meanwhile, the existing loss calculation methods in traditional EMS systems are generally based on the generator slacks or references. Since the units with automatic generation control (AGC) are selected as the distributed slacks, and the patterns or status of AGC units are variable for the different time periods in the real time energy market, the sensitivity values will keep changing, which complicates the issue. This chapter presents a fast and useful formula to calculate loss sensitivity for any slack bus [14]. The simultaneous feasibility test (SFT) performs the network sensitivity analysis under the base case and contingency cases in the power system. The base case and post-contingency MW flows are compared against their respective limits to generate the set of critical constraints. For each critical constraint, SFT calculates constraint coefficients (shift factors) that represent linearized sensitivity factors between the constrained quantity (e.g. MW branch flow) and MW injections at network buses. The B-matrix used to calculate the shift factors is constructed to reflect proper network topology.

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267

The objective of SFT is to identify whether or not network operation is feasible for a real power injection scenario. If operational limits are violated, generic constraints are generated that can be used to prevent the violation if presented with the same network conditions. In the energy market systems, the trade is often considered between the source and the sink (i.e., the point of resource, POR and point of demand, POD). The source and the sink may be an area or any bus group. Therefore, the area based sensitivities are needed, which can be computed through the constraint shift factors within area. Voltage sensitivity analysis can detect the weak buses/nodes in the power system where the voltage is low. It can be used to select the optimal locations of VAR support service [1520]. According to the sensitivity values – voltage benefit factor (VBF) and loss benefit factor (LBF), a ranking of VAR support sites can also be obtained. This chapter presents the implementation details of the several sensitivities calculations in the practical transmission network and energy markets. Section 2 describes the calculation of the market-based loss sensitivities. Section 3 describes the implementation of SFT and the calculation of the constraint’s shift factors. Section 4 describes the calculation of the voltage sensitivity. Section 5 shows the simulation results of the above-mentioned sensitivities.

II. LOSS SENSITIVITY CALCULATION This section presents a fast and useful formula to calculate loss sensitivity for any slack bus. The formula is based on the loss sensitivity results from the distributed slacks without computing a new set of sensitivity factors through the traditional power flow calculation. Especially, the loads are selected as the distributed slacks rather than the usual generator slacks. The loss sensitivity values will be the same for the same network topology no matter how the status of the AGC units changes. In the energy market, the formulation of the optimum economic dispatch can be represented as follow:

Min F = ∑ C j Pj

j ∈ NG

(1)

j ∈ NG

(2)

j

such that

s.t.

∑P

∑S

D

ij

+ PL = ∑ PGj

Pj ≤ Pi max

j

j ∈ NG , i ∈ K max

(3)

j

PGj min ≤ PGj ≤ PGj max where PD: The real power load.

j ∈ NG

(4)

268

Jizhong Zhu Pimax: PGj: PGjmin: PGjmax: PL: Sij: Cj : Kmax: NG:

The maximum requirement of power supply at the active constraint i. The real power output at generator bus j. The minimal real power output at generator j. The maximal real power output at generator j. The network losses. The sensitivity (shift factor) for resource or unit j and active constraint i with respect to the market-based reference. The real time price for the resource (or unit) j. The maximum number of active constraints. The number of units.

The Lagrangian function is obtained from equations (1) and (2).

FL = ∑ f i ( PDi ) + λ (∑ PDi + PL − ∑ PGj ) i

i

(5)

j

Traditionally, generation reference (single or distributed slack) is used in the calculation of loss allocation. This works, but may be inconvenient or confusing for the users who frequently use the loss factors. The reason is that the AGC status or patterns of units are variable in the real time EMS or energy markets. The loss sensitivity values based on the distributed unit references will keep changing due to the change of unit AGC status. Thus, the distributed load slack or reference is used here. The optimality criteria of the Lagrangian function (5) are written as follow:

⎛ ∂FL ∂P ⎞ df = i + λ ⎜⎜1 + L ⎟⎟ = 0 i ∈ ND ∂PDi dPDi ⎝ ∂PDi ⎠ ⎛ ∂P ⎞ ∂FL df = i + λ ⎜ L − 1⎟ = 0 j ∈ NG ⎜ ∂P ⎟ ∂PGj dPGj ⎝ Gj ⎠ df i LDi = λ i ∈ ND dPDi

LDi = −

1 ∂P 1+ L ∂PDi

dfi LGj = λ dPGj

(6)

(7)

(8)

i ∈ ND

(9)

j ∈ NG

(10)

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

LGj =

1 ∂P 1− L ∂PGj

j ∈ NG

269

(11)

where, λ:

the Lagrangian multiplier.

∂PL : ∂PDi

the loss sensitivity with respect to load at bus i.

∂PL : ∂PGj

the loss sensitivity with respect to unit at bus j.

We use both

∂PL , which is the loss sensitivity with respect to an injection at bus i, stand for ∂Pi

∂PL ∂PL and . Since the distributed slack buses are used here, all loss sensitivity ∂PDi ∂PGj

factors are non-zero. If an arbitrary slack bus, k, is selected, then Pk is the function of the other injections, i.e.

Pk = f ( Pi )

i ∈ n, i ≠ k

(12)

where n is the total number of buses in the system, and Pi is the power injection at bus i, which includes the load PDi and generation PGj. Actually, the load can be treated as a negative generation. Then equation (9) and (11) can be expressed as (13), and equation (8) and (10) can be expressed as (14).

Li =

1 ∂P 1− L ∂Pi

i∈n

df i Li = λ dPi

i∈n

(13)

(14)

Equation (2) will be rewritten as

PL = Pk + ∑ Pi i≠k

i∈n

(15)

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Jizhong Zhu

The new Lagrangian function can be obtained from (1) and (15).

FL* = ∑ f i ( Pi ) + λ ( PL − Pk − ∑ Pi )

(16)

i≠n

i

The optimality criteria can be obtained from the Lagrangian function (16).

⎛ ∂P ∂P ⎞ ∂FL* df i df k ∂Pk + λ ⎜⎜ L − k − 1⎟⎟ = 0 i∈n, i≠k = + ∂Pi dPi dPk ∂Pi ⎝ ∂Pi ∂Pi ⎠

(17)

From (15), we get

∂PL ∂P =1+ k ∂Pi ∂Pi

(18)

From (17) and (18), we get

df i * df k Li = dPk dPi L*i =

1 ∂P 1− L ∂Pi

(19)

i ∈ n, i ≠ k

(20)

It is noted that Li and Li* are similar, but they have different meaning [14]. The former is based on the distributed slack buses, and the latter is based on an arbitrary slack bus k. Similarly, the loss sensitivity in Li is based on the distributed slack, i.e.

∂PL ∂Pi

(The DS

subscript DS means the distributed slack); the loss sensitivity in Li* is based on an arbitrary single slack bus k, i.e.

∂PL . Note that the k-th loss sensitivity, with bus k as the slack bus, is ∂Pi k

zero. From (14) and (19), we have the following equation.

L*i =

Li , L*k = 1 Lk

From the above equations (13), (20) and (21), we get

(21)

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

1 ∂P 1− L ∂Pi 1−

1− = 1− k

∂PL = ∂Pi k

1−

∂PL ∂Pk

DS

∂PL ∂Pi

DS

∂PL ∂Pi

∂P 1− L ∂Pk

DS

271

(22)

(23)

DS

Hence, with one set of the incremental transmission loss coefficients for the distributed slack buses, the loss sensitivity for an arbitrary slack bus can be calculated from the following formula.

∂PL = ∂Pi k

∂PL ∂Pi

− DS

∂PL ∂Pk

∂P 1− L ∂Pk

DS

(24)

DS

The formula of loss sensitivity calculation is very simple, but is accurate and efficient for real-time energy markets. It will avoid computing a new set of the loss sensitivity factors whenever the slack bus k changes. Consequently, it means huge time savings. In addition, the loss factors based on the distributed load reference will not be changed no matter how the AGC statuses of units vary, as long as network topology is the same as before.

III. IMPLEMENTATION OF SFT The objective of SFT is to identify whether or not network operation is feasible for a real power injection scenario. If operational limits are violated, generic constraints and the corresponding sensitivities (the shift factors) are generated, which can be used to prevent the violation if presented with the same network conditions. Meanwhile, the shift factors can also be used in the generation scheduling or economic dispatch to alleviate the overload of transmission lines. The SFT calculations include the contingency analysis (CA), in which the decoupled power flow (DPF) or DC power flow is used. The set of component changes that can be analyzed include transmission line, transformer, circuit breaker, load demand and generator outages. SFT informs the user of contingencies that could cause conditions violating

272

Jizhong Zhu

operating limits. These limits include branch overloads, abnormal voltages, and voltage angle differences across specified parts of the network. SFT reports the sensitivity (shift factor) of the constraint with respect to the controls. These controls include unit MW control, phase shifter, and load MW control.

3.1. Unit MW Control The unit MW control is the most efficient and cheap control among these available controls. The formulation of sensitivity for unit can be written as follows.

S ij =

∂ Ki ∂Uj

i = 1,......, K max ,

j = 1,......U max

(25)

where, Sij: Ki: Uj: Kmax: Umax:

The sensitivity of the constraint i to the unit MW control j. The constraint i. The unit MW control j. The maximum number of constraints. The maximum number of generator unit MW controls.

3.2. Phase Shifter Control The phase shifter is another efficient control among these available controls. There are some assumptions for phase shifter in the SFT design. The phase shifter control variable is tap number. Normally tap number is an integer, but it can be handled as a real number in the practical SFT calculation. In addition, all opened phase shifters will be skipped over, that is, the sensitivity for the phase shifter that is open at any end will not be calculated. The step on the tap-type is the sensitivity of angle with respect to tap number. The formulation of sensitivity for phase shifter can be written as follows.

Sijp =

∂ Ki ∂ PS jp

i = 1,......, K max ,

jp = 1,......PSmax

where, Sijp: Ki: PSjp: Kmax: PSmax:

The sensitivity of the constraint i to the phase shifter control jp. The constraint i. The phase shifter control jp. The maximum number of constraints. The maximum number of phase shifter controls.

(26)

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

273

It is noted that there is a special “branch in constraint” logic that must be implemented when the phase shifter branch itself is in the constraint. Basically the artificial flow through transformer branch must be subtracted from constraint flow.

3.3. Load MW Control The load MW control should be last control when other controls are not available. The formulation of sensitivity for load MW control can be written as follows. S ijd = −

∂ Ki ∂ LD jd

i = 1,......, K max ,

jd = 1,...... LD max

(27)

where, Sijd: Ki: LDjd: Kmax: LDmax:

The sensitivity of the constraint i to the load MW control jd. The constraint i. The load MW control jd. The maximum number of constraints. The maximum number of load MW controls in whole system.

It is noted that the sensitivity sign for load MW control is negative. The reason is that increasing load will cause more serious constraint violation, rather than reduce the constraint violation. According to the sensitivity relationship between the constraint and the load MW control, it is needed to reduce / shed load for alleviating or deleting the constraint violation.

3.4. Constraint Value For each constraint, constraint value (DC value) is computed from the control values multiplied by sensitivities. The formulation can be written as follows.

DCVAL i =

U max

∑ VAL _ U j * S ij

(28)

j =1

where, DCVALi: VAL_Uj: Sij: Umax:

The constraint value for the constraint i. The value of control j. Here, controls including unit MW control, phase shifter and load MW control. The sensitivity or shift factor of the constraint i to the control j. The maximum number of controls.

274

Jizhong Zhu

The shift factors computed in SFT is based on the reference bus in EMS topology, but it can be easily converted to any market-based reference. Let k be market-based reference unit, and the shift factor of the constraint i with respect to any unit j that is obtained based on EMS reference bus is Sij. For unit k, the shift factor of the constraint i is Sik. Then, the shift factors after converting to market-based reference unit k can be computed as follows.

S ik ' = 0

i = 1,......, K max

S ij ' = S ij − S ik

i = 1,......, K max ,

(29)

j≠k

(30)

where, S’ij: The shift factor of the constraint i with respect to unit j that is based on the marketbased reference k. S’ik: The shift factor of the constraint i with respect to unit k that is based on the marketbased reference k. Let PFj be the participation factor of unit j, and the shift factor of the constraint i with respect to any unit j is Sij. For area A, the area based shift factor of the constraint i is SiA, which can be computed as follows.

S iA =

∑ (PF S ) j∈ A

j

∑ PF j∈ A

ij

i = 1,......, K max ,

j∈ A

(31)

j

where, SiA: PFj:

The area based shift factor of the constraint i. The participation factor of the unit j.

As we know that the shift factor of the constraint is related to the selected reference, i.e., the value of shift factor will be different if the reference is different even the system topology and conditions are the same. Sometimes the system operators would like to have the stable shift factor values without caring about the selection of reference bus/unit. Thus, the distributed load reference will be used to get the unique constraint shift factors if the system topology and conditions are unchanged. Let Sildref be the sensitivity of load distribution reference for the constraint i, and the shift factor of the constraint i with respect to any control j that is obtained based on EMS reference bus is Sij. Then, the shift factors based on the load distribution reference LDREF can be computed as follows.

Sij ' = Sij − Sildref

i = 1,......,Kmax

(32)

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

275

where Sildref: the sensitivity of load distribution reference for the constraint i, that is,

∑ (S

LDmax

ijd jd =1 LDmax

Sildref =

∗ LDjd )

∑ LD jd =1

i = 1,......, Kmax

(33)

jd

In the practical energy markets such as independent system operator (ISO), the system consists of many areas but one is a major area in the ISO system that is called the internal area, and others are called as external areas. If the internal area is major concerned during the price calculation for this market system, the load distribution reference can be selected based on the internal area only. Similarly, Let LDAmax be the total number of load controls in the internal area of ISO system, which is less than the total number of load controls in whole ISO system LDmax. The shift factors based on the area load distribution reference LDAREF can be computed as follows.

Sij ' = Sij − Sildaref

i = 1,......,Kmax

(34)

where Sildaref: the sensitivity of load distribution reference in area A for the constraint i, that is,

∑ (S

LDAmax

S ildaref =

jd =1

ijd

i = 1,......, K max LDAmax ∈ LDmax

LDAmax

∑ LD jd =1

LDAmax:

∗ LD jd ) (35)

jd

The maximum number of load MW controls in area A.

IV. VOLTAGE SENSITIVITY ANALYSIS The purpose of the voltage sensitivity analysis is to improve the voltage profile and to minimize system real power losses through the optimal VAR control. These goals are achieved by proper adjustments of VAR variables in power networks. Therefore, if the voltage magnitude at generator buses, VAR compensation (VAR support) and transformer tap position are chosen as the control variables, the optimal VAR control model can be represented as:

276

Jizhong Zhu min PL(QS, VG, T)

(36)

such that Q(QS, VG, T, VD) = 0

(37)

QGmin ≤ QG(QS, VG, T) ≤ QGmax

(38)

VDmin ≤ VD(QS, VG, T) ≤ VDmax

(39)

QSmin ≤ QS ≤ QSmax

(40)

VGmin ≤ VG ≤ VGmax

(41)

Tmin ≤ T ≤ Tmax

(42)

where VG: QS: QG: T: VD:

the voltage magnitude at generator buses. the VAR support in the system. the VAR generation in the system. the tap position of the transformer. the voltage magnitude at load buses

Two kinds of sensitivity-related factors can be computed through (36) – (42). Here they are called as voltage benefit factors (VBF) and loss benefit factors (LBF), which are expressed as follows.

LBFi =

VBFi =

∑

( PL 0 − PL (Qsi ))

i

∑

Qsi

(Vi (Q si ) − Vi 0 )

i

Qsi

× 100% i ∈ ND

× 100% i ∈ ND

(43) (44)

where Qsi: LBFi: VBFi: PL0: PL(Qsi): Vi0:

the amount of VAR support at the load bus i. the loss benefit factors from the VAR compensation Qsi . the voltage benefit factors from the VAR compensation Qsi . power transmission losses in the system without VAR compensation. the power transmission losses in the system with VAR compensation Qsi. the voltage magnitude at load bus i without VAR compensation.

Sensitivity Calculation in Real Time Transmission Network and Energy Markets Vi(Qsi): ND:

277

the voltage magnitude at load bus i with VAR compensation Qsi. the number of load buses.

V. SIMULATION RESULTS The calculation results of the several sensitivities are illustrated using the IEEE 14 bus system and AREVA T&D 60-bus system. The one-line diagram of the AREVA T&D 60-bus system is shown in Figure 1. The 60-bus system, which has three areas, consists of 24 generation units (15 units are available in the tests), 32 loads, 43 transmission lines and 54 transformers. CHENAUX

CHFALLS

ECAR

NANTCOKE

MARTDALE

BRIGHTON

HUNTVTIL

CEYLON

RICHVIEW

MITCHELL KINCARD

REDBRIDG

HEARN HANDOVER PICTON PARKHILL M’TOWN HOLDEN

STRATFRO LAKEVIEW

J’VILLE

WEST COBDEN

B’VILLE

EAST DOUGLAS

GOLDEN

STINSON

W’VILLE WALDEN

Figure 1. One-line diagram of AREVA T&D system (Area 1 - EAST, Area 2 - WEST, Area 3 – ECAR).

278

Jizhong Zhu The following test cases are used to analyze the loss sensitivity in this chapter: Case 1: Case 2:

Case 3:

Case 4: Case 5:

Case 6:

Calculate loss sensitivities using the distributed generation slack and load slack, respectively. All units have AGC on. Calculate loss sensitivities using the distributed generation slack and load slack, respectively. All units have AGC on except the units under station Douglas in Area 1 Calculate loss sensitivities using the distributed generation slack and load slack, respectively. All units have AGC on except the units under station HEARN in Area 1 Calculate loss sensitivities using the distributed generation slack and load slack, respectively. All units have AGC on except the units in Area 2 Calculate loss sensitivities using the distributed generation slack and load slack, respectively. All units have AGC on except the units under station HOLDEN in Area 3 Calculate loss sensitivities for the selected single slack based on the loss factors under the distributed slack.

The simulation results are shown in Table 1–6. All loss sensitivity factors for units and loads are computed. In order to reduce the length of the chapter, only loss sensitivities of generators are listed in Table 1–6, in which column 1 is the name of station and units. Column 2 is the area number that the unit belongs to. Column 3 is the AGC status of the unit. Tables 1–5 are the test results and comparison of loss sensitivity calculation based on the distributed generation reference and distributed load reference, respectively. The loss factors computed from the distributed unit reference are listed in column 4 of Table 1–5. The loss factors computed from the distributed load reference are listed in column 5 of Table 1–5. Generally, the values of loss sensitivities based on the generation reference are different from those based on the load reference, because the distribution of the units is not exactly the same as the distribution of loads in the power system. The loss factors will be close or equal if the units are close to the load locations. This can be observed from Table 1, where all units are on AGC status. For the 60-bus system, each load in area 3 has at least one unit connected, so the loss factors in area 3 are the same for both the distributed generation slack and distributed load slack. It is noted that from Table 1 – 5 that the loss sensitivity factors based on the distributed load slack are the same whether the status of the units is changed or not. But the loss factors based on the distributed generation references are changed since the AGC status of the units are different. Generally, the change of AGC status of the units only affects the loss sensitivities in the same area that these units belong to. It can be seen from Table 2 – 3 that, when AGC status of the units in area 1 changes, only the loss factors in area 1 is affected. The loss factors in the other areas are unchanged. For Table 5, when AGC status of the units in area 3 changes, only the loss factors in area 3 is affected. The loss factors in the other areas are unchanged. But for Table 4, when AGC status of the units in area 2 changes and all units in this area are not on AGC, it means that there is no unit reference in area 2. Then the units with AGC on in the other areas will pick up the

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

279

mismatch (i.e. area 1 in this case). Thus, the loss factors in area1 and 2 are changed. The loss factors in the other areas are unchanged. Table I. Test Results and Comparison of Loss Sensitivity Calculation (Case 1: All units on AGC)

Station, Generator

Area No.

AGC Unit

DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

1 1 1 1 1 1 1 1 2 2 3 3 3 3 3

YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES

Loss Sensitivity Distributed generation Slack 0.015100 0.012100 0.009900 0.009900 0.009700 -0.016500 -0.016500 -0.018800 -0.001000 0.000700 -0.008900 0.021200 0.021200 0.001000 -0.012200

Loss Sensitivity Distributed load Slack 0.017000 0.014000 0.011800 0.011800 0.011600 -0.014600 -0.014600 -0.017000 -0.004200 -0.002500 -0.008900 0.021200 0.021200 0.001000 -0.012200

Table II. Test Results and Comparison of Loss Sensitivity Calculation (Case 2: All units on AGC except the units under station Douglas in Area 1)

Station, Generator

Area No.

DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

1 1 1 1 1 1 1 1 2 2 3 3 3 3 3

AGC Unit NO NO NO NO NO YES YES YES YES YES YES YES YES YES YES

Loss Sensitivity Distributed generation Slack 0.032800 0.029900 0.027800 0.027800 0.027600 0.001500 0.001500 -0.000800 -0.001000 0.000700 -0.008900 0.021200 0.021200 0.001000 -0.012200

Loss Sensitivity Distributed load Slack 0.017000 0.014000 0.011800 0.011800 0.011600 -0.014600 -0.014600 -0.017000 -0.004200 -0.002500 -0.008900 0.021200 0.021200 0.001000 -0.012200

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Jizhong Zhu Table III. Test Results and Comparison of Loss Sensitivity Calculation (Case 3: Only units under HEARN in Area 1 not on AGC)

Station, Generator

Area No.

AGC Unit

DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

1 1 1 1 1 1 1 1 2 2 3 3 3 3 3

YES YES YES YES YES NO NO YES YES YES YES YES YES YES YES

Loss Sensitivity Distributed generation Slack 0.012600 0.009600 0.007400 0.007400 0.007200 -0.019000 -0.019000 -0.021300 -0.001000 0.000700 -0.008900 0.021200 0.021200 0.001000 -0.012200

Loss Sensitivity Distributed load Slack 0.017000 0.014000 0.011800 0.011800 0.011600 -0.014600 -0.014600 -0.017000 -0.004200 -0.002500 -0.008900 0.021200 0.021200 0.001000 -0.012200

Table IV. Test Results and Comparison of Loss Sensitivity Calculation (Case 4: All units on AGC except the units in Area 2)

Station, Generator

Area No.

AGC Unit

DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

1 1 1 1 1 1 1 1 2 2 3 3 3 3 3

YES YES YES YES YES YES YES YES NO NO YES YES YES YES YES

Loss Sensitivity Distributed generation Slack 0.015200 0.012200 0.010000 0.010000 0.009900 -0.016700 -0.016700 -0.019100 -0.021000 -0.019300 -0.008900 0.021200 0.021200 0.001000 -0.012200

Loss Sensitivity Distributed load Slack 0.017000 0.014000 0.011800 0.011800 0.011600 -0.014600 -0.014600 -0.017000 -0.004200 -0.002500 -0.008900 0.021200 0.021200 0.001000 -0.012200

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

281

Table V. Test Results and Comparison of Loss Sensitivity Calculation (Case 5: All units on AGC except unit 3 under station HOLDEN in Area 3)

Station, Generator DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

Area No. 1 1 1 1 1 1 1 1 2 2 3 3 3 3 3

AGC Unit YES YES YES YES YES YES YES YES YES YES YES YES YES NO YES

Loss Sensitivity Distributed generation Slack 0.015100 0.012100 0.009900 0.009900 0.009700 -0.016500 -0.016500 -0.018800 -0.001000 0.000700 -0.008500 0.021600 0.021600 0.001400 -0.011800

Loss Sensitivity Distributed load Slack 0.017000 0.014000 0.011800 0.011800 0.011600 -0.014600 -0.014600 -0.017000 -0.004200 -0.002500 -0.008900 0.021200 0.021200 0.001000 -0.012200

Table VI. Test Results of Loss Sensitivity Calculation (Distributed Slack vs Single Slack)

Station, Generator

AGC Unit

DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES

Loss Sensitivity Distributed Slack 0.017000 0.014000 0.011800 0.011800 0.011600 -0.014600 -0.014600 -0.017000 -0.004200 -0.002500 -0.008900 0.021200 0.021200 0.001000 -0.012200

Loss Sensitivity Single Slack, HOLDEN 1 0.016016 0.013013 0.010811 0.010811 0.010611 -0.015616 -0.015616 -0.018018 -0.005205 -0.003504 -0.009910 0.020220 0.020220 0.000000 -0.013213

Loss Sensitivity Single Slack, Douglas ST 0.005463 0.002428 0.000202 0.000202 0.000000 -0.026507 -0.026507 -0.028936 -0.015985 -0.014265 -0.020741 0.009713 0.009713 -0.010724 -0.024079

Through the above comparisons, it can be observed that the method of the distributed load references for loss sensitivity calculation is superior to that of the distributed generation

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Jizhong Zhu

references in the real time energy markets, since the AGC status of the units are changeable in the real time system. The results of loss sensitivity calculation for a single slack, which are computed from the proposed formula (24), are shown in Table 6. Column 3 in Table 6 is the set of the loss sensitivity coefficients for the distributed slack buses. Column 4 in Table 6 is the set of loss sensitivity factors with a single slack bus at the location of HOLDEN 1. Column 5 in Table 6 is the set of loss sensitivity factors with a single slack bus at the location of Douglas. It is noted that all the loss sensitivities are nonzero if the distributed slack is selected. If the single slack is selected, the loss sensitivity of the slack equals zero. Since the loss sensitivity values based on the distributed slacks from EMS are unchanged as long as the system topology is the same, the loss sensitivities for any market-based single slack can be easily and quickly acquired by use of the loss sensitivity formula (24). Therefore, a large amount of the computations are avoided whenever the loss sensitivities for a marketbased reference are needed in the real time energy markets. Table 7 – 8 are the results of the detected constraint and the corresponding shift factors. The results of one constraint that is branch T525 at Station CHENAUX are listed. Table VII. Example of the Active Constraint (Branch T525 At Station Chenaux) Constraint name Branch T525

Rating (MVA) 1171.4

Actual Flow (MVA) 1542.7

Constraint deviation 371.3

Percent of Violation 131.7

Table VIII. Test Results of SFT (Shift Factors) Calculation for the Active Constraint T525 at Station Chenaux

Station, Generator

Area No.

Unit in Serve

DOUGLAS, G2 DOUGLAS, G1 DOUGLAS, CT1 DOUGLAS, CT2 DOUGLAS, ST HEARN, G1 HEARN, G2 LAKEVIEW, G1 BVILLE, 1 WVILLE, 1 CHENAUX, 1 CHEALLS, 1 CHEALLS, 2 HOLDEN, 1 NANTCOKE, 1

1 1 1 1 1 1 1 1 2 2 3 3 3 3 3

YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES

Shift Factors on EMS Reference at Station DOUGLAS 0.000000 0. 000000 0. 000000 0. 000000 0. 000000 0. 000000 0. 000000 0. 000000 -0.013650 -0.024336 0.617887 0.521795 0. 521795 0.304269 0.291815

Shift Factors on Market Reference at Station HOLDEN -0.304269 -0.304269 -0.304269 -0.304269 -0.304269 -0.304269 -0.304269 -0.304269 -0.317919 -0.328605 0.313618 0. 217526 0.217526 0.000000 -0.012454

Sensitivity Calculation in Real Time Transmission Network and Energy Markets

283

In Table 8, column 1 is the name of station and units. Column 2 is the area number that the unit belongs to. Column 3 is the AGC status of the unit. Column 4 is the set of the shift factors of the constraint T525 with respect to the units for the EMS-based reference at station DOUGLAS. Column 5 is the shift factors of the constraint T525 with respect to the units for the market-based reference at the location of HOLDEN 1. It is noted that all the shift factors are zero for the units in area 1 for the EMS-based reference since the reference is located in area 1 and all units in area 1 are close to the reference unit. If the market-based slack is selected, the shift factors for the market-based reference can be easily obtained from equations (29) and (30). Table 9 shows the major VAR support sites as well as the corresponding benefit factors LBF and VBF for the IEEE-14 bus system. Table IX. Voltage Sensitivity Analysis Results for IEEE 14 Bus Systems VAR support Site Bus 4 Bus 5 Bus 8 Bus 9 Bus 10 Bus 11 Bus 12 Bus 13

LBFi 0.000376 0.000337 0.002309 0.007674 0.002618 0.007407 0.006757 0.008840

VBFi 0.000855 0.000884 0.001775 0.001989 0.002097 0.002175 0.002268 0.002122

CONCLUSION This chapter presents a practical approach to compute the sensitivities in the practical transmission network and energy markets. The analysis and implementation details of the loss sensitivity, voltage sensitivity, generator constraint shift factor, and area based constraint shift factor are discussed. The chapter also comprehensively discusses how to compute and use the sensitivities under the different references such as the market-based reference, and the energy management system based reference, as well as how to convert the sensitivities based on EMS system reference into the ones based on the market system reference. These sensitivities calculations can be used to determine whether the on-line capacity as indicated in the resource plan is located in the right place on the network to serve the forecasted demand. The proposed approach is tested on IEEE 14-bus system and AREVA T&D 60-bus system. The test results show the reported approaches are very fast, useful and efficient for the practical transmission network and energy markets.

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REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

[16] [17] [18]

[19] [20]

T.E. Dy-Liyacco, “Control Centers Are Here to Stay,” IEEE Computer Applications in Power, Vol.15, No.4, pp18-23, 2002. N. Winser, “FERC's Standard Market Design: the ITC Perspective,” 2002 IEEE PES Summer Meeting, Chicago, IL. July 22 - 26, 2002. A. Ott, “Experience with PJM Market Operation, System Design, and Implementation,” IEEE Trans. on Power Systems, Vol.18, No.2, pp528-534, 2003. D. Kathan, “FERC’s Standard Market Design Proposal,” 2003 ACEEE/CEE National Symposium on Market Transformation, Washington, DC, April 15, 2003. J.Z. Zhu, D. Hwang, and A. Sadjadpour, “The Implementation of Alleviating Overload in Energy Markets,” in Proc. IEEE PES 2007 General Meeting, Tampa, Florida, 2007. L.K. Kirchamayer, Economic Operation of Power Systems, New York: Wiley, 1958. H.W. Dommel, and W.F. Tinney, “Optimal power flow solutions,” IEEE Trans. on PAS, Vol.PAS-87, No.10, pp1866-1876, 1968. M. Ilic, F.D. Galiana, and L. Fink, Power Systems Restructuring: Engineering and Economics. Norwell, MA: Kluwer, 1998. D. Kirschen, R. Allan, and G. Strbac, “Contributions of individual generators to loads and flows,” IEEE Trans. Power Systems, Vol.12, No.1, pp52-60, 1997. F. Schweppe, M. Caramanis, R. Tabors, and R. Bohn, Spot Pricing of Electricity, Norwell, MA: Kluwer, 1988. J. Conejo, F.D. Galiana, and I. Kochar, “Z-Bus loss allocation,” IEEE Trans. Power Systems, Vol.16, No.1, pp105-110, 2001. F.D. Galiana, A.J. Conjeo, and I. Korkar, “Incremental transmission loss allocation under pool dispatch,” IEEE Trans. Power Systems, Vol.17, No.1, pp26-33, 2002. Elgerd, “Electric Energy Systems Theory: An Introduction,” New York: McGraw-Hill, 1982. J.Z. Zhu, D. Hwang, and A. Sadjadpour, “Loss Sensitivity Calculation and Analysis,” in Proc. 2003 IEEE General Meeting, Toronto, July 13-18, 2003. J.Z. Zhu and M.R. Irving, “Combined Active and Reactive Dispatch with Multiple Objectives using an Analytic Hierarchical Process,” IEE Proc. C, Vol.143, No.4, pp344-352, 1996. J.Z. Zhu, and J.A. Momoh, “Optimal VAR pricing and VAR placement using analytic hierarchy process,” Electric Power Systems Research, Vol.48, No.1, pp11-17, 1998. M.O. Mansour, and T.M. Abdel-Rahman, “Non-linear VAR Optimization Using Decomposition and Coordination,” IEEE Trans. PAS, Vol. 103, pp. 246-255, 1984. N.H. Dandachi, M.J. Rawlins, O. Alsac, and B. Stott, “OPF for Reactive Pricing Studies on the NGC System,” IEEE Power Industry Computer Applications Conference, PICA’95, Utah, pp. 11-17, May 1995. Alsac and B. Sttot, “Optimal Power Flow with Steady-State Security,” IEEE Trans., PAS, Vol.93, pp745-751, 1974. J.A. Momoh and J.Z. Zhu, “Improved Interior Point Method for OPF Problems,” IEEE Trans. on Power Systems, Vol.14, No.3, pp1114-1120, 1999.

In: Leading-Edge Electric Power Research Editor: C. M. O’Sullivan, pp. 285-316

ISBN: 978-1-60021-981-8 © 2008 Nova Science Publishers, Inc.

Chapter 7

WIDE-AREA MONITORING AND ANALYSIS OF INTER-AREA OSCILLATIONS USING THE HILBERT-HUANG TRANSFORM A. R. Messinaa, M. A. Andradeb and E. Barocio c a

The Center for Research and Advanced Studies (Cinvestav), Mexico b The Autonomous University of Nuevo León, Mexico c The University of Guadalajara, Mexico

ABSTRACT Many transient processes in power systems involve phenomena that vary in time and space in complicated ways. Comprehensive monitoring of large-scale power systems by means of properly placed time-synchronized phasor measurement units (PMUs) provides the opportunity to analyze and characterize complex inter-area swing dynamics involving all or most of the power system. Wide-area real-time monitoring may prove invaluable in power system dynamic studies by giving a quick assessment of the damping and frequency content of dominant system modes after critical contingencies. Measured data, however, may exhibit quite different dynamics at each system location or exhibit abrupt changes, dynamic irregularities, or be complicated by nonlinear trends or noise. Traditional Fourier and Prony methods for system identification are unable to resolve the localized nature of these processes and hence provide little useful information concerning the nature of noisy, time-varying oscillatory processes. In this Chapter, a new method for analyzing the temporal dynamics of nonlinear and non-stationary inter-area oscillations using a local empirical mode decomposition (EMD) method and the Hilbert transform is presented. Two novel algorithms are developed to address nonlinear and non-stationary issues. The first method is a local implementation of the empirical mode decomposition technique. The second is an algorithm to compute the Hilbert transform using finite impulse response (FIR) filters. By combining these approaches, the method can be used to analyze complex signals for which the conventional assumptions of linearity and stationarity may not apply and can be implemented for on-line estimation of modal damping and frequency using synchronized wide-area measurement systems.

286

A. R. Messina, M. A. Andrade and E. E. Barocio The physical mechanism underlying nonlinear time-varying inter-area oscillations is investigated and methods to characterize the observed oscillatory phenomena in terms of physically meaningful modal components are proposed. Emphasis is placed on identifying modal content in the presence of noise and nonlinear trends. Issues concerning the implementation of the method and numerical considerations are also discussed. As specific applications, data obtained from PMU measurements from a real event in the northern systems of the Mexican interconnected system are used to examine the potential usefulness of nonlinear time series analysis techniques to characterize the spatio-temporal characteristics of the observed oscillations and to determine the nature and propagation of the system disturbance. The efficiency and accuracy of the method is demonstrated by comparison to other approaches.

BACKGROUND ON THE HILBERT-HUANG TECHNIQUE Nonlinear, non-stationary behavior plays an important role in a variety of physical processes but it may be hard to identify and quantify. In this section, the combined use of Hilbert spectral analysis and the EMD method to characterize the time evolution of nonlinear, non-stationary processes is discussed.

The Empirical Mode Decomposition Technique The empirical mode decomposition is a time-series analysis method that decomposes a signal, x(t ) , into essentially band-limited components or basis functions, a requirement to get meaningful instantaneous frequencies [1,2], using information from the data itself. The essence of this technique is to identify the basis oscillatory functions by their characteristic time scales and then decompose the signal into a series of temporal modes called intrinsic mode functions (IMFs) given by n

x(t ) =

∑ c (t ) + r (t ), i

(1.1)

i =1

where n is the number of IMF components, and r (t ) is the residue after the n IMF’s have been extracted; the functions ci (t ) are nearly orthogonal and have zero local means. Each IMF is associated with a local, physical time scale and can be amplitude and/or frequency modulated and even non-stationary. The first IMF accounts for the higher frequency oscillations, while each succeeding component accounts for lower average frequencies. An IMF is defined as a wave in which [1]: (i) in the whole time span of the signal, the number of extremes, namely maxima and minima, and the number of zero crossings must be equal or differ at most by one, and (ii) at any time instant, the mean value of the amplitudes defined by the local maxima and minima, must be zero. In practice, however, only a set of IMFs contain relevant information to system behavior. As a result, we rewrite the basic model in (1.1) in the more useful form [3]

Wide-Area Monitoring and Analysis of Inter-Area Oscillations…

p

x(t ) =

∑ c (t ) + ∑ c (t ) + r (t ).

287

n

j

j =1

(1.2)

l

l = p +1

where the terms c j (t ) , j = 1,K , p contain the physical behavior of interest, and the remaining n − p terms contain uninteresting, non-sinusoidal characteristics. Given a model of the form (1.2), it is possible to apply Hilbert transform to determine local characteristics of the data. Following the work of Huang et al. [1], the original signal x(t ) can be expressed as the real part of the complex expansion n

x(t ) =

∑ j =1

⎧⎪ c j (t ) + r (t ) = Re ⎨ ⎪⎩

n

∑ j =1

t

Aj (t )e ∫0

i ω j ( t ) dt

⎫⎪ ⎬. ⎪⎭

(1.3)

where A j , ω j are the instantaneous amplitude and frequency of the j-th modal component. The IMFs are symmetric, have a unique local frequency, are nearly orthogonal, i.e.

IMFi , IMFj ≈ 0 , for i ≠ j and form a complete basis; the sum of the IMFs equals the original series. Although the HHT technique can be efficiently used to characterize nonlinear, nonstationary oscillations several problems persist: (i) The IMFs are a mix of amplitude and frequency modulated signals. Extracting from these components the underlying dynamics is not easy (ii) Although experience suggest that the extracted IMFs usually represent recognizable physical characteristics of the data, the results are not supported by an underlying physical theory, (iii) Further, some IMFs may have no practical significance in the study of complex multi-component signals. This makes the analysis and interpretation of complex phenomena a difficult task. These basis functions are then processed through Hilbert analysis to obtain magnitude, phase and damping information as a function of time.

The Sifting Process The basic method adopted to extract the IMFs consists of three steps [1,3]: a) Starting with the original signal, x(t ) , set hi (t ) = x(t ) , and extract the local minima and local maxima from hi (t ) ,

288

A. R. Messina, M. A. Andrade and E. E. Barocio b) Interpolate the local minima and local maxima with a cubic spline to form upper and lower envelopes respectively, and c) Obtain the mean of the envelopes, mi (t ) , and subtract it from hi (t ) to determine a new function hi +1 (t ) = hi (t ) − mi (t ) . The three-step procedure is repeated until

hi +1 (t ) satisfies the criteria of an IMF and then c j (t ) = hi +1 (t ) . This procedure is known as the sifting process. Although this method does not always guarantee a perfect instantaneous frequency over all conditions, the resulting instantaneous frequency is still consistent with the physics of the system under study. As pointed out in [3], an inherent aspect of the EMD is that each IMF represents a simple oscillatory mode as a counterpart of the simple harmonic function. Unlike other approaches, the decomposition of the original signal into intrinsic mode functions uses a direct, and adaptive method, which does not assume any basis. This makes it particularly attractive for the study of general signals. Once the original signal has been decomposed into set of intrinsic mode functions, the Hilbert transform can be applied to the IMF components to construct the energy-timefrequency distribution designated as the Hilbert spectrum. The following sections give a brief review of the Hilbert transform and describe the adaptation of proposed method to produce physically meaningful representations of nonlinear, and non-stationary data.

BACKGROUND: THE ANALYTIC SIGNAL OF GABOR The Hilbert Transform For a given real signal, u (t ) , its Hilbert transform is defined as

v(t ) = −

1

π

P

u (η ) 1 dη = π −∞ η − t

∫

∞

u (η ) dη , −∞ t − η

∫

∞

(1.4)

where P indicates that the Cauchy principal value of the integral is taken as the transform is an improper integral [2]. In a similar way, the Hilbert inverse transform is given by

u (t ) =

1

π

P

v(η ) 1 dη = − P π −∞ η − t

∫

∞

v(η ) . −∞ t − η

∫

∞

(1.5)

These expressions can be written in a more convenient way in the form of convolutions as

v(t ) = u (t ) ∗

1 , πt

(1.6)

Wide-Area Monitoring and Analysis of Inter-Area Oscillations…

u (t ) = −v(t ) ∗

1 . πt

289

(1.7)

As shown in (1.6), the Hilbert transform returns a signal v(t ) with the same power as the original u (t ) but phase-shifted at each frequency by −π / 2 . Instead of evaluating the integral in (1.4), it is more practical to obtain the representation in the Gabor domain to take advantage of the analytical properties of the Frequency spectrum [4].

The Analytic Signal The complex helical signal whose imaginary part is the Hilbert transform of the real signal,

ψ (t ) = u (t ) + jv(t ),

(1.8)

is known as the analytic signal [4], where the real part, u (t ) , is the data itself and the imaginary part is given by the Hilbert transform of the signal in (1.4). The term analytic function is used in the sense of a complex function Ψ ( z ) of the complex variable

z = t + jτ . In what follows, we briefly review existing techniques for computing the Hilbert transform, and propose a technique for its local calculation based on finite impulse response (FIR) filters.

CONTINUOUS-TIME ESTIMATION OF THE HILBERT TRANSFORM Existing approaches to the numerical calculation of the Hilbert transform are based on the computation of the analytic signal using the Fourier transform. Details of this technique are given in [5], but a brief outline is provided here. Fourier-based techniques have a global character since they span the whole data range and hence, are not well adapted for characterization of local signal attributes. This limits its application to off-line studies requiring the full data set. Essentially, the practical implementation of the Hilbert transform using these approaches can be achieved by using the following steps [5]: • •

Perform a Fourier transform of the data, and set all the Fourier coefficients with negative frequency to zero. Multiply the results by two, and perform and inverse Fourier transform. The result is the complex-valued Hilbert transform.

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A. R. Messina, M. A. Andrade and E. E. Barocio

A major problem with this approach is that the Hilbert transform is a step function in frequency. This behavior can cause undesirable Gibbs’ phenomena resulting in ripples in the Hilbert spectrum at the end of the data set. This, in turn, creates errors in the instantaneous frequencies and amplitudes calculated from the affected regions. In the succeeding sections, a brief description of the method is discussed, followed by the mechanics of deriving the numerical approximations. First, some drawbacks and limitations of the existing frameworks for computation of the Fourier-based Hilbert transform are presented and the alternative algorithms are outlined. A new technique for the local computation of the Hilbert transform and the associated analytic signal is then introduced.

The Continuous-Time Hilbert Transform The analytic signal ψ (t ) associated with the signal x(t ) , is defined by

ψ (t ) = x(t ) + xh (t ).

(1.9)

Taking the Fourier transform of (1.9) with respect to f , gives [5]

⎧2 X ( f ), ⎪ Ψ ( f ) = ⎨ X (0), ⎪ 0, ⎩

for f > 0 for f = 0, for f < 0

where

X(f ) =

∫

∞

−∞

x(t )e − j 2π ft dt

is the Fourier transform of x(t ) . Since x(t ) is real, it is possible to show that its Fourier ∗

transform is complex conjugated and symmetric, i.e. X (− f ) = X ( f ) [2]. Based on these relationships, the analytic signal can be obtained, in continuous time, using the fast Fourier transform as follows: 1. Obtain the N-point FFT of the real signal x(t ) . Compute the Hilbert transform using the expression above. 2. Obtain the Fourier transform of the analytic signal as

⎧ X [0], ⎪2 X [m], ⎪ Ψ[m] = ⎨ N ⎪ X [ 2 ], ⎩⎪0,

for m = 0 for 1 ≤ m ≤ for m = N2 for

N 2

N 2

−1

.

+1 ≤ m ≤ N −1

(1.10)

Wide-Area Monitoring and Analysis of Inter-Area Oscillations…

291

Compute the analytic signal in continuous-time using the N-point FFT inverse of (1.10). While the underlying idea is straightforward, this approach has some drawbacks that make it not too reliable. A further limitation is that it is necessary to use the whole of the signal to obtain instantaneous characteristics. In a variation to existing approaches a new algorithm for the local implementation of the Hilbert transform is proposed that circumvents these limitations and enables to track the temporal evolution of arbitrary signals, on an on-line basis.

REAL-TIME IMPLEMENTATION OF THE HILBERT TRANSFORM Transient signals encountered in power systems and other applications are inherently non-stationary. This section explores approaches to extending Hilbert spectral techniques to analyze the local properties of general non-stationary signals. In this formulation, the Hilbert transform is developed using finite impulse response (FIR) filters whose frequency response is an approximation to the response of an ideal Hilbert transformer. Since the method is based on local information, this technique is well-suited for real-time applications.

The Discrete-Time Hilbert Transform jω

The linear time-invariant discrete-time system whose frequency response H (e ) is given by [6,7]

⎧− j, H (e jω ) = ⎨ ⎩ j,

0 ≤ω