Stand-alone and Hybrid Wind Energy Systems: Technology, Energy Storage and Applications (Woodhead Publishing Series in Energy)

Stand-alone and hybrid wind energy systems © Woodhead Publishing Limited, 2010 Related titles: Wind energy systems: ...

Author: J. K. Kaldellis

287 downloads 2555 Views 9MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

Stand-alone and hybrid wind energy systems

© Woodhead Publishing Limited, 2010

Related titles: Wind energy systems: Optimising design and construction for safe and reliable operation (ISBN 978-1-84569-580-4) Large-scale wind power generation is one of the fastest developing sources of renewable energy and already makes substantial contributions to power grids in many countries worldwide. With technology maturing, the challenge is now to increase penetration, and optimise the design, construction and performance of wind energy systems. Fundamental issues of safety and reliability are paramount in this drive to increase capacity and efficiency. This book provides a comprehensive reference on the design and construction of wind energy systems, from wind resource modelling and siting considerations, to advanced systems integration and optimisation, including offshore and other problematic environments. Solid oxide fuel cell technology: Principles, performance and operations (ISBN 978-1-84569-628-3) High-temperature solid oxide fuel cell (SOFC) technology is a promising power generation option, which features high electrical efficiency and low emissions of environmentally polluting gases such as CO2, NOx and SOx. It is ideal for distributed stationary power generation applications where both high-efficiency electricity and high-quality heat are in strong demand. This book presents a systematic and indepth narrative of the technology from the perspective of fundamentals, providing comprehensive theoretical analysis and innovative characterisation techniques for SOFC technology. The book covers the development of SOFC technology, from cell materials and fabrication, to performance analysis and stability and durability issues. Details of these and other Woodhead Publishing books can be obtained by: • •

visiting our web site at www.woodheadpublishing.com contacting Customer Services (e-mail: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext. 130; address: Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK)

If you would like to receive information on forthcoming titles, please send your address details to: Francis Dodds (address, tel. and fax as above; e-mail: francis. [email protected]). Please confirm which subject areas you are interested in.


Woodhead Publishing Series in Energy: Number 6

Stand-alone and hybrid wind energy systems Technology, energy storage and applications

Edited by J. K. Kaldellis

Oxford

Cambridge


New Delhi

Published by Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK www.woodheadpublishing.com Woodhead Publishing India Private Limited, G-2, Vardaan House, 7/28 Ansari Road, Daryaganj, New Delhi – 110002, India www.woodheadpublishingindia.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway, NW, Suite 300, Boca Raton, FL 33487, USA First published 2010, Woodhead Publishing Limited and CRC Press LLC © Woodhead Publishing Limited, 2010 The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN 978-1-84569-527-9 (book) Woodhead Publishing ISBN 978-1-84569-962-8 (e-book) CRC Press ISBN 978-1-4398-0143-7 CRC Press order number: N10031 The publishers’ policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elemental chlorine-free practices. Furthermore, the publishers ensure that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Toppan Best-set Premedia Limited Printed by TJ International Limited, Padstow, Cornwall, UK


Contents

Contributor contact details Woodhead Publishing Series in Energy Preface

Part I

1

1.1 1.2 1.3 1.4 1.5 1.6 1.7 2

2.1 2.2 2.3 2.4

Fundamental science and engineering of stand-alone and hybrid wind energy systems and energy storage technology Overview of stand-alone and hybrid wind energy systems J. K. Kaldellis, TEI of Piraeus, Greece Introduction Description of a wind-based stand-alone energy system Description of a stand-alone hybrid energy system Energy storage opportunities of stand-alone hybrid energy systems Applications of stand-alone and hybrid energy systems The future of stand-alone hybrid energy systems References Overview of energy storage technologies for renewable energy systems D. P. Zafirakis, TEI of Piraeus, Greece Introduction Description of a typical energy storage system (ESS) Application range of energy storage systems (ESSs): category of generation Application range of energy storage systems (ESSs): category of transmission and distribution

xiii xvii xix

1

3 3 4 7 11 13 24 26

29 29 32 42 44 v


vi

Contents

2.5

Application range of energy storage systems (ESSs): category of customer service Application range of energy storage systems (ESSs): requirements of electricity applications Contemporary energy storage systems (ESSs) Mechanical energy storage Chemical energy storage Electrical energy storage Comparison of energy storage systems (ESSs) Future trends References

2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 3

3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 4

4.1 4.2 4.3 4.4 4.5 4.6 4.7

Design and performance optimisation of stand-alone and hybrid wind energy systems E. Kondili, TEI of Piraeus, Greece Introduction: scope and objectives of the chapter Energy systems modelling Synthesis, design and operation of a hybrid energy system Hybrid energy systems optimisation techniques Software tools for the simulation and optimisation of hybrid energy systems Summary of optimisation techniques Future trends References and further reading Feasibility assessment for stand-alone and hybrid wind energy systems J. K. Kaldellis, TEI of Piraeus, Greece Introduction First installation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Maintenance and operation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Cost-benefit analysis of a typical stand-alone hybrid electricity generation wind-based (HEW) system Reliability impact-loss of load cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Electricity generation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system Socio-environmental impacts of stand-alone hybrid electricity generation wind-based (HEW) systems


46 47 49 50 53 61 63 72 74

81 81 82 87 91 94 96 97 98

102 102 104 107 109 112 114 115

Contents 4.8 4.9

4.10 4.11

Analysis of case studies of stand-alone hybrid electricity generation wind-based (HEW) systems Sensitivity analysis of the financial behaviour of stand-alone hybrid electricity generation wind-based (HEW) systems Conclusions References

vii

121

145 155 156

Part II Development of stand-alone and hybrid wind energy systems and energy storage technology

163

5

Stand-alone wind energy systems D. Wood, University of Newcastle, Australia and P. Freere, Monash University, Australia Introduction Stand-alone wind energy systems Small wind turbine technology Control and electronics Stand-alone power systems Further aspects of system sizing Conclusions References

165

Hybrid wind–diesel energy systems G. Bhuvaneswari and R. Balasubramanian, Indian Institute of Technology (Delhi), India Introduction Overview of wind–diesel generation system Wind turbine sizing in a hybrid wind–diesel scheme Wind–diesel systems: design considerations Components of a hybrid wind–diesel system Control strategies for wind–diesel generation systems Modelling and simulation of wind–diesel systems Conclusions Future trends References

191

Hybrid wind–photovoltaic energy systems G. Notton, University of Corsica, France Introduction Renewable energy resources and their potential

216

5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 6

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10 7 7.1 7.2


165 166 170 177 183 185 188 189

191 192 194 195 197 199 207 211 213 214

216 216

viii

Contents

7.3

Design and configuration of a wind–photovoltaic (PV) hybrid energy system Modelling and simulation of a wind–photovoltaic (PV) hybrid energy system Sizing and optimization of a wind–photovoltaic (PV) hybrid energy system Wind–photovoltaic (PV) hybrid energy system: case studies Future trends Conclusions References Nomenclature

7.4 7.5 7.6 7.7 7.8 7.9 7.10 8 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 9

9.1 9.2 9.3 9.4

9.5 9.6

226 227 239 242 247 248 248 251

Hybrid wind–hydrogen energy systems T. Tsoutsos, Technical University of Crete, Greece Introduction Design of wind electrolysis production systems Design of hydrogen storage systems Optimization of wind–hydrogen power systems Environmental impact assessment of wind–hydrogen systems Market potential and barriers for wind–hydrogen systems Future trends Sources of further information and advice References Abbreviations

254

Hybrid wind–hydropower energy systems O. A. Jaramillo, O. Rodríguez-Hernández and A. Fuentes-Toledo, Universidad Nacional Autónoma de México, Mexico Introduction The need to couple wind–hydropower systems (WHPS) Different types of wind–hydropower systems (WHPS) Research and development of wind–hydropower systems (WHPS) (modelling/simulation and evaluation experience) Benefits and limitations of wind–hydropower systems (WHPS) Different operational policies and techniques for isolated grids

282


254 255 260 263 267 272 274 279 279 281

282 283 284

302 310 314

Contents 9.7 9.8 9.9 9.10 9.11 10

10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 10.10 10.11 10.12 10.13 10.14 10.15 10.16 10.17 10.18 10.19 10.20 10.21 11

11.1 11.2 11.3 11.4 11.5

Environmental impacts of wind–hydropower systems (WHPS) The economics of wind–hydropower systems (WHPS) Conclusions Acknowledgements References Electro-chemical energy storage technologies for wind energy systems M. Skyllas-Kazacos, University of New South Wales, Australia Introduction Off-grid or remote power systems Wind–diesel grids Large grid-connected wind farms Energy storage Fundamentals of electrochemical cells Types of electrochemical energy storage technologies Electrochemical capacitors (EC) Fuel cells Lead–acid battery Nickel–metal hydride batteries Li ion battery Metal–air battery Sodium–sulphur (NaS) battery The zero emissions battery research activity (ZEBRA) battery Flow batteries Zn/Br battery All-vanadium redox battery (G1 VB) Vanadium bromide redox battery (G2 V/Br) Summary References Flywheel energy storage technologies for wind energy systems A. Ruddell, STFC Rutherford Appleton Laboratory, UK Introduction Flywheel design and construction Features and limitations of flywheel storage technology Technology status of flywheel storage technology Application of flywheel storage technology


ix

315 316 318 319 320

323

323 324 326 328 329 329 335 335 336 339 343 344 346 347 350 352 354 357 361 362 363

366

366 368 375 377 383

x

Contents

11.6 11.7

Sources of further information and advice References

12

Compressed air energy storage technologies for wind energy systems A. Cavallo, Princeton, USA Introduction Current status and future progress of compressed air energy storage (CAES) Texas: the Ridge Energy wind compressed air energy storage (CAES) study Wind integration issues Discussion and conclusions References and notes

12.1 12.2 12.3 12.4 12.5 12.6

Part III Applications of stand-alone and hybrid wind energy systems and energy storage technology 13

13.1 13.2 13.3 13.4 13.5 13.6 13.7 13.8 13.9 13.10 13.11 13.12 14

14.1 14.2 14.3

Integration of renewable energy systems into remote micro-grids J. A. Carta, University of Las Palmas de Gran Canaria, Spain Introduction Hybrid micro-grid options General comments on the technological components of isolated micro-grids Architectures for stand-alone hybrid micro-grids Control and monitoring of hybrid micro-grids Design and construction of hybrid micro-grids Modelling and simulation of hybrid micro-grids Optimising integration of hybrid micro-grids Advantages and limitations of hybrid micro-grids Future trends Sources of further information and advice References Integration of stand-alone and hybrid wind energy systems into buildings K. A. Kavadias, TEI of Piraeus, Greece Introduction Building sector characteristics An overview of energy consumption in buildings


390 390

393 393 396 403 404 418 419

423

425

425 427 429 435 437 442 450 457 461 462 463 464

475 475 477 478

Contents 14.4 14.5 14.6 14.7 14.8 14.9 15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9

European Union facts about hybrid energy systems in buildings Description of hybrid energy systems in buildings Sizing procedure for hybrid energy systems in buildings Operational modes of hybrid energy systems in buildings System performance and optimization of hybrid energy systems in buildings References and further reading

xi

481 482 489 495 496 504

Hybrid wind energy systems for desalination E. Kondili, TEI of Piraeus, Greece Introduction: the water scarcity problem Desalination processes and plants Energy requirements of desalination processes Integrated systems of renewable energy sources (RES) with desalination plants Environmental impacts of renewable energy sources (RES)-based desalination plants Economic considerations in renewable energy sources (RES)-based desalination Future trends Sources of further information and advice References

506

Index

536


506 507 512 516 525 527 531 532 533

Contributor contact details

(* = main contact)

Chapter 3 and 15

Editor and Chapters 1 and 4

E. Kondili Mechanical Engineering Department TEI of Piraeus 250 P. Ralli and Thivon Avenue 122 44 Athens Greece E-mail: [email protected]

J. K. Kaldellis Laboratory of Soft Energy Applications and Environmental Protection TEI of Piraeus PO Box 41046 12201 Athens Greece E-mail: [email protected]

Chapter 2 D. P. Zafirakis Laboratory of Soft Energy Applications and Environmental Protection TEI of Piraeus PO Box 41046 12201 Athens Greece E-mail: [email protected]

Chapter 5 David Wood* School of Engineering University of Newcastle NSW 2308 Australia E-mail: David.Wood@newcastle. edu.au Peter Freere Department of Electrical and Computer System Engineering Monash University Victoria 3800 Australia

xiii © Woodhead Publishing Limited, 2010

xiv


Chapter 6

Chapter 9

Dr G. Bhuvaneswari* Department of Electrical Engineering Indian Institute of Technology New Delhi-110016 India E-mail: [email protected]

O. A. Jaramillo* Centro de Investigación en Energía Universidad Nacional Autónoma de México Privada Xochicalco s/n Temixco Morelos CP 62580 México E-mail: [email protected]

Dr R. Balasubramanian Centre for Energy Studies Indian Institute of Technology New Delhi-110016 India E-mail: [email protected]

Chapter 7 G. Notton Department of Renewable Energy University of Corsica – UMR CNRS 6134 Scientific Centre of Vignola Route des Sanguinaires F20 000 Ajaccio France E-mail: [email protected]

Chapter 8 T. Tsoutsos Laboratory of Renewable and Sustainable Energy (ReSEL) Department of Environmental Engineering Technical University of Crete University Campus GR 73100 Chania Greece E-mail: theocharis.tsoutsos@ enveng.tuc.gr

O. Rodríguez-Hernández and A. Fuentes-Toledo Universidad Nacional Autónoma de México Privada Xochicalco s/n Temixco Morelos CP 62580 México E-mail:[email protected] [email protected]

Chapter 10 M. Skyllas-Kazacos School of Chemical Sciences and Engineering Faculty of Engineering University of New South Wales Sydney NSW 2052 Australia E-mail: [email protected]

Chapter 11 A. Ruddell STFC Rutherford Appleton Laboratory Harwell Science and Innovation Campus Didcot OX11 0QX UK E-mail: [email protected]



xv

Chapter 12

Chapter 14

A. Cavallo 289 Western Way Princeton NJ 08540 5336 USA E-mail: [email protected]

K. A. Kavadias Laboratory of Soft Energy Applications and Environmental Protection TEI of Piraeus PO Box 41046 Athens 12201 Greece E-mail: [email protected]

Chapter 13 J. A. Carta Department of Mechanical Engineering University of Las Palmas de Gran Canaria Engineering Building 35017 Las Palmas de Gran Canaria Spain E-mail: [email protected]


Woodhead Publishing Series in Energy

1

Generating power at high efficiency: Combined cycle technology for sustainable energy production Eric Jeffs

2

Advanced separation techniques for nuclear fuel reprocessing and radioactive waste treatment Edited by Kenneth L. Nash and Gregg J. Lumetta

3

Bioalcohol production: Biochemical conversion of lignocellulosic biomass Edited by Keith Waldron

4

Understanding and mitigating ageing in nuclear power plants: Materials and operational aspects of plant life management (PLiM) Edited by Philip G. Tipping

5

Advanced power plant materials, design and technology Edited by Dermot Roddy

6

Stand-alone and hybrid wind energy systems: Technology, energy storage and applications Edited by J. K. Kaldellis

7

Biodiesel science and technology: From soil to oil Jan C. J. Bart, Natale Palmeri and Stefano Cavallaro

8

Developments and innovation in carbon dioxide (CO2) capture and storage technology Volume 1: Carbon dioxide (CO2) capture, transport and industrial applications Edited by M. Mercedes Maroto-Valer

9

Geologic repositories for safe disposal of nuclear materials Edited by Joonhong Ahn and Mick Apted xvii © Woodhead Publishing Limited, 2010

xviii

Woodhead Publishing Series in Energy

10

Wind energy systems: Optimising design and construction for safe and reliable operation Edited by John Dalsgaard Sørensen and Jens Nørkær Sørensen

11

Solid oxide fuel cell technology: Principles, performance and operations Kevin Huang and John Bannister Goodenough

12

Handbook of advanced radioactive waste conditioning technologies Edited by Michael I. Ojovan

13

Nuclear reactor safety systems Edited by Dan Gabriel Cacuci

14

Materials for energy efficiency and thermal comfort in buildings Edited by Matthew R. Hall

15

Handbook of biofuels production: Processes and technology Edited by Rafael Luque, Juan Campelo and James Clark

16

Developments and innovation in carbon dioxide (CO2) capture and storage technology Volume 2: Carbon dioxide (CO2) storage and utilisation Edited by M. Mercedes Maroto-Valer

17

Oxy-fuel combustion for fossil-fuel power plants: Developments and applications for advanced CO2 capture Edited by Ligang Zheng


Preface

There is a growing awareness of the necessity to develop and implement innovative energy solutions that will be cost-effective, cover the required energy demand and also minimize potential environmental impacts. Standalone and hybrid wind-based energy systems are a very promising solution with excellent prospects to cover the energy needs of specific areas in an efficient and sustainable way. The development of this book has been stimulated by the continuously increased interest in the stand-alone and hybrid wind-based energy systems. In order to cover the scientific and practical issues of these systems, the book’s chapters deal with the state of the art of the technology, design, operation, feasibility and applications of hybrid wind-based energy systems. The chapters have been written by expert authors from academia and industry, who are highly experienced in their respective fields, and I would like to take the opportunity to sincerely thank all the contributors to the book and to express my hope that the result of our concerted efforts will be rewarding. More specifically, the book is based on a comprehensive synthesis of the aforementioned subjects in holistic chapters that are described below. Stand-alone wind-based hybrid energy systems are an attractive solution supplying clean electricity to autonomous consumers and allowing them to be independent from oil price fluctuations and any political conflicts that might affect the energy sector. Hence, taking into consideration the increased development of these systems and their significant future prospects, Chapter 1 presents an overview of the stand-alone and hybrid wind energy systems. Subsequently, Chapter 2 is dedicated to the presentation of contemporary energy storage technologies, also emphasizing the critical role of energy storage for further stand-alone system expansion. Accordingly, the scope of Chapter 3 is to analyse and describe the concepts and the parameters that may affect the design and optimization of wind-based hybrid energy systems. For this purpose a short review of the methods and techniques usually employed in energy systems optimization is included. xix © Woodhead Publishing Limited, 2010

xx

Preface

The first part of the book is completed with the investigation of the financial behaviour of hybrid energy systems on the basis of an integrated cost–benefit analysis. In this context, methods and tools for the estimation of the corresponding pay-back period, the financial efficiency, the net present value and the internal rate of return of a hybrid energy system are described. In addition, the reliability, social and environmental benefits of wind-based stand-alone and hybrid energy systems are also investigated. The second part of the book demonstrates the development of standalone and hybrid energy systems in view of the evolution of the corresponding energy storage technologies. In this context, Chapter 5 deals with the stand-alone power systems containing small wind generators. Emphasis is placed on the control and the electronics system as well as on the assessment of the energy yield and the estimation of loads to be powered. Chapter 6 deals with certain important issues concerning wind–diesel systems, such as the circumstances under which such a wind–diesel hybrid energy system may be installed, the system overview, design considerations, selection of generator ratings and control schemes especially under varying wind velocities and fluctuating load conditions. Subsequently, Chapter 7 includes a brief presentation of a photovoltaic-assisted wind-based standalone system, while special attention is given to the complementarity of wind and solar resources. Chapter 8 focuses on wind–hydrogen stand-alone energy systems, underlining the fact that there is a potential of generating relatively inexpensive hydrogen from the exploitation of wind energy via electrolysis. This hydrogen quantity can be used in appropriate fuel cells in order to produce electricity during high demand and low wind speed time periods. The corresponding environmental benefits and impacts are also discussed. Accordingly, in Chapter 9 a number of useful concepts are presented to help the understanding of the different factors involved in coupling water and wind as complementary energy systems. Different wind–hydro energy systems are described, including water pumping. Chapter 10 analyses in depth the existing electro-chemical energy storage technologies, including sodium sulphur, zinc bromine and vanadium redox flow battery technologies. Actually, special attention is paid in order to demonstrate the abilities of the flow batteries, since they separate power output and storage time thus offering great flexibility, particularly in applications requiring several hours of storage. This is not the case for flywheel energy storage technologies presented in Chapter 11, which are mainly applied to power smoothing in wind energy systems. More precisely, Chapter 11 provides an overview of flywheel storage technology, including the rotor design and construction, the power interface using flywheels and a description of commercial products, specifications, and capital and running costs. Part II ends


Preface

xxi

with Chapter 12, describing the compressed air energy storage technologies for wind energy systems. Recognising the importance of the implementation of socially and environmentally beneficial energy systems, the last part of the book describes some of the most interesting and financially attractive applications of windbased stand-alone hybrid energy systems in selected sectors of economy. More specifically, Chapter 13 describes micro-grids comprising hybrid energy systems, loads and energy storage systems as a sustainable energy solution for remote areas in the world. Accordingly, the capability of standalone hybrid energy systems, when properly sized, to handle the electrification requirements of numerous isolated consumers worldwide, including country houses and remote farms is examined in Chapter 14. Chapter 15 describes the technology and implementation of water desalination systems, based on wind energy exploitation, for fresh water supply and highlights another valuable contribution of wind-based stand-alone and hybrid energy systems in the solution of imperative social problems. J. K. Kaldellis


1 Overview of stand-alone and hybrid wind energy systems J. K. KALDELLIS, TEI of Piraeus, Greece

Abstract: This chapter introduces the reader to the definition and development of stand-alone and hybrid energy systems. Emphasis is given to the description of wind-based stand-alone hybrid energy systems as well as to the use of energy storage for the support of such configurations. Accordingly, the most established applications of similar systems are presented, including some representative real-life examples. Future prospects of such systems are discussed. Key words: stand-alone system, hybrid system, wind power, energy storage, remote consumer.

1.1

Introduction

At the beginning of the twenty-first century, almost every inhabitant of the industrialized world has access to a constant electricity supply and thus electricity may be viewed as a significant aspect of contemporary societies, similar to fresh water and clean air. Nevertheless, this is not the case for the planet’s entire population. According to official statistics (European Commission, 1999), almost two billion people worldwide have no direct access to electrical networks with 500 000 of them living in the European Union and other financially developed countries. Afar from decision centres and having limited political influence, isolated consumers are usually abandoned, facing a dramatically insufficient infrastructure (Jensen, 2000; Kaldellis et al., 2001a). In this context, autonomous stand-alone wind-power systems have proven to be one of the most interesting and environmentally friendly technological solutions for the electrification of remote consumers, especially in the presence of high wind potential (Kaldellis, 2002, 2004). Small wind turbines are able to produce an annual total of only few MW h which, although limited in absolute numbers, makes a considerable difference in upgrading living standards in the remote areas of our planet. The required investment cost, however, may be quite high, especially in cases of medium quality wind potential regions and no-load rejection operational conditions, i.e. the entire load demand must be met. One of the most expensive components of a stand-alone system is the energy storage device, necessary to guarantee the required system reliability. Thus, in cases of 3 © Woodhead Publishing Limited, 2010

4


increased system autonomy the energy storage contribution to the initial or the total operational cost is found to be dominant (Kaldellis, 2003, 2008a). In addition, energy storage systems are usually land-intensive, need a lot of maintenance and often need to be replaced every specific time period, thus increasing the operational cost of the system. To avoid oversizing of energy storage configurations, wind-based stand-alone systems are augmented with another available energy source, such as solar energy, hydropower or biomass. Such a stand-alone hybrid energy system is an option worth considering (Muselli et al., 1999; Kaldellis and Kavadias, 2001; Kaldellis et al., 2006a). Recapitulating, stand-alone wind energy systems are electricity-generating systems, based on the operation of one or more wind turbines, being also remote (not connected) from the central electrical grids. In this context, import or export of electricity is not permitted, but there are occasions where a stand-alone system can be connected to an existing electrical network, e.g. emergency status (Bueno and Carta, 2006). Accordingly, hybrid energy systems incorporate two or more electricity generation options, based either on the exploitation of renewable energy sources (RES) or on small thermal power units, e.g. diesel-electric generators or even micro-turbines. Note, however, that with regards to the case currently studied, the first electricity generation option is by definition wind energy.

1.2

Description of a wind-based stand-alone energy system

A typical wind energy stand-alone system (see Fig. 1.1) includes: • one or more (usually small) wind converters of No kW; • an appropriate energy storage device, e.g. a lead–acid battery storage array, able to guarantee ho hours of autonomy, or equivalently with energy storage capacity Qmax and maximum permitted discharge capacity Qmin; • an AC/DC rectifier of Nr kW in case the energy storage installation operates on DC current; • a charge controller of Nc kW; • a UPS (uninterruptible power supply) of Np kW in order to guarantee high quality AC electricity generation; • a DC/AC inverter of Np kW.

1.2.1 Wind turbine The rated power of the selected wind-turbine(s) depends on the system electricity demand, the available wind potential and the operational


Overview of stand-alone and hybrid wind energy systems

5

UPS AC/DC rectifier

Charge controller

Inverter Control panel

Wind turbine

AC load

Battery bank

1.1 Typical wind energy stand-alone system.

characteristics of the machine (Vlachou et al., 1999). Keep also in mind that the wind-turbine output curves are given at standard-day conditions, without air humidity. Thus, in real-day conditions, the output of the turbine depends (Kaldellis et al., 2004) on the wind speed value V at hub height, the manufacturer’s power curve NW = NW* (V) at standard day conditions and the air density ρ at the installation area, thus: NW (V) =

ρ * (V) NW 1.2215

1.1

Finally, note that the air density value depends on the ambient temperature and pressure as well as on the corresponding air humidity (Houghton and Brock, 1980).

1.2.2 Energy storage There are several different energy storage alternatives, such as flywheels, hydraulic storage, pumped hydro, battery storage and even fuel cells (Kaldellis and Zafirakis, 2007; Kaldellis et al., 2009a), with lead-acid batteries being one of the most widely applied solutions adopted in similar size applications. The operational principle of an energy storage installation in this kind of systems is based on the accumulation of available energy surplus in order for it to be used during periods of inadequate wind. More precisely, the energy storage size is given in units of the time-period that the storage can cover the average load without the contribution of other power sources. Hence, the energy storage system used is defined by the installation’s hours of energy autonomy ho, the corresponding operational characteristics, e.g. the output voltage Ub for battery storage systems, the maximum permitted depth of discharge DODL and the overall efficiency of the energy storage branch. Note that the latter includes the energy


6


storage process (e.g. rectifier and charge controller losses), the standing losses owing to the energy storage self-discharge, the losses of the line connecting the storage branch apparatus and finally any electricity generation losses (e.g. inverter).

1.2.3 System electronic devices To ensure smooth operation for the remote consumer under investigation an AC/DC rectifier of nominal power Nr related to the wind turbine rated power No is necessary to convert the incoming three-phase AC voltage UAC from the wind turbine excess power to a nominal UDC corresponding to the DC current accepted by the system charge controller. Note that in cases of pumped hydro (Kaldellis et al., 2001b) or small compressed air energy storage (CAES) (Zafirakis and Kaldellis, 2009) systems this transformation is not required. The output of the AC/DC rectifier enters a DC/DC charge controller of Nc rated power that charges the system batteries with a charging voltage Ucc, slightly higher than the respective of the batteries Ub and feeds any existing DC loads of the installation. The corresponding charge rate Rch depends on the charge voltage and the battery charge current, while the discharge rate is defined by the battery voltage and the corresponding discharge current. Finally, any excess energy is forwarded to other low-priority loads or is directly rejected into a water-heating dump load by the controller, if no other low-priority loads exist. The energy storage electricity production branch is based on either an appropriate DC/AC inverter converting the DC output of the batteries into standard 50 Hz current of operational voltage 220/380 V or a small hydro turbine (in the case of pumped hydro). Several other electricity options are described in the next chapters of this book. The maximum power Np of the inverter (hydro turbine) should be capable of meeting the AC consumption peak load demand, including a future increase margin (e.g. 30%), while its efficiency strongly varies with the load demand. In fact, during partial load operation remarkable efficiency decrease is encountered. In Fig. 1.2 one may find a typical inverter efficiency curve for its entire operational range. Finally, UPS of rated power Np, frequency 50 Hz and operational voltage 220/380 V, is optionally applied in cases that the system load requires specific operational conditions and the existing wind turbine cannot provide the required quality of power for the consumer devices. The UPS autonomy time δt (e.g. δt ≈ 1–2 min) should be also adequate to facilitate the other power devices (e.g. battery-inverter, diesel electric generator) in meeting the consumption load on occasions of sudden low wind energy production.



7

1.00

Efficiency (h)

0.98 0.96 0.94 0.92 0.90 0.88 0.86

0

1000

2000

3000

4000

5000

6000

Electrical load (W)

1.2 Typical inverter efficiency curve.

1.2.4 Operational modes During the long-term operation of the proposed stand-alone system, the following situations may appear: The power demand ND is less than the power output NW of the wind turbine, (NW > ND). In this case the energy surplus (ΔN = NW − ND) is stored via the rectifier and the energy charge controller. If the energy storage facility is full (Q = Qmax), the residual energy is forwarded to low-priority loads. • The power demand is greater than the power output of the wind turbine (NW < ND), which is not zero, i.e. NW ≠ 0. In similar situations, the energy deficit (ΔN = ND − NW) is covered by the energy storage system via the DC/DC converter and the DC/AC inverter. During this operational condition, special emphasis is laid on the two electricity production subsystems management plan. • There is no energy production (e.g. low wind speed, machine not available), i.e. NW = 0. In this case, all the energy demand is fulfilled by the energy storage–DC/DC controller–DC/AC inverter subsystem, under the condition that Q > Qmin. In this case and the previous one, when the energy storage system is near the energy storage bottom limit, an electricity-demand management plan should be applied; otherwise the load would be rejected. •

1.3

Description of a stand-alone hybrid energy system

Stand-alone systems based on RES exploitation have proved both interesting and environmentally friendly technological solutions for the electrification of remote consumers. However, the first installation cost is quite high


8


(Kaldellis and Kavadias, 2007) while in some occasions the life-cycle cost is also high (Kaldellis and Kavadias, 2006). As already mentioned, to limit the relatively high operational cost and to increase the system reliability several authors suggest the reinforcement of the stand-alone solution with the parallel exploitation of more than one RES, i.e. installation of stand-alone hybrid energy systems based on the available renewable potential of each candidate region. Actually, a hybrid energy system incorporates two or more electricity generation options based either on pure RES or utilizing also a small thermal power unit (e.g. diesel-electric generator or a small gas-turbine) along with an appropriate energy storage bank and the corresponding electronic devices. In this context, a hybrid energy system combines the potential of more than one RES, i.e. wind/solar/hydro-power or even biomass, while the utilization of geothermal and wave energy is also expected in the near future. The main advantages of RES-based hybrid energy systems include the following: • • •

•

•

Increased reliability of the hybrid energy installation, since it is based on more than one electricity generation source. Reduction of the energy storage capacity, especially in cases where the different RES utilized present complementary behaviour. Limited operation and maintenance (O&M) cost, especially in cases where the installation of photovoltaic (PV) panels replaces classic energy storage devices, such as the lead–acid batteries. Optimum environmental behaviour, especially in cases where the hybrid energy system does not use any fossil fuel (exclusively RES-based hybrid energy systems). Minimum levelized life-cycle electricity generation cost, not dependent on the fossil fuels price time evolution, especially in cases where the hybrid energy system is based on optimum design techniques.

On the other hand, the hybrid energy systems also present some disadvantages: •

•

In most cases the hybrid energy system is over-sized, since the system designers try to make each system component able to cover the load demand without the contribution of the other participating energy sources. This aspect can, however, be resolved by using new sizing algorithms. The first installation cost is rather high, although the long-term cost is normally low. This high installation cost discourages some potential investors.


Overview of stand-alone and hybrid wind energy systems •

•

9

The application of different technologies introduces a degree of complication (especially in the electronic control devices and in the O&M procedures) to the stand-alone installation, a serious problem especially for remote consumers. The introduction of thermal units (e.g. diesel-electric generators) as well as the utilization of batteries are both related to environmental impacts, thus decreasing the environmentally friendly attributes of these RESbased systems.

On the basis of the existing information in the international literature one may find several wind-based hybrid energy configurations, e.g.: • • • • • • •

wind–diesel systems; wind–PV-based systems; wind–hydro installations; wind–biomass-based installation; wind–PV and diesel-based systems; wind–hydro and diesel-based installations; wind–hydrogen/fuel cell hybrid energy systems.

Similar power stations are able to cover the electricity needs of single remote consumers up to isolated villages and remote islands, with minimum fossil fuel consumption. Accordingly, a typical wind-based stand-alone hybrid energy system (see Fig. 1.3), is based on the following:

UPS AC/DC rectifier

Charge controller

Inverter Control panel

Wind turbine

PV array/ small hydro/ small biomass thermal power station

Battery bank

1.3 Typical hybrid wind-based stand-alone system.


AC load

10


• One or more (usually small) wind converters of No kW. • A PV array of z panels (N+ maximum/peak power of every panel) properly connected to feed the charge controller with the voltage and the power required, or a small hydro turbine able to meet the remote consumer load demand, or even a small thermal power station based either on biomass (biogas or biofuel) or consuming fossil fuels. • An appropriate energy storage device, e.g. a lead–acid battery storage array, able to guarantee ho hours of autonomy, or equivalently with energy storage capacity Qmax and maximum discharge capacity Qmin. • An AC/DC rectifier of Nr kW in case that the energy storage installation operates on DC current. • A charge controller of Nc kW. • An optional UPS of Np kW in order to guarantee high-quality AC electricity generation. • A DC/AC inverter of Np kW, in the case of AC load demand. During the long-term operation of a typical wind-based stand-alone hybrid energy system (a wind–PV system is selected here as the working example), the following situations may appear: •

The power demand ND of the consumption is less than the power output (including any transformation losses) NW of the wind turbine (NW > ND). In that case the energy surplus (ΔN = NW − ND) is stored via the rectifier and the battery charge controller along with the energy production of the PV generator, NPV. If the battery is full (Q = Qmax), the residual energy is forwarded to low-priority loads. • The power demand is greater than the power output of the wind turbine (NW < ND) but less than the sum of power (including any transformation losses) of the PV station and the wind converter, i.e. NW + NPV > ND. In this case the remaining load demand is covered by the PV station via the DC/AC inverter. Any energy surplus from the PV station is stored in the battery via the charge controller. If the battery is full (Q = Qmax), the residual energy is forwarded again to low-priority loads. • The power demand is greater than the power output of the two renewable stations, i.e. NW + NPV < ND, where NW + NPV ≠ 0. In similar situations the energy deficit (ΔN = ND − (NW + NPV)) is covered (along with the corresponding losses) by the batteries via the DC/DC controller and the DC/AC inverter. During this operational condition, special emphasis is laid on the management plan of the three electricity production subsystems. • There is no renewable energy production (e.g. low wind speed, machine not available and zero solar irradiance), i.e. NW + NPV = 0. In that case, all the energy demand is covered by the battery–DC/DC controller–DC/ AC inverter subsystem under the condition that Q > Qmin. In this case



11

and the previous one, when the battery capacity is near the bottom limit, an electricity demand management plan should be applied, otherwise the load would be rejected. Recapitulating, the scope of a stand-alone system is to meet the electricity demand of a remote consumer at a rational cost and under a given loss of load (or reliability level) constraint. Depending on the importance and on the installation to be served one may demand no-load rejection (i.e. the load should be fulfilled at any case) operation or may permit a maximum (pre-defined) number of hours without electricity load coverage.

1.4

Energy storage opportunities of stand-alone hybrid energy systems

As already mentioned, interest in the use of wind energy has grown significantly over the past years, mainly as a reaction to concerns about the environmental impact from the use of fossil and nuclear fuels, along with oil and natural gas price instability in the international market. In contrast, renewable energy sources and especially wind energy have demonstrated their independence from economic fluctuations (Kaldellis and Zervos, 2002), while in most cases an initial cost reduction over the course of time is encountered. However, because of the stochastic behaviour of wind, wind generation cannot provide firm capacity to an electrical power system (Kaldellis, 2008b). Additionally, these fluctuations can – in some cases – cause problems to a distribution network related to stability, harmonics or flicker. Such issues pose serious obstacles to the extensive establishment of windonly power systems for the electrification of remote consumers and small (weak) power grids (Kaldellis, 2001). However, an energy storage system, when sized appropriately, can match (see Fig. 1.4) the stochastic wind power production to a generally variable and hardly predictable system demand, greatly limiting the energy production cost (e.g. generating capacity savings). In this context, the vast majority of stand-alone and hybrid energy systems use several energy storage devices in order to store wind energy during high wind and low consumption periods and provide electrical energy during low wind and high load demand periods. The next chapter provides an overview of the main features of the most commonly applied energy storage solutions for wind-based stand-alone and hybrid energy systems, including among others: • •

lead-acid batteries; pumped hydro;


12

Stand-alone and hybrid wind energy systems Energy production (W h) Energy consumption (W h)

3500

Battery capacity (A h)

6000

Energy (W h)

2500

5000 2000 4000 1500 3000 1000

2000

500


7000

3000

1000

0

0 1

10 19 28 37 46 55 64 73 82 91 100 109 118 127 136 145 154 163

Hours (h)

1.4 Stand-alone wind-based power system energy balance for a high wind potential area. Energy storage impact.

• •

Compressed air energy storage (CAES); flywheels.

In this context, the main advantages of the incorporation of energy storage systems include: • •

• •

• • • • •

exploitation of otherwise wasted amounts of energy (e.g. rejected amounts of wind energy can be stored); increase of energy autonomy/independence and promotion of the distributed generation concept through maximum exploitation of the local RES potential; increased reliability of energy supply (since an extra power source is available); increased energy efficiency and reduced emissions through the optimum energy management of a given electricity system (e.g. operation of thermal units at their optimum point); elimination of peak demands and deferral of electricity capacity increase; higher utilization and decongestion of transmission lines; abatement of risk entailed by the fuel price volatility provided that RES potential is used efficiently with the contribution of storage; high quality of power delivered to end-users; reduced life-cycle electricity generation costs.

On the other hand, the main disadvantages of these systems are the following:



13

• • • •

high initial cost required in most cases; inherent transformation and other types of losses; considerably lower energy densities than fossil fuels; introduction of environmental (especially for bulk energy storage such as pumped-hydro) and safety concerns (e.g. toxic wastes in the case of certain battery types); • additional energy use in the first place in order to build/construct a new energy storage system/device (may, however, ameliorate an entire standalone system energy payback through maximum exploitation of RES energy production); • anticipation of advances in other scientific and technological fields required for some energy storage technologies to develop.

1.5

Applications of stand-alone and hybrid energy systems

Stand-alone hybrid energy systems can be used in several cases worldwide. One of the most common applications includes installations in remote and stony areas, where the corresponding grid connection cost is very high. Keep in mind that the minimum grid extension cost for low-voltage lines exceeds 10 000g per km of grid line, while this value may be quite higher in cases of difficult access situations. On top of these, there are numerous cases where technical constraints hinder remote consumers from connecting to an electrical network. This is the case for isolated small islands or installations existing in entirely remote locations, far from any electrical grid. In all these cases the solution adopted up to now is based on the utilization of small diesel-electric generators, consuming expensive and heavy polluting diesel oil. On the other hand, the installation of a properly sized wind-based hybrid energy system may fulfil energy demand, at the same time improving the living standards of the remote communities. In this context, some common applications of stand-alone wind-based hybrid energy systems may include telecommunication (T/C) stations, small desalination systems, water pumping installations, isolated farms, winter or summer shelters and small or grid-isolated communities (e.g. remote islands). Applicability of these types of systems is demonstrated by the discussion following, considering also some typical installations worldwide.

1.5.1 T/C stations Among the alternatives of electricity generation for T/C stations, T/C providers themselves (Motorola, 2007) identify wind energy as an energy solution of minimum operating expense and negligible environmental footprint,


14


1.5 Small wind turbine adjusted to the mast of a T/C relay.

suitable for coastal locations or hilly areas with appreciable wind potential. Small wind turbines may even be adjusted on the relay mast (Fig. 1.5) as a supplement to the diesel energy option, while wind-based stand-alone systems occupy comparatively larger wind turbines, installed near the mast area, able to minimize the fuel consumption of diesel generators used as back-up suppliers only. In any case given, however, a battery bank of the appropriate capacity is also necessary. Depending on the local area characteristics, reduction of the battery bank size may be achieved through the incorporation of a PV array. Introducing PV power to the system may complement wind energy generation while also eliminating oversizing of storage and further reducing oil fuel consumption, especially during the summer months, i.e. when the air-conditioning needs of the station increase. Energy efficiency programmes run by T/C companies, e.g. in Portugal, use wind micro-generating systems country-wide (Fig. 1.5), reducing fuel consumption and emissions by a considerable 15–20%, while in other cases the employment of higher power output wind turbines minimizes oil use and its impacts. For example, three very remote base stations were installed in Kenya in 2005, based on pilot wind–diesel hybrid energy systems. The systems consisted of a 7.5 kW turbine on a 24 m tower, sealed batteries and an inverter, with the results obtained showing excellent reliability and diesel fuel savings of 70–95% (Fig. 1.6). Other examples include the installations shown in Fig. 1.7 to 1.9. In Fig. 1.7, a similar but comparatively larger wind– diesel installation located at Osmussaar, Estonia, comprises a 30 kW wind turbine, two ordinary diesel generator sets of 32 kW each and a battery bank of 250 A h, while Fig. 1.8 and 1.9, show two wind–PV systems found



15

1.6 Remote cell phone base station at Laisamis, Kenya.

1.7 T/C station at Osmussaar, Estonia, powered by a wind-based system.

in Turkey, with the system of Fig. 1.8 incorporating two wind turbines of 5 kW each, PV panels of 4 kWp and an appropriate battery bank system.

1.5.2 Small desalination systems The scarcity of potable water resources often found in remote island regions may be resolved by wind-based installations, provided of course that wind conditions in the area of interest are favourable. Generally speaking


16


1.8 T/C station powered by a wind–PV based installation in Turkey.

1.9 T/C station powered by a wind–PV based installation at Cesme-Izmir, Turkey.

however, coastal areas usually have considerable wind potential, thus, the purpose of applying wind energy solutions becomes two-fold, serving both the electricity and the potable water needs of these remote consumers. There are several studies (Habali and Saleh, 1994; Kiranoudis et al., 1997;



17

Miranda and Infield, 2003) regarding the implementation of wind energy in both seawater and brackish water desalination plants of membrane and distillation technologies, with special interest laid on the techniques of reverse osmosis first – being arguably the most efficient (Tzen and Morris, 2003) – and mechanical vapour compression secondly. Note, however, that as underlined in Tzen et al. (2002), non-steady power supply, i.e. a common defect of wind energy in stand-alone units, forces desalination plants to operate in non-optimal conditions, and therefore considerable energy storage capacity, usually batteries, is necessary to serve small-scale stand-alone systems. Information on several applications and studies may be obtained from Tzen and Morris (2003) and Kalogirou (2005), while the contribution of PV power has a sound effect on these types of wind-based hybrid energy systems as well (Petersen et al., 1979). A typical installation is the wind-driven desalination plant on the island of Rugen, Germany, operating since 1995 (Fig. 1.10). Based on the technique of reverse osmosis, the average daily production amounts to approximately 13 m3 of drinking water, from seawater with a salt concentration of 36 g/kg and with a mean annual wind speed of 7 m/s, using a 300 kW wind turbine. Some additional examples of wind-based installations are given in the following:

1.10 Wind-driven desalination plant on the island of Rugen, using a Tacke TW 300 wind turbine and a pressurized evaporation plant.


18 •

• • •

Stand-alone and hybrid wind energy systems A seawater desalination plant at Pozo Izquierdo, Gran Canaria (Spain) with a potential for a production of 50 m3/d, based on the technique of mechanical vapour compression and the operation of a 230 kW wind turbine (Ehmann and Cendagorta, 1996). The Syros island (Greece) seawater plant, using reverse osmosis and a 500 kW wind turbine, able to produce 900 m3 on a daily basis. A seawater reverse osmosis unit on the island of Drenec, France, driven by a wind turbine of 10 kW rated power (Peral et al., 1991). Two reserve osmosis desalination plants supplied by a 6 kW wind energy converter and a 2.5 kW solar generator in Mexico (Petersen et al., 1979).

Of special interest is the concept of a floating wind-based desalination plant, already in operation in the area of the Aegean Sea, Cyclades, Greece. The system is based on the energy production of a 30 kW wind turbine and is able to produce potable water at the rate of 70 m3/day while its main advantages are the exploitation of high-quality sea wind potential and the ability to serve vicinal island regions, as described in detail in Chapter 15.

1.5.3 Water pumping According to Smulders (1996), wind water pumping embraces a number of potential applications, including domestic water supply, community water supply, cattle watering and irrigation. Several wind water pumping installations may be encountered in remote areas (e.g. in isolated farms, see Fig. 1.11) where infrastructure is poor and water supply is used to cover additional needs, on top of domestic ones. Actually, the importance of serving the water needs of remote communities is well illustrated by the fact that even though there is increasing water consumption in both the domestic and the industrial sector, agriculture – especially in the developing countries (Mohsen and Akash, 1998; Sadrul Islam et al., 2000) – is still the dominant water user, absorbing almost three-quarters of the global water resources (IWMI, 2006). In this context, national programmes such as the one of India (Purohit, 2007), although not achieving the targets expected (initial estimations of 400 000 installations in India) have promoted the use of wind pumps throughout the developing world and have led to the development of small-scale markets for multi-bladed and low-rated speed wind turbines. Given the complementarity, however, between increased water needs and high solar potential available during the summer months, a shift has been noted during the recent years to the PV pumping concept, encouraged also by the gradual cost reduction of contemporary PV modules. Nevertheless, as in previous applications, wind-based hybrid energy systems incorporating



19

1.11 Wind turbine used for water pumping in an off-grid farm.

1.12 Wind-based hybrid water pumping unit with the incorporation of PV panels.

PV power as well are also an option (Fig 1.12). A similar pilot hybrid energy system is operated by the Soft Energy Applications & Environmental Protection laboratory in Greece (Fig. 1.13), where a 2 kW wind turbine along with 610 Wp of PV power and an appropriate lead–acid battery bank


20


Solar collector

PV panels Wind turbine

Lamps Data logger stylitis-41

Charge controller

Control panel Water pump

PC Battery bank

Water reservoir

24V DC circuit Data circuit Water circuit

1.13 Experimental hybrid wind-based stand-alone unit in the Soft Energy Applications and Environmental Protection Lab.

are able of elevating water quantity of 23 m3/day from a ground depth of 30 m (Kaldellis et al., 2009b). More details on wind water pumping installations may be found in Chapter 9 as well as in several studies, e.g. Smulders and de Jongh (1994).

1.5.4 Domestic to community level electrification Apart from the special applications presented in Sections 1.5.1–1.5.3, windbased stand-alone systems may apply to satisfy domestic electrification demands, from the level of an isolated farm or a winter/summer shelter to the level of an entire remote community that do not benefit from an electricity grid. In all these cases, the presence of the appropriate energy storage system is essential to ensure energy autonomy and minimize any dependence on fuel imports. In this context, as already implied, the existence of



21

numerous energy storage technologies (from small to large scale) enables the application of most systems; nevertheless detailed study of all problem parameters must be undertaken in order to select the most appropriate solution. Local demand patterns, energy storage characteristics, local RES energy potential, and the possibility of the implementation of energy management plans and cost issues should all be considered when designing a wind-based stand-alone system. In most cases, single consumer needs are satisfied by the incorporation of a typical battery system (e.g. lead–acid batteries), the back-up use of a diesel generator and the possibility of introducing PV panels as well; while to serve a remote community, larger-scale energy storage technologies may be used, such as pumped hydro, hydrogen storage and fuel cells, etc., often supported by the contribution of local thermal power stations (Kaldellis and Zafirakis, 2007; Kaldellis, 2008a). Of special interest are the isolated communities of remote island areas where either the absence of an electrical grid or the existence of a weak micro-grid forces the system to operate on the use of dominant oil power, which allows a minimum contribution of RES power, and wind power in pacticular. To overcome these problems at both the micro-level of a single consumer (isolated farms, winter/summer shelters) and the level of an entire remote community, constant research and development of windbased stand-alone systems is necessary. Relative to this, one may encounter several research studies addressing the problem of energy satisfaction on the basis of the local wind potential (Bueno and Carta, 2006; McDowall, 2006; Kaldellis et al., 2006b; Zafirakis and Kaldellis, 2009), while some typical applications are given in the following. Figure 1.14 shows two wind-based hybrid energy systems used to cover the electricity needs of two small remote villages in Chile. The first system in Isla Tac Village consists of 2 × 7 kW wind turbines, flooded batteries, 2 × 4.5 kW inverters and one 16 kW back-up gas generator, while the second system (Villa Las Araucarias) is based on a 7 kW wind turbine, a battery of 33.6 kW h capacity, a 4.5 kW inverter and a 4.5 kW back-up gas generator. Figures 1.15 and 1.16 show two wind–PV–diesel systems, the first in Mexico and the second in Mongolia. The system of San Juanico in Mexico, supplying the local fishing community of 400 people, uses 70 kW of wind power, 17 kWp of PV power and an 80 kW diesel generator able to cover any RES deficit, even in the extreme case of a wind and solar power blackout. Relatively smaller is the system of Fig. 1.16, located in Mongolia, where two wind turbines of 5 kW each are employed, in collaboration with PV panels of 1500 Wp and a diesel generator set of 10 kW. Note that in all these cases, three-bladed wind turbines are employed, opposite to the multibladed machines used for water pumping. On the other hand, wind energy


22

Stand-alone and hybrid wind energy systems (a)

(b)

1.14 Wind-based hybrid system covering the needs of the remote communities of (a) Isla Tac Village, Chile and (b) Villa Las Araucarias, Chile.



23

1.15 Wind-based hybrid system including PV power used for the electrification of a remote fishing community at San Juanico, Mexico.

1.16 Wind-based hybrid system including PV power used for the electrification of a remote fishing community in Mongolia.

surplus in remote island areas may be used for desalination purposes as well, such as in the case of the El Hierro island project, where the 100% RES plan based on the implementation of wind pumped-hydro system will exploit any wind energy surplus for seawater desalination as well.


24


1.17 Wind-solar hybrid street lamps for city lighting in China.

Finally, applications on the community level may also include other types of stand-alone wind-based systems, such as the city road lighting system in China (Fig. 1.17) where wind–solar hybrid street lamps use a 400 W smallscale wind turbine, PV cells of 150 Wp and a battery of 200 A h, gradually replace conventional street lighting with multiple environmental and financial gains.

1.6

The future of stand-alone hybrid energy systems

The continuous instability of fossil fuel prices, the forthcoming depletion of oil and natural gas reserves and the serious environmental degradation due to the over-exploitation of existing energy reserves are among the main reasons supporting the establishment of wind-based applications. On top of these, the continuous augmentation of the electrical power demand strongly questioning the reliability of large electrical networks, and the existence of considerable numbers of remote consumers claiming the coverage of their vital energy and clean water needs, certainly encourage further development of stand-alone hybrid energy power systems. Finally, the modular type of most hybrid power stations limits the corresponding initial capital to be invested while the life-cycle cost of the installation may be considerably reduced via an optimum sizing technique (Kaldellis and Kavadias, 2006, 2007; Kaldellis et al., 2006b).



25

In this context, one of the most important markets of the wind-based stand-alone systems is the one comprising more than 1 000 000 remote consumers and farmers of Europe and North. America (see, for example, Figs 1.18 and 1.19), who are either located far from the existing electrical grids

1.18 Wind-based hybrid system including PV panels, used for the electrification of a farm in Netherlands.

1.19 Small wind turbine used for the electrification of a farm in Kansas, USA.


26


or have no possibility of being connected with large power systems due to technical constraints. In the same category one may include the number of small communities, such as those in small remote islands, where the implementation of hybrid power stations may create several stand-alone micro-grids. The prospects of stand-alone wind-based hybrid energy systems are even more encouraging if one takes into consideration the fact that more than two billion people live in countries under development, where even small amounts of electricity could make a big difference in the living standards of local inhabitants. In this context, the proposed hybrid energy systems are able to at least face the fundamental needs of all these people on the basis of the existing wind and solar potential, requiring a minimum first installation cost. On top of these applications one should not disregard the continuous increasing demand for covering the electrical requirements of remote T/C stations or remote shelters, while similar wind-based hybrid power stations can contribute substantially to supporting small desalination and water pumping installations. Recapitulating, stand-alone wind-based hybrid energy systems are an attractive electricity generation solution in areas with high and medium– high wind potential and are able to provide a viable techno-economic answer for the problems related to oil price fluctuations, also supplying clean electricity to autonomous consumers and allowing them to be independent from the energy sector political conflicts. Finally, in terms of technology a similar hybrid power system has low maintenance needs, can be easily adapted to the specific consumer load profile and may respect the individual character of every specific community.

1.7

References

Bueno, C., Carta, J.A., 2006. Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable energy in the Canary Islands. Renewable and Sustainable Energy Reviews, 10, 312–340. Ehmann, H., Cendagorta, M., 1996. PRODESAL – Development and pilot operation of the first wind powered reverse osmosis seawater desalination plant. In: Mediterranean Conference on Renewable Energy Sources for Water Production, European Commission, EURORED Network, CRES, EDS, Santorini, Greece, 10–12 June. European Commission, 1999. Wind Energy. The Facts. A Plan for Action in Europe. Habali, S.M., Saleh, I.A., 1994. Design of stand-alone brackish water desalination wind energy system for Jordan. Solar Energy, 52, 525–532. Houghton, E.L., Brock, A.E., 1980. Aerodynamics for Engineering Students. Edward Arnold Ltd, London. IWMI (International Water Management Institute), 2006. Insights from the Comprehensive Assessment of Water Management in Agriculture. In: Stockholm World Water Week, Stockholm, Sweden.



27

Jensen, Th.L., 2000. Renewable energy on small islands. Forum for Energy & Development, Copenhagen, Denmark. Kaldellis, J.K., 2001. Evaluating the maximum wind energy penetration limit for weak electrical grids. In: European Wind Energy Conference, Bella Centre, Copenhagen, 2–6 July. Kaldellis, J.K., 2002. Minimum stand-alone wind power system cost solution for typical Aegean Sea islands. Wind Engineering Journal, 26, 241–255. Kaldellis, J.K., 2003. An integrated feasibility analysis of a stand-alone wind power system, including no-energy fulfillment cost. Wind Energy Journal, 6, 355–364. Kaldellis, J.K., 2004. Parametric investigation concerning dimensions of a standalone wind power system. Journal of Applied Energy, 77, 35–50. Kaldellis, J.K., 2008a. Integrated electrification solution for autonomous electrical networks on the basis of RES and energy storage configurations. Energy Conversion and Management Journal, 49, 3708–3720. Kaldellis, J.K., 2008b. Maximum wind potential exploitation in autonomous electrical networks on the basis of stochastic analysis. Journal of Wind Engineering & Industrial Aerodynamics, 9, 1412–1424. Kaldellis, J.K., Kavadias, K.A., 2001. Optimal wind-hydro solution for Aegean Sea islands electricity demand fulfillment. Journal of Applied Energy, 70, 333–354. Kaldellis, J.K., Kavadias, K.A., 2006. Optimum sizing of a stand-alone wind–diesel system on the basis of life cycle cost analysis. In: European Wind Energy Conference and Exhibition, Athens, Greece, 27 February–2 March. Kaldellis, J.K., Kavadias, K.A., 2007. Cost–benefit analysis of remote consumers’ electrification on the basis of hybrid wind–diesel power stations. Energy Policy Journal, 35, 1525–1538. Kaldellis, J.K., Zafirakis, D., 2007. Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency. Energy Journal, 32, 295–2305. Kaldellis, J.K., Zervos, A., 2002. Wind power: a sustainable energy solution for the world development. In: Energy–2002 International Conference, Athens, Greece. Kaldellis, J.K., Vlachou, D., Kavadias, K., 2001a. An integrated renewable energy solution for very small Aegean Sea islands. In: Renewable Energies for Islands – Towards 100% RES Supply International Conference, Chania, Greece, June 14–16. Kaldellis, J.K., Kavadias, K., Christinakis, E., 2001b. Evaluation of the wind-hydro energy solution for remote islands. Journal of Energy Conversion and Management, 42, 1105–1120. Kaldellis, J.K., Kavadias, K.A., Korbakis, G., Vlachou, D.S., 2004. The impact of local ambient conditions on the energy production of contemporary wind power stations. In: 7th Hellenic Conference in Meteorology, Climatology and Atmospheric Physics, Univ. of Cyprus, Nicosia, Cyprus, September 27–29. Kaldellis, J.K., Kostas, P., Filios, A., 2006a. Minimization of the energy storage requirements of a stand-alone wind power installation by means of photovoltaic panels. Wind Energy International Journal, 9, 383–397. Kaldellis, J.K., Kondili, E., Filios, A., 2006b. Sizing a hybrid wind–diesel stand-alone system on the basis of minimum long-term electricity production cost. Applied Energy Journal, 83, 1384–1403.


28


Kaldellis, J.K., Zafirakis, D., Kavadias, K., 2009a. Techno-economic comparison of energy storage systems for island autonomous electrical networks. Journal of Renewable and Sustainable Energy Reviews, 13, 378–392. Kaldellis, J.K., Spyropoulos, G.C., Kavadias, K.A., Koronaki, I.P., 2009b. Experimental validation of autonomous PV-based water pumping system optimum sizing. Renewable Energy Journal, 34, 1106–1113. Kalogirou, S.A., 2005. Seawater desalination using renewable energy sources. Progress in Energy and Combustion Science, 31, 242–281. Kiranoudis, C.T., Voros, N.G., Maroulis, Z.B., 1997. Wind energy exploitation for reverse osmosis desalination plants. Desalination, 109, 195–209. McDowall, J., 2006. Integrating energy storage with wind power in weak electricity grids. Journal of Power Sources, 162, 959–964. Miranda, M.S., Infield, D., 2003. A wind-powered seawater reverse osmosis system without batteries. Desalination, 153, 9–16. Mohsen, M.S., Akash, B.A., 1998. Potentials of wind energy development for water pumping in Jordan. Renewable Energy, 14, 441–446. Motorola, 2007. White Paper on the Alternatives for Powering Telecommunications Base Stations. Muselli, M., Notton, G., Louche, A., 1999. Design of hybrid-photovoltaic power generator, with optimization of energy management. Solar Energy Journal, 65, 143–157. Peral, A., Contreras, G.A., Navarro, T., 1991. IDM-Project: Results of one year’s operation. In: Seminar on New Technologies for the Use of RE Sources in Water Desalination, Athens, Greece, 26–28 September. Petersen, G., Fries, S., Mohn, J., Muller, A., 1979. Wind and solar powered reverse osmosis desalination units-description of two demonstration projects. Desalination, 31, 501–509. Purohit, P., 2007. Financial evaluation of renewable energy technologies for irrigation water pumping in India. Energy Policy, 35, 3134–3144. Sadrul Islam, A.K.M, Islam, M.Q., Hussain, S.S., 2000. Wind power utilization for water pumping in Bangladesh. In: World Renewable Energy Congress VI, Brighton, UK, 3–7 July. Smulders, P.T., 1996. Wind water pumping: the forgotten option. Energy for Sustainable Development, 2, 8–13. Smulders, P.T., de Jongh, J., 1994. Wind water pumping: status, prospects and barriers. Renewable Energy, 5, 587–594. Tzen, E., Morris, R., 2003. Renewable energy sources for desalination. Solar Energy, 75, 375–379. Tzen, E., Theofilloyianakos, D., Sigalas, M., Karamanis, K., 2002. Design and development of a hybrid autonomous system for seawater desalination. In: PV in Europe. From PV Technology to Energy Solutions Conference, Rome, Italy, 7–11 October. Vlachou, D., Messaritakis, G., Kaldellis, J.K., 1999. Presentation and energy production analysis of commercial wind turbines. In: European Wind Energy Conference and Exhibition, Nice, France, March 1–5. Zafirakis, D., Kaldellis, J.K., 2009. Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks. Energy Policy, 37, 1958–1969.


2 Overview of energy storage technologies for renewable energy systems D. P. ZAFIRAKIS, TEI of Piraeus, Greece

Abstract: This chapter presents a range of contemporary energy storage systems (ESSs). The introduction emphasizes distributed generation and renewable energy sources (RES), also designating the critical role of energy storage for further RES penetration. Topics such as the description of a typical ESS, the range of applications served by energy storage technologies, the presentation of each technology and a comparison of different systems, are all extensively discussed, along with a short description of future trends at the end of the chapter. Key words: distributed generation, renewable energy sources, energy storage.

2.1

Introduction

To satisfy the ever-increasing demand for electricity consumption (EIA, 2007), promote the protection of the environment (Stern, 2006; IPCC, 2007) and foster energy self-sustained communities (Scheer, 2006), constant research and development (R&D) into renewable energy sources (RES) technologies is required. Scarcity of fossil fuels, price volatility of oil and natural gas imports (Geman and Ohana, 2009) and the establishment of environmental policies via legislative measures (Soleille, 2006) prompt electricity production from RES, while plans concerning their exploitation on an international level (European Commission, 2001) are found in accordance with the shift attempted from conventional, fossil fuel-based electricity generation to cleaner and more sustainable power production methods (Little, 2005). In this context, distributed generation is at the centre of attention (Bayod-Rújula, 2009; Chicco and Mancarella, 2009), as if power generation has come full circle (Strachan, 2004), i.e. centralized power generation is gradually being abandoned so that cleaner and more agile systems can emerge. Higher energy efficiencies and the avoidance of transmission and distribution losses, enhanced flexibility of the local electricity networks and increased levels of reliability/security of supply, together with the fact that the problem of pollution is brought about to its actual extent, may be reckoned among the advantages of decentralized energy production (Pepermans et al., 2005). Restructuring of current power generation systems, however, requires the use of alternative energy technologies such as 29 © Woodhead Publishing Limited, 2010

30


combined cycle gas turbines, internal combustion engines, micro-turbines, Stirling engines, and RES (Ackermann et al., 2001), with past predictions claiming that by 2010, 60% of RES installations should be decentralized (Grubb, 1995). According to the above, sound stimuli do exist for the adoption of RES technologies on a broader scale, encompassing both the need for the introduction of more sustainable power production methods and the trend noted towards the re-establishment of the distributed generation concept. For RES technologies to meet the challenge, however, there are certain issues that must be addressed. Although comprising mature technologies with considerable progress over the years (Charters, 2001), RES are still on the sidelines of conventional generation methods. Acting as complementary power sources has allowed RES technologies to obscure some of their inherent handicaps. However, operation under the conditions set by fossil fuel plants and centralized generation networks condemns RES technologies to restrictions and limitations (Papathanassiou and Boulaxis, 2006; Georgilakis, 2008). As a result, impacts may be encountered on the economic efficiency of these systems (Kaldellis et al., 2004), also hindering the shift to alternative patterns of electricity generation previously discussed. For example, integration of wind energy, with wind turbines being admittedly the most mature RES technology, is one of the most common issues regarding further penetration of RES into various generation mixtures (Maddaloni et al., 2009). Fluctuating and/or intermittent wind energy production, owing to wind speed variability, is in most cases found to be unable to adjust to the profile of electricity demand, while the impacts (being more or less severe depending on the level of wind power penetration and the scale-characteristics of the electrical system examined) on power quality, power systems dynamics, transmission planning, etc. (Georgilakis, 2008) also reflect the need for developing new integration strategies. To confront the problems encountered at the level of both centralized generation networks and future distributed generation schemes, the idea of introducing energy storage constantly gains ground (IRES, 2006, 2007). Regarding large-scale conventional electricity networks, the requirement for an even geographical spreading of wind power to avoid overloading the network at certain points may be bypassed by the implementation of energy storage (mostly bulk systems) used to absorb the shock of wind energy overproduction and also fill the gap of energy production in times of low wind speeds or wind energy black-outs (Korpaas et al., 2003). The same is valid for RES-based distributed generation systems (either connected to the distribution network or isolated), where both power quality issues become more important – owing to the difficulty of small-scale networks to handle disturbances equally well – and the efficient management of RES energy production becomes critical in the absence of other major conven-


Overview of energy storage technologies for RES

31

tional power contributors (Weisser and Garcia, 2005). Finally, it must not be neglected that energy storage also applies at the remote consumer level where RES-based stand-alone systems are rather common for the satisfaction of electricity needs (Nema et al., 2009). In this context, the benefits stemming from the adoption of energy storage systems (ESSs) may be summarized as the exploitation of otherwise wasted amounts of energy (e.g. rejected amounts of wind energy can be stored), the increased reliability of energy supply (since an extra power source is available) and the improved operation of the power system and existing power units (e.g. operation of conventional units at optimum point). However, storage technologies are treated with some scepticism, arising from the high initial cost of the system and the inherent transformation losses. What should be stressed is that via the utilization of ESSs, maximum exploitation of RES is possible across the entire range of applications, i.e. from the remote consumer level (stand-alone systems such as wind-battery or photovoltaic (PV)-battery) to the service of large-scale wind parks, while the value of introducing energy storage in the various stages of the conventional electricity chain should also be acknowledged (Makansi and Abboud, 2002). More specifically, the utilization of ESSs allows base-load arbitrage (Bathurst and Strbac, 2003; Walawalkar et al., 2007) and abatement of risk entailed by the fuel price volatility (both owing to the proper design and operation of fossil fuel power stations since oversizing is not necessary), supports higher utilization and decongestion of the transmission lines (where reinforcement was previously required) (Cavallo, 2007), ensures the stability of the distribution system and provides high quality of power delivered to end-users (see also Fig. 2.1).

Challenges Low utilization

Congestion

Security

Dirty power

Generation

Transmission

Distribution

Services

Stability

Power quality

Fuel price

Volatility

Fuel

(t)

Energy storage Hedge risk

Baseload arbitrage Higher utilization Benefits

2.1 Benefits stemming from the adoption of energy storage (Makansi and Abboud, 2002).


32


Transition to RES and distribution generation

2.2 The critical role of energy storage; from central to distributed generation.

In conclusion, energy storage is faced with two coexisting challenges (Fig. 2.2): the first is to improve the operation of already existing conventional centralized power networks and the second is to signal the shift to the era of RES-based and distributed electricity generation. During this transition, ESSs should prove sufficiently flexible so as to serve both purposes and should most importantly designate the ability of RES technologies to overcome any inherent shortcomings. For this to be realized, the number of available technologies covering a broad range of applications comprises a critical factor itself. Contemporary technologies include pumped hydro storage (PHS), compressed air energy storage (CAES), fuel cells and hydrogen storage (FC-HS), flywheels, supercapacitors (SCs), superconducting magnetic energy storage (SMES) and various battery systems.

2.2

Description of a typical energy storage system (ESS)

Before proceeding to the presentation of each system in detail, some basic definitions concerning ESSs in general need to be established. More precisely, the main components of a typical ESS, its operating principles, energy flows and main characteristics, should all be discussed.

2.2.1 Description of a typical ESS’s main components Figure 2.3 shows a typical energy storage configuration comprising an energy source, either fuel- or RES-powered (or on the electrical grid), the


Overview of energy storage technologies for RES Energy source

Power conversion system

33

Data communication Energy flows

Control panel Source interface

PCSout

PCSin

AC interface

Energy demand

Energy storage

Load ESS

2.3 Typical energy storage configuration.

power conversion components (including the energy source and the AC load interface, as well as the main power conversion system (PCS)), the control devices and, finally, the energy storage media and demand side. The electricity generated (either DC or AC) passes through the necessary conversion stages (source interface and PCSin) in order to be stored mechanically, chemically, or in an appropriate form of electricity. Whenever an energy deficit appears on the demand side, the required amount of energy is drawn from storage in order, eventually, to be converted to AC (PCSout and AC interface). Note that the source and AC interfaces, along with the control devices and other auxiliary subsystems, are better known as balance of the system (BOS) components. In the case of other main devices participating in the energy storage configuration (e.g. water pumps, air compressors, gas turbines), common PCSs, meaning a rectifier and an inverter, can also be included in the BOS. In this context, the breakdown of the various system components that may be encountered in an energy storage installation is given in Table 2.1 (Butler et al., 2002). As already explained, the energy source may be either the electrical grid or a single/multiple number of power units, either interconnected or standalone, single-source or hybrid, having AC or DC outputs. However, since most electrical appliances require AC, it is common for the PCSout and load interface to feed consumption with AC rather than DC. In either case (whether for an AC or a DC interface), the equipment required for the source and load interfaces includes current and voltage sensors, fuses, isolation switches, transformers, filters and surge arrestors (among other items). There are four common types of PCS, divided according to their connection arrangement (Atcitty et al., 1998):


34


Table 2.1 Main components of a typical ESS (Butler et al., 2002) Energy storage system

Electromechanical Electrochemical Electrical

Interfaces to AC load and source

New lines Voltage transformers Protection devices

Power conversion system

AC switchgear/disconnect Rectifier/inverter DC switchgear/disconnect Protection devices

Auxiliary systems and accessories

Electrical parts (interconnects, protection devices, chargers) Mechanical parts (racking support, water/heating/air and fluid pumping systems, safety equipment, refrigeration systems, vacuum)

Monitors and controls

Monitors/diagnostics (storage media, power conversion, subsystems) Controls (storage media, protection devices, power conversion, subsystems)

Facilities

Foundation and structure Lighting Grounding/cabling Heating, ventilation, air-conditioning (HVAC)

• • • •

grid connected parallel configuration; grid connected series configuration; stand-alone parallel hybrid configuration; and stand-alone series hybrid configuration.

In the case of a grid parallel connection PCS, the ESS (e.g. electricity storage) is connected to the load in parallel with the utility/power source, via a bidirectional inverter (inverter/rectifier) and a transformer; a converter is optional, and a controller should also be considered. Grid connected series configurations are permanently on-line, and consist of a PCS that is used non-stop and which includes a rectifier, an inverter, an optional DC/DC or AC/DC converter and a bypass switch. The purpose of a PCS in a hybrid installation – usually including an oilbased generator, a RES installation (e.g. a wind turbine) and storage – is to serve the load through the coordination of all participating energy sources. Parallel connection means that the participating engine generator is directly connected to the AC load, while in a series connection the generator is first connected to the DC bus of the PCS via a rectifier. Furthermore, in a series connection, the load is primarily satisfied by storage, which is



35

charged whenever energy from the RES installation is available. An oilbased generator, on the other hand, feeds storage only when the maximum depth of discharge condition is violated (i.e. the electricity storage device has insufficient charge from RES generation to meet the operational load requirements). This is not the case for parallel connections, where either the engine generator remains off-line or operates at its optimum point, being supported by the operation of other energy sources and storage (for more information see Atcitty et al., 1998). In addition, controllers are used to manage the operation of all participating components and to ensure that the desired result is obtained. This is carried out by functions such as control of current supplied to the consumption, power management via voltage or power regulation, communication via signals with the various device drivers, and the simultaneous reading of several operation parameters (e.g. the storage system state of charge, charging and discharging rates, and many other parameters).

2.2.2 Operation principle and energy flows of a typical ESS

Peaks Mid merit

Response tr time

Without storage With storage Storage period tst

tdis Discharging period

Charging period tch

Charging

Frequency & voltage control via storage

Baseload

Electricity demand

The principle of operation of an ESS is the storage of energy during times of low energy demand (provided that an energy surplus is available), and the delivery of stored energy during times of high energy consumption, i.e. when energy production is not sufficient. A simplified profile of charging and discharging for a typical ESS is given in Fig. 2.4. According to the common patterns of energy demand, during late evening/night-time, consumption is kept at baseload levels and therefore any excess energy may be used to charge the ESS (charging period tch). Conversely, electrical loads

6am

Midday

period

6pm

Midnight

2.4 Energy generation management and frequency–voltage control through energy storage (based on Boyes, 2000; Makansi and Abboud, 2002).


36


gradually increase as day comes, and the entire energy production is used to cover demand (storage period tst for the ESS). Depending on its size and the destination of the ESS utilized, certain amounts of stored energy are used to supplement energy production (discharging period tdis) and cover parts of, or the entire energy deficit (meaning the extra electrical demand in comparison to either the baseload or the mid-merit levels) that appears as the electricity demand tends to peak (usually during the middle of the day and during the early evening). In this context, special attention must be given to the response time of energy storage tr, in order for scheduling to be accurate. The daily cycle is completed by the gradual reduction of electricity demand and the subsequent recharging of the storage unit, partly or entirely discharged during the previous time period. Management of the power injections used to regulate frequency and voltage are also among the duties of an ESS. This rather abstract description of how an ESS operates demonstrates that there are a variety of potential applications for the respective systems to be used in. Below, it will be shown that for the entire range of applications (from network scale to stand-alone, and from large-scale energy storage to voltage and frequency regulation) the qualities and characteristics of the various ESSs both vary and overlap. However, before proceeding to the presentation of the main characteristics and applications of ESSs, a short analysis of the energy flows in a typical ESS is necessary. Figure 2.5 shows the Sankey diagram of a typical ESS. The input energy delivered to the ESS during its charging phase is reduced owing to distribution and conversion losses. Distribution losses occur during the transfer of energy from the original energy source to the ESS, while conversion losses (usually the most critical losses) derive from

Energy storage system Conversion Self-discharge losses or idling Conversion losses losses Distribution losses

h=

W hout W hin

h ≈ hin · hout Distribution losses

Input engergy

ESS energy losses

Output energy

2.5 Energy flows in an ESS (based on Denholm and Kulcinski, 2003).



37

the conversion of electrical energy to the form of energy required to charge the ESS. Analogous to these are the losses during the discharging phase, i.e. when energy is drawn from the ESS and converted to electricity so as to feed the connected load. Additional losses, although minor in most cases, include self-discharge or idling losses, and take place during the standby or off-duty mode of the system. Usually, distribution losses are disregarded and it is the conversion and idling losses that are referred to as energy storage losses. The cycle efficiency during the charging–discharging cycle of an ESS, being one of its most common energy storage characteristics, is defined as the ratio of the ESS energy output to the ESS energy input, i.e. η = W hout /W hin. This, of course, takes into account all intermediate losses, and only by assuming that idling losses are negligible is η = ηin · ηout valid, with ηin being the charging stage efficiency and ηout the discharging stage efficiency. Otherwise, the self-discharge or idling losses (in the form of power losses Ploss) and the cyclic period T have to be taken into account, in order for the cycle efficiency to be estimated (Kondoh et al., 2000), i.e.

η=

ηout

−1

ηin −1 + T ⋅ Es ⋅ ( Eout ⋅ τ s )

where Es and Eout are the amount of stored energy and energy output respectively, while τs represents the Es/Ploss ratio.

2.2.3 Main characteristics of a typical ESS Apart from cycle energy efficiency, performance of an ESS is determined by a number of qualities and technical characteristics. A definition of the main characteristics used to evaluate and classify the various ESSs is provided below. Rated or available power, energy to power ratio The power rating of an ESS, meaning the size of the power conversion subsystems, usually results from the maximum power requirements of the electrical load on the generation side (the discharging part) and the most frequently appearing excess power on the input side (the charging part). Since energy is the product of time and average power (over a period of time), influence of the power drain on the energy storage capacity of the system is apparent, determining also the energy to power ratio, i.e. the amount of time the system can deliver full rated power. Similarly, the current and voltage requirements of the load and the energy source determine the size of the interface equipment at both the charging and the discharging side of the system.


38


Energy storage capacity, useful energy storage capacity The energy storage capacity is the actual parameter determining the size of storage, and it can be decided based on the power and autonomy period requirements as well as on the system’s efficiency and ability to perform deep discharging. Physical and cost constraints may keep the storage size below the initial theoretical estimations. Additionally, since 100% discharging is not usually an option, the term useful energy capacity is introduced, precisely to point out that part of the energy store cannot be used for electricity generation. Discharging time, reaction time, storage duration Discharging and reaction time comprise two very critical parameters for ESSs. Although the time of discharge (or autonomy) may be thought of as a dependent variable, interacting with the available energy storage capacity and the system rated power, reaction (or response) time is inherent to the system. ESSs with short reaction times (or ramping times) may be asked to provide electricity instantly (black start capability), while systems with comparatively longer reaction times only adjust to scheduled generation patterns that allow for a time interval between start-up and electricity production. Note also that the rate of discharge is directly related to the efficiency of recovering energy stores, i.e. exceeding a certain rate limit means that efficiency is considerably reduced. The opposite is valid in the case that the rate minimizes and the energy stores are left idle for long periods of time, i.e. discharge losses occurring decrease the available energy stores. This of course is directly related to another time parameter, which is the storage duration (or cyclic period), clearly configured by the system losses during off-duty periods. In case these losses are important, there is no rationale in leaving the system idle for long periods of time. Efficiency, energy ratio (ER) and energy payback There are several expressions used to evaluate the energy performance of an ESS, of which standard terms include cycle efficiency, round-trip efficiency, energy ratio (ER) and energy payback period. Cycle efficiency takes into account the ratio between the energy output and the energy input of the storage system, i.e. η = W hout/W hin, also including storage losses during standby mode. That means that a full cycle is considered: from charging to discharging. Round-trip efficiency (also known as ‘from AC to AC’ efficiency) on the other hand also includes transmission losses (see also Fig. 2.5), which are considerable in cases of bulk energy storage where it is



39

common for storage to be installed at a respectable distance from both the load and the energy source. ER suggests the exact inverse expression of the cycle efficiency ER = W hin /W hout, while when referring to the respective net ER, ERnet, transmission losses should also be taken into account (i.e. the respective ERtr), as in the case of the round-trip efficiency ERnet = ER · ERtr. Finally, the energy payback period suggesting an alternative evaluation standard compares the life-cycle (LC) electricity output of the energy storage installation with the respective energy required for its construction, installation, maintenance and decommissioning (LC embodied energy). Although from a system point of view it seems that the additional losses and the additional LC energy requirements introduced by the ESS increase the energy payback of the entire system (since there is no additional energy production by the ESS), the opposite may also be valid. More precisely, if instead of system energy production, the respective useful energy generation is used (Kaldellis et al., 2009a), i.e. any energy curtailments or wasted energy is excluded from the original energy payback estimation that does not consider the operation of an ESS, then it is possible for the energy payback to present a considerable reduction as soon as the ESS is added. Self-discharge, parasitic losses, ageing mechanisms, maximum depth of discharge Self-discharge, ageing mechanisms and depth of discharge suggest inherent characteristics of an ESS that strongly influence all other system parameters. As discussed earlier, losses occurring during the time that amounts of energy remain stored, namely self-discharge or idling losses, determine the maximum storage duration and thus delimit the system’s application range. Energy expenses are required to compensate for self-discharge losses and/ or sustain certain conditions of operation required for some ESSs (such as maintaining a rather high or a rather low temperature environment, creating and maintaining vacuum, etc.). These energy expenses are known as parasitic losses and common examples include the trickle charging of batteries, where attention must be paid so that the rate of supplied energy does not overcome the respective self-discharge, leading to system overcharging. Ageing mechanism is a term commonly met in batteries and applies to any chemical or mechanical reason leading to system failure. These mechanisms may suggest long-term gradual degradation and/or abrupt causes that, along with stress factors, configure the system service period expectancy (Ruetschi, 2004). Also already addressed, the maximum depth of discharge, DODmax, determines the maximum exploitable energy storage capacity of a system in order for smooth operation to be guaranteed. Relative to this, the state


40


of charge, SOC, of the ESS should at all times exceed the respective minimum value, SOCmin, corresponding to the term 1 − DODmax, while it should be mentioned that both charging and discharging efficiency are considerably affected once certain SOC values are exceeded (Gergaud, 2002). Energy and power density Another set of parameters distinguishing ESSs involves energy and power density, expressed either in relation to the mass or the volume of the system. Power or energy density estimation is provided by the ratio of energy storage capacity or rated power to the system volume or mass. Specific system boundaries are critical and may be limited to storage media only, or expanded so as to include power conversion subsystems and source/load interfaces (Kondoh et al., 2000). In this context, floor space requirements are of critical interest for the installation of an ESS. The space to be occupied by the system may in certain cases be extremely large or, in others, the volume required may not be available (e.g. when natural caverns are used as storage). Alternatively, the term footprint requirements may be used (Butler et al., 2002), especially when space and volume requirements imply strong disorder of the local environment. Both energy and mass densities determine the portability requirements of a given storage device, for which, depending on its size and weight, transportation issues may prove very important (Rydh and Sandén, 2005a). Influence on the environment Although destined to support RES and increase energy efficiency, ESSs themselves have environmental impacts and also contribute towards greenhouse gas (GHG) emissions. Serious environmental considerations include disposal of toxic waste and chemical solutions (Morrow, 2001), disturbance of the local environment in case of considerable civil works required, conventional fuel use, maintenance of strong magnetic fields, and others. On top of these, embodied energy, as previously discussed, is also a matter of concern (Denholm and Kulcinski, 2004; Rydh and Sandén, 2005a), while considerable GHG emissions should also be expected, owing to the activities involved especially during construction or manufacturing stages. Note also that for certain systems (although not as significant), GHG emissions are produced during the operational period as well (Denholm and Kulcinski, 2004). Nevertheless, to obtain a solid result concerning the environmental performance of a given ESS, a comparison with other supplementary energy options should be carried out at all times, while the fact that potentially waste energy is recovered should also be considered (Weisser, 2007).



41

Lifetime, number of cycles, duty cycle requirements The lifetime (or service period) is either expressed in years under a certain cycling rate, or in number of cycles, where a cycle is the period during which the system is fully charged and discharged. The number of operating cycles and the lifetime of an ESS system strongly depend on the characteristics of discharge, meaning at which depth discharges are carried out, with deep discharges implying reduction of the system lifetime. Furthermore, distribution and periodicity are important characteristics of the duty cycle requirements. Based on the uniform distribution or not of cycling over time, the efficiency of various storage subsystems may be either positively or negatively influenced, while ageing mechanisms and stress factors previously addressed also play a vital role in the life time expectancy of an ESS (Ruetschi, 2004). System costs The capital cost of an ESS, CESS, comprises the capital cost of the storage device, CSTOR, the capital cost of the PCSs, CPCS (charging and discharging side), and the capital cost of the various BOS components, CBOS (Schoenung and Hassenzahl, 2003). The capital cost of the storage device is usually expressed in relation to its energy storage capacity (c/kW h), while the respective power conversion cost is expressed in relation to the PCS rated power (c/kW). Finally, the BOS component’s cost is either proportional to the system or given as a fixed value. As may easily be concluded, the capital cost, being directly dependent on the size of the ESS, is largely influenced by all previous parameters determining the system dimensions. Nevertheless, it must be underlined that, due to economies of scale, costs per kW h and costs per kW for the same ESS may present considerable variation depending on the system size. On the other hand, to obtain a common comparison basis for all ESSs, the introduction of the discharge efficiency (ESA, 2009a) and the maximum depth of discharge in the cost evaluation provides the cost per unit of useful energy, while by also considering the number of cycles, the cost per unit of useful energy and per cycle of operation may be given: CESS · (Eused · Ncycles)−1, where Eused = Estor · ηout · DODmax. For a detailed cost analysis, however, usually on a life-cycle basis, the fixed and variable maintenance and operation (M&O) costs of the system are also required (Kaldellis and Zafirakis, 2007; Kaldellis et al., 2009b). Other features In addition to the characteristics so far presented, other features of an ESS, although not easily quantified, can in certain cases be critical to its adoption.


42


In this context, parameters such as the maturity of the technology, its adaptation to the location of interest, any operational constraints and the supplier’s profile may prove crucial for the feasibility of the project. Some technologies may be considered mature while others are still in the development stage; depending on the local environment, energy status and socioeconomic standards of a given site, an ESS may just as easily prove suitable or inappropriate to install. Furthermore, the operational constraints of certain systems (e.g. thermal behaviour, landscape requirements, fuel supply) and the profile of the main suppliers (e.g. guarantees and service provided) may also affect decision making.

2.3

Application range of energy storage systems (ESSs): category of generation

As has already been seen, energy storage is faced with two challenges (Fig. 2.2): support of conventional centralized generation on the one hand and promotion of RES-based distributed generation on the other. To cope with these challenges, ESSs are increasingly expected to provide services to a number of applications. Based on both the flexibility of energy storage due to the numerous technologies available, and on the fact that technological developments in the field are ongoing, the grounds for application of energy storage are constantly expanding. Different classification areas for the various recognized applications include the following. Taking into account the power and energy requirements of the demand side, one may distinguish the areas of energy management, bridging power and power quality-reliability (or enhanced power quality) (ESA, 2009b). Energy management encompasses the applications concerned with harmonizing the energy generation and demand profiles, i.e. for demand to coincide with generation via the implementation of storage, and requires the use of bulk ESSs with considerable energy storage capacity, storage duration and discharge time. Bridging power, on the other hand, is less demanding as far as the capacity requirements are concerned and involves applications where the discharge duration is kept within a timescale of minutes. Finally, power quality and reliability applications refer to the provision of considerable power within periods of seconds, so as to ensure that power disturbances are eliminated. A second demarcation (Butler et al., 2002), according to the service area, includes generation, transmission and distribution and customer service (or energy service), whereas the renewable support is often disengaged from utility applications in order to emphasize the critical role of ESS in the promotion of RES. Besides, it should be noted that regardless of the identification of an ESS to serve a given application, multifunctional capabilities of ESSs, i.e. the ability of a system to serve more than a single purpose, should also be taken



43

Renewable energy support (grid connected, DG and stand-alone applications) ESS Transmission & distribution (system stability, voltage regulation, facility deferral) ESS

Generation category (rapid reserve, area & frequency control and commodity storage) ESS

ESS

ESS

Customer service (energy management, peak shaving, power quality & reliability)

2.6 Application areas of ESSs.

into account. Of the two classifications seen, classification of applications according to service purpose is the most commonly used. In this context, services provided by ESSs are summarized in Fig. 2.6, while definitions and classifications for each application are given below.

2.3.1 Rapid or spinning reserve or contingency reserve In order for utilities to compensate for the possible failure of a system power generator, common practice suggests the employment of thermal units as back-up power. This is achieved either by the operation of existing thermal units under their rated power, or by the installation of new back-up units (combustion turbines), dedicated to covering any energy deficit appearing. In both cases, thermal units and combustion turbines ought to operate in reserve mode, thus presenting increased fuel consumption and fast wear. To avoid the results of this tactic, energy storage may substitute thermal units and provide the required amounts of energy, on the presumption that the response time of the candidate ESS is analogous to the system expectations. Furthermore, an additional benefit that may accrue from the adoption of energy storage in that case is the potential reduction of generating capacity, since a certain number of generators may be allowed to operate at optimum point levels, i.e. fewer units are required for the same power output.


44


2.3.2 Area control and frequency responsive reserve Although large-scale networks have the ability to assimilate the imbalance between generation and load demand (based on the operation of many generators, as well as on the existence of excess energy areas from which energy can be drawn, and high-demand neighbouring areas to which excess energy may be directed towards), the same is not valid for small-scale, island-mode networks. In the absence of energy trade-off with vicinal network areas, the implementation of energy storage may prove quite useful by accepting any energy surplus and by covering any energy deficit, thus balancing energy generation with energy demand. On the other hand, load fluctuations exceeding given limitations in an interconnected system entail significant frequency changes that may result in the damaging of electrical appliances at the consumer end and utility equipment at the generation side. With the introduction of energy storage, the counterbalancing of load fluctuations and the regulation of frequency become possible.

2.3.3 Commodity storage or load levelling or arbitrage Commodity storage, or load levelling or arbitrage, is clearly one of the most important applications for ESSs. The satisfaction of peak demand has always been a major issue for electricity utilities. Such as in the case of spinning reserve, supplementary energy is usually provided by the operation of an extra combustion generator, determined by considerably low utilization rates and therefore increased costs of operation. During off-peak times, on the other hand, the operation of base-load thermal power units at a minimum given load implies the appearance of energy surplus in case that demand is lower. Using this energy surplus to charge an ESS and allow discharge during peak hours entails certain profits for utilities, depending, however, on the amount of energy provided and the contract terms agreed. Note also that according to the local network demand profile, commodity storage may involve cycling on both a diurnal and seasonal basis, while avoidance of installing extra generation capacity should also be taken into account.

2.4

Application range of energy storage systems (ESSs): category of transmission and distribution

2.4.1 Transmission system stability What actually happens during system instability periods is that the system generators fail to synchronize with the rest of the system. This means that there is a difference between the phase-angle of the generator and that of



45

the demand side which, if too large for the system to handle, may even cause the system to collapse. Since load disturbances are the original cause of transmission instabilities, smoothing the load via the use of energy storage may lead to the avoidance of similar events and thus ensure synchronous network operation.

2.4.2 Transmission voltage regulation To obtain uniform voltage across the entire transmission line, the injection of reactive power is necessary. Hence, to deal with voltage differences, capacitors are used that provide the required reactive power. Using an energy storage device, provision of reactive power is possible at all times (charging phase, discharging phase and standby mode), and thus it is common for a utility to employ an ESS for a certain purpose and also use it as a reactive power provider (i.e. multifunctional use of the ESS).

2.4.3 Transmission facility deferral Transmission facility deferral is another major application area for ESSs. Utilities, faced with a constant increase in electricity demand, are obliged to provide sufficient capacity of transmission lines. This, in turn, may result in the low utilization of new lines and transformers, making this solution cost-ineffective. Until the new capacity to be installed is close to the electricity demand of the area and the utilization rate is sufficient to justify the investment, transmission capacity may be replaced by the use of energy storage, serving the peak demand periods which would otherwise be responsible for the upgrading of transmission lines. Apart from deferring the upgrading of transmission facilities, the implementation of energy storage may also extend the life of already existing transmission lines, through the avoidance of overloading (i.e. avoiding the overheating of transmission lines which otherwise reduces the lines’ in-service lifetime). Finally, one side benefit not easily quantified is that, owing to the trade-off between the ESS and transmission lines, the utilization rate of the transmission network increases and thus greater utilization of the respective investment is also realized.

2.4.4 Distribution facility deferral As in the case of transmission deferral, the purchase and installation of new distribution equipment may be deferred until local electricity demand requirements match aggregate distribution capacity. In the meantime, ESSs may better adjust to the satisfaction of peak demands exceeding the existing distribution capacity. It should be noted that ESSs introduced into a system


46


for this reason are not necessarily retired when the new lines are installed; on the contrary, until such a time that the cumulative new capacity is also exceeded, and provided that the service period of the ESS has not yet ended, the system may be used for other utility purposes (i.e. multifunctional use of the ESS).

2.5

Application range of energy storage systems (ESSs): category of customer service

2.5.1 Energy management or peak shaving or demand charge reduction Energy management concerns the elimination of high load demand in order for utility customers to avoid the imposition of monthly utility fees concerned with the highest peak. Peak shaving via the implementation of energy storage is used in order to prevent the appearance of a high peak that leads to the imposition of analogous fees. An ESS is employed onsite, and is kept in charging and standby mode during the interval between two consecutive peaks; attention should be paid to the system sizing, so as to adjust to the given demand profile and cope with any forthcoming peak.

2.5.2 Power quality and reliability Harmonic distortions, voltage sags, spikes and failures may cause serious problems to numerous vulnerable electronic devices. In order for customers to protect this kind of device from similar phenomena, an appropriate ESS may be used to replace the grid power supply until the power quality of the supply network is restored.

2.5.3 Renewable energy Integration with RES is clearly the most important challenge facing ESSs at the moment. As has already been seen, a shift to RES-based distributed generation, operation of stand-alone systems and the establishment of RES technologies as baseload units in conventional power generation reflect the wide range of applications for ESSs. In this context, the role of ESSs is twofold. On the one hand, energy storage may be used for power firming, and on the other hand it may be used to match RES production with peak demand. In the first case, the provision of firm power capacity through energy storage is achieved by the provision of supplementary power by the ESS whenever RES production drops below the guaranteed power output. In this way the incorporation of an energy system equates RES technologies with other power generation technologies, and thus eliminates the



47

inherent disadvantage of stochastic energy generation. Additionally, by using energy storage, any excess RES energy production on a diurnal basis may be used to cover the respective peak demand and thus take advantage of the higher rates offered by the utilities. Finally, the use of ESSs in standalone applications may be further expanded, since developments and cost reduction in both fields of RES technologies and energy storage may eventually lead to the abandonment of diesel hybrid schemes, in areas where the RES potential is considerable.

2.6

Application range of energy storage systems (ESSs): requirements of electricity applications

Figures 2.7 and 2.8 list the requirements for a number of different applications concerning main characteristics that candidate ESSs need to cope with. More specifically, Fig. 2.7 shows the power output and discharge time requirements of ten different applications, also providing a preliminary estimation of the system energy storage capacities. As can be seen from the figure, the applications of commodity storage, rapid reserve, area control and frequency responsive reserve, and transmission system stability demand the highest power output. Among these, commodity storage requires considerably higher discharge duration, i.e. significant energy storage capacity, 1000 10 h

Commodity storage Distribution deferral

Discharge duration (min)

100

Customer energy management

10

Transmission deferral

Rapid reserve

Transmission voltage regulation

Area control & frequency responsive reserve

1h

Renewable energy management 1 min

1

0.1 Power quality & reliability Transmission system stability

0.01

1s

100 ms 0.001 10 kW

100 kW

1 MW

10 MW

100 MW

Power output

2.7 Power output and discharge period requirements of electricity applications (based on Butler et al., 2002).


48


>1000 Area control & frequency responsive reserve(LP)

Annual duty cycle requirements

1000

HP: high portability importance LP: low portability importance NP: negligible portability importance VP: variable portability importance

Transmission system stability(LP) Transmission voltage regulation(HP) Commodity storage(NP)

100

Transmission deferral(HP) Distribution deferral(HP)

Rapid reserve(LP)

10

1

Power quality & reliability(HP) Customer energy management(VP) Renewable energy management(HP)

Low

Medium

High

Floor space (importance)

2.8 Duty cycle, portability and space requirements of electricity applications (based on Butler et al., 2002).

while transmission system stability requires the employment of ESSs with fast response and rather short discharge times (of the scale of milliseconds to seconds). Distribution and transmission deferral are both described by respectable discharge duration, while in the case of distribution, less power is required. Customer energy management and transmission voltage regulation present similar discharge time requirements (tens of minutes) with voltage regulation presuming power ratings at the level of transmission system stability, and customer energy management ranging from a few kilowatts to the megawatt scale. Power quality and reliability applications are found in the same scale of power (i.e. from a few kilowatts to the megawatt scale) but correspond to discharge duration at the scale of milliseconds to tens of seconds. Finally, it is interesting to see the broad range of applications introduced with the support of RES technologies, reflecting the challenge with which ESSs are faced. In Fig. 2.8, the various applications are examined with regards to duty cycle, floor space and portability requirements. Area control and frequencyresponsive reserve applications require the ESS to charge and discharge thousands of times. On the other hand, issues of portability and especially of floor area are not of great importance. The opposite is true for the major-



49

Table 2.2 Alternatives of ESS for common electrical applications (Butler et al., 2002) Application

Technology & non-technology alternatives

Rapid reserve

Centralized: thermal fossil, combustion gas turbines, small diesel generators, spot purchases Distributed: diesel generators, microturbines

Area control & frequencyresponsive reserve

Intermediate cycling and peaking plants, combustion turbines

Commodity storage

Flexible AC transmission system, cogeneration

Transmission system stability

Auto-transformer, flexible AC transmission system

Transmission voltage regulation

Capacitor banks

Transmission facility deferral

Diesel generators, oil coolers for transformers, superconducting cables

Distribution facility deferral

Diesel generators, oil coolers for transformers, superconducting cables

Energy management

Thermal storage (passive solar, chiller), diesel generators, microturbines

Power quality & reliability

Diesel generators, static and rotary uninterruptible power supply (UPS), dynamic voltage restorers

Renewable energy management

Diesel generators

ity of applications, where both portability and footprint issues should be considered. Less frequent cycling is required for rapid reserve applications, while only in the case of commodity storage can portability be granted as negligible. Finally, what is interesting to see is the systems and technologies currently used to serve the numerous electrical applications presented; put in a different way, there is a wide range of systems and technologies that ESSs have to compete with to establish their role in electricity markets (Table 2.2).

2.7

Contemporary energy storage systems (ESSs)

ESSs used for electricity generation purposes are usually classified according to their form of energy storage. In this context, there are three main categories that should be considered:


50 • • •

Stand-alone and hybrid wind energy systems mechanical storage, including flywheels, PHS and CAES; chemical storage, including all batteries, flow batteries and FC-HS; electrical storage, including SCs and SMES.

2.8

Mechanical energy storage

2.8.1 PHS PHS systems should be considered as the most mature bulk energy storage technology with more than 100 GW of installed capacity worldwide (ASCE, 1993). In a PHS system, energy surplus appearing in times of low demand, either deriving from the electrical grid or any given generation unit (such as a wind park), is exploited to pump water into an elevated (upper) storage reservoir (see Fig. 2.9). During peak demand, water is released from the upper reservoir and water turbines operate to ‘feed’ a connected electric generator. As a result, the system is able to cover an existing power deficit by using the appropriate amount of previously stored energy. In another version, water turbines can be replaced by reversible hydraulic machines working either way (in pumping and turbine mode), often supported by an independent pump unit (Fig. 2.9). Operating two water pipes is, in many cases, thought to be unnecessary; nevertheless, parallel operation (with simultaneous storage and generation) is also an option. The cycle efficiency of a typical PHS ranges between 65% and 77% (Papantonis, 1995), while the main drawback of such systems is their high capital cost, directly related to the need for the construction of at least two reservoirs, preferably close to consumption. If natural reservoirs are

2

5 7 6

3

H 4 P

1

1. Wind park 2. Electricity grid 3. Lower reservoir 4. Pumping system 5. Upper reservoir 6. Reversible hydr. machines 7. Electricity consumption

2.9 Pumped hydro storage configuration.



51

employed instead, the feasibility of the project is in most cases guaranteed, while large-scale projects are usually more attractive (Bindner, 1999). Lack of suitable sites, on the other hand, sets some serious restrictions, and environmental impacts caused to the surroundings during installation are also a matter of concern. With this in mind, the open sea (Fujihara et al., 1998) and underground caverns (Chen and Berman, 1981) may serve equally well as lower reservoirs. Such systems are able to take up load within a few minutes, and are determined by a high rate of extracted energy, while PHSs in general are suitable for energy management, spinning reserve and frequency control applications. As a result, pumped hydro is thought to be essential for the support of wind parks operating in island mode networks (Bueno and Carta, 2006; Kaldellis et al., 2006; Anagnostopoulos and Papantonis, 2008; Katsaprakakis et al., 2008), where wind energy curtailments are considerable.

2.8.2 CAES In a CAES system (see Fig. 2.10), off-peak power is taken from the grid or other generation source and is used to pressurize air into an underground cavern (with air pressures reaching 80 bars). During times of peak demand, the required amount of air is released from the cavern, heated with natural gas and then supplied in the form of gases to a gas turbine where expansion takes place, as in a typical Brayton/Joule cycle. This is actually the main benefit of a CAES system: the fact that the stages of compression and generation are separated from one another. Consequently, what can be as much

11 10

T

C

M 2

7

3 5 P/H 4

1

6 C.C.

G

1. Wind park 8 2. Motor 3. Air compressor 4. Air storage cavern 9 5. Preheater 6. Combustion chamber 7. Gas turbine 8. Generator 9. Natural gas tank 10. Electricity grid 11. Electricity consumption

2.10 Compressed air energy storage configuration.


52


as 66% of fuel consumption needed to drive the compressor in a typical Brayton/Joule cycle, is not needed in the case of a CAES cycle. As a result, in a CAES system, the entire power of the gas turbine is available for consumption. In this context, during a charging/discharging cycle, 1 kW h of generated electricity requires approximately 0.75 kW h of electricity for the compressor and 4500 kJ of fuel for combustion (Denholm and Kulcinski, 2004). The amount of fuel required is the main subject of controversy over the unconditional acceptance of such systems. In an effort to disengage CAES from the natural gas factor, one concept supports the use of biofuels (Denholm, 2006), while another interesting approach is the socalled ‘advanced adiabatic CAES’ where no fuel is used (Bullough et al., 2004). CAES, like PHS, demands favourable sites and geological formations, suitable for underground storage. The storage media most commonly used are rock caverns, salt caverns, porous media reservoirs and buried pipes for small subsurface CAES units (Bradshaw, 2000; Dayan et al., 2004). In terms of energy capacity, CAES is thought to be the only reliable alternative option for PHS. Since the losses recognized are not appreciable, the storage period is considerable. Among the advantages of CAES are its fast ramp rate (two to three times faster than conventional units), its stable heat rate at low capacity, and the considerably lower emissions (compared with simple and combined cycle units) (Bradshaw, 2000). Note that because of their potential to operate at partial load with satisfying fuel consumption, CAES systems are more suitable for load control applications. Additionally, the flexibility of CAES systems to serve as both base load plants (Cavallo, 1997; Denholm, 2006; Greenblatt et al., 2007) and peak following units (Lund et al., 2009) provides considerable opportunities for improved management of wind energy generation (Cavallo, 1997; Denholm, 2006; Cavallo, 2007; Salgi and Lund, 2008).

2.8.3 Flywheels By adjusting any voltage sags and surges as well as instantaneous interruptions, flywheels (Hull, 2004; Suzuki et al., 2005; Bolund et al., 2007) aim mainly to ensure short duration power quality as well as to provide a reliable option for UPS applications. In this context, the use of flywheels for wind power regulation is a common application for such systems (Davies et al., 1988; Infield, 1994; Carrillo et al., 2009). In a flywheel ESS (Fig. 2.11), kinetic energy is stored by causing a disk or rotor to spin on its axis. When short-term back-up power is required, the flywheel takes advantage of the rotor’s inertia and the kinetic energy stored is converted into electricity. A modern flywheel consists of a rotating mass (a rim attached to a shaft) supported by bearings and connected to a motor/generator. During the motor’s


Overview of energy storage technologies for RES 5

4

6 3

53

2

1. RES and/or grid 2. Source interface ± rectifier 3. Vacuum pump 4. Flywheel (with motor/generator) 5. Inverter & AC interface 6. Electricity consumption

1

2.11 Flywheel energy storage configuration.

operation, electrical energy is provided to the stator and the torque produced increases the kinetic energy of the rotor. During discharge, the system operates in the opposite way. The amount of energy stored in a flywheel is directly proportional to the rotor’s mass moment of inertia and the square of its rotational speed. Taking this into consideration, flywheels can be classified in two main categories: low-speed and high-speed. A high-speed flywheel is able to rotate at speeds greater than 100,000 rpm, therefore implying a significant increase of its energy density. To accomplish such rotational speeds, air drag and bearing losses should be minimized, while the rim’s tensile strength should be maximized. For the first goal to be achieved, the flywheel along with the motor/generator must be placed inside a vacuum chamber to avoid the deceleration effects caused by air (hence a vacuum pump is required; see Fig. 2.11), while the use of active magnetic, passive magnetic and superconducting magnetic bearings is recommended to avoid friction losses (Koshizuka, 2006). Some of the key features describing flywheels are their high power density, relatively low maintenance needs (considering that a flywheel consists of kinetic components), their high cycling rate, deep discharges and very high self-discharge rate.

2.9

Chemical energy storage

2.9.1 Battery energy storage Batteries are the most widely adopted energy storage technology, traditionally used in many RES-based stand-alone applications. The numerous battery types existing, each with its own special characteristics, cover a wide range of applications, from power quality to energy management. The


54

Stand-alone and hybrid wind energy systems 4

3

1. RES and/or grid 2. Source interface ± rectifier 3. Battery bank 4. Inverter & AC interface 5. Electricity consumption

5 2

1

2.12 Battery energy storage configuration.

technologies considered here include ‘mature’ lead–acid (L/A) and nickel– cadmium (Ni-Cd) batteries, and advanced sodium–sulphur (Na-S) as well as metal–air and lithium-ion (Li-ion) batteries, which have lately become commercially available. A typical battery system comprises a battery stack, where electrical energy is converted to chemical energy, and vice versa (battery cells connected in series and in parallel to obtain the desired levels of voltage and current output), the PCS, meaning the energy source and load interfaces together with an AC/DC rectifier on the production side and an inverter on the demand side, and, finally, the implementation of any control systems used to coordinate system operation (see also Fig. 2.12). Batteries are usually determined by their efficiency, depth of discharge, number of cycles, operating temperature, energy density and self-discharge, while one of their main technological advantages is the absence of kinetic parts limiting maintenance requirements. L/A L/A batteries comprise two electrodes, the negative made from lead and the positive made from lead dioxide, separated by an electrolyte (dilute H2SO4), assigned to electrically isolate the two electrodes and provide the sulphate ions for the discharge reactions. There are two main types of L/A batteries: flooded and valve regulated. Flooded batteries demand periodic maintenance (water refilling is required) and present moderate energy densities (∼25 W h/kg), whereas valve regulated batteries are maintenance free and are determined by higher energy density values (up to 50 W h/kg) and deeper discharges. On the other hand, the lifespan of flooded batteries



55

is much higher than that of the respective valve regulated (1000 and 300 cycles respectively); nevertheless, the service period in both cases is among the lowest for any energy storage. Overall, L/A batteries can be described as a mature technology with known performance characteristics and a reliable market background (Paul, 1994; Parker, 2001; Razelli, 2003; Perrin et al., 2005). Their self-discharge is considerable among batteries (though amongst the lowest in energy storage) while low maintenance requirements is one of their benefits. On the other hand, their low energy density, limited service period, environmentally unfriendly content and the recommended low depth of discharge are the drawbacks of this particular technology. Ni-Cd Ni-Cd batteries can also be considered a mature technology. The main structure of such batteries contains a nickel hydroxide positive electrode plate, a cadmium hydroxide negative electrode plate, a separator, and an alkaline electrolyte (usually KOH). Although their energy density is higher than that of corresponding L/A batteries, self-discharge is more significant. Deep discharges and a considerable service period, counterbalancing the respective high capital cost, are the technology’s most positive features; low efficiency rates and environmental concerns regarding cadmium toxicity (Rudnik and Nikiel, 2007) are the technology’s most negative features. Na-S In an Na-S battery the positive (liquid sulphur) and the negative (liquid sodium) electrode are separated by a solid beta-alumina ceramic electrolyte, allowing only the positive sodium ions to pass. During discharge, positive ions of sodium pass through the electrolyte and electrons flow in the external circuit of the battery producing voltage. When combined with sulphur, sodium ions form sodium polysulphides. The opposite process occurs during charging periods. For the battery to operate, however, a temperature of 300 °C is necessary, meaning that a heat supply should also be considered for a Na-S system. Because of the material’s high energy potential, Na-S batteries demonstrate proportional energy densities, both gravimetric and volumetric, while because of the existence of beta-alumina (with zero electron conductivity) there is no self-discharge. In addition, both efficiency and depth of discharge for such batteries are quite high (Oshima et al., 2004; Rydh and Sanden, 2005a,b; Wen et al., 2008); however, the use of Na-S may not be able to satisfy certain application requirements as the need to maintain high temperature levels sets a serious obstacle.


56


Li-ion In an Li-ion battery (Ritchie and Howard, 2006) the positive electrode is a lithiated metal oxide (LiCoO2, LiMO2) and the negative electrode is made of graphitic carbon. The electrolyte consists of lithium salts dissolved in organic carbonates. During the charging stage, the atoms of lithium in the cathode ionize. These ions move through the electrolyte to the negative electrode where they combine with external electrons and finally end up between the carbon layers as lithium atoms. The reverse process occurs during discharge. The main advantages of this technology are the high energy density with a potential for yet higher capacities, the high efficiency value (∼95%), and the respectable number of cycles combined with deep discharges (Rydh and Sanden, 2005a,b). Additional advantages include a low self-discharge rate and analogous maintenance needs. The limitations set at present are the required protection circuits to maintain voltage and current within safety limits, the immaturity of the technology and, most importantly, the capital cost. As a result, Li-ion batteries are currently used in small portable equipment (Megahed and Ebner, 1995) and are expected to expand their application range in the next few years. Metal–air batteries In metal–air batteries (Blurton and Sammells, 1979), common metals releasing electrons when oxidized, preferably of high energy density, are used for the anode (e.g. zinc or aluminium) (Chakkaravarthy et al., 1981), while for the cathode a porous carbon structure or metal mesh with a proper catalyst are typical. KOH in liquid form or a solid polymer membrane saturated with KOH is used as the electrolyte. Energy density of the scale of hundreds of W h/kg is typical of the technology; nevertheless, difficult recharging conditions limit the system performance to a maximum of 50% efficiency and to a maximum of a few hundred cycles for the system life time. Selfdischarge is negligible, while system cost is in the range of 100c/kW h.

2.9.2 Flow batteries Flow batteries are a relatively novel technology that stores energy by means of a reversible chemical reaction. The main characteristic determining the operation of such systems is that energy is stored in two liquid electrolyte solutions. Given this, energy capacity and system rated power are independent from one another. The storage capacity depends exclusively on the quantity of the electrolytes used, while the power rating is determined by the active area of the cell stack. The flow battery system shown in Fig. 2.13 is formed by a number of electrochemical cells, each one having two com-



5

1. RES and/or grid 2. Source interface ± rectifier 3. Electrolyte pumps 4. Electrolyte tanks 5. Electrochemical cells 6. Inverter & AC interface 7. Electricity consumption

6 4 4 3

57

7

3

2

1

2.13 Flow battery energy storage configuration.

partments (one for each electrolyte), being separated by an ion-exchange membrane. The two electrolytes are pumped from the tanks, through the cell stack and across the membrane. When passing through the membrane, the one electrolyte is oxidized and the other is reduced, producing current available to the external circuit. The pumps needed to circulate the electrolytes bring some losses while the use of aggressive chemical solutions is an additional concern. As already mentioned, the energy capacity of these systems depends on the size of the electrolytic tanks. Apparently, increasing the quantities of electrolytes used could lead to the service of large energy storage applications, theoretically in areas where only PHS and CAES may apply. Hence, novel flow battery technology may be encountered in a number of applications, also including the service of both large-scale and stand-alone RES-based installations (Joerissen et al., 2004; Dufo-López et al., 2009). Present technologies are determined by the electrolytes used and are described below. Vanadium redox battery In a vanadium redox battery (VRB), energy is stored by using vanadium redox couples (V2+/V3+ in the negative and V4+/V5+ in the positive half-cells) (Sum and Skyllas-Kazacos, 1985). The couples are stored in mild sulphuric acid solutions. During a charge–discharge cycle, H+ ions are exchanged


58


between the two electrolyte tanks, through a hydrogen-ion permeable polymer membrane. Some of the advantages that VRB holds are its high efficiency, deep discharges and service period that may even exceed 10,000 cycles (Lotspeich, 2002). Purchase costs show great variation, proportional to the given capacity, and appear quite low when referring to larger systems. Polysulphide bromide battery The polysulphide bromide battery (PSB) is a regenerative fuel cell technology that stores energy by means of a reversible electrochemical reaction between two salt solution electrolytes (sodium bromide and sodium polysulphide). The two electrolytes are placed close together inside a cell and remain separated by a polymer membrane. The membrane only allows positive sodium ions to pass, producing voltage across the membrane. The roundtrip efficiency is lower than in VRB (Divya and Østergaard, 2009; Lotspeich, 2002) and the energy density is around 20–30 W h/l. In standby mode, with the electrolytes charged, the system can respond in milliseconds, while the technology potential concerning lifetime and power-energy capacity extends to over 15 years and to a scale of tens of megawatt hours, respectively. Zinc–bromine battery Energy storage in zinc–bromine (Zn-Br) batteries (Singh and Jonshagen, 1991) is based on the reaction occurring between two common chemicals, zinc and bromine. The negative electrode is made up of zinc and the positive from bromine. The electrolyte is usually aqueous with zinc bromide salt dissolved in water. During charge, zinc is plated from the electrolyte solution onto the negative electrode surfaces, inside the cell stacks. The bromine produced on the positive electrodes surfaces is stored afterwards on the bottom of the positive electrolyte tank in the form of a chemically complex organic phase. When the Zn-Br battery is completely discharged, all the metallic zinc formed on the negative electrode surface is dissolved into the electrolyte and is again available for the next charge. Moderate cycle life combined with relatively high energy density (65–84 W h/kg), deep discharges and negligible self-discharge are the main system characteristics (Lotspeich, 2002).

2.9.3 Fuel cells and hydrogen storage (FC-HS) Fuel cells consist of two electrodes surrounding an electrolyte. Oxygen passes over one electrode and hydrogen over the other, generating electric-



59

ity, water and heat. In principle, a fuel cell operates like a battery. However, a fuel cell does not require recharging; as long as fuel is supplied to the cell, electricity is produced. Thus, the restrictions imposed on storage capacity are determined by the fuel tank size. The energy that a fuel cell produces is directly dependent on the fuel cell type, the operation temperature, and the catalyst used to improve the chemical reaction’s performance. In this context, the main characteristics of different fuel cell types are listed in Table 2.3 (EG&G, 2004), while a typical FC-HS configuration is shown in Fig. 2.14. The main disadvantage of this technology is the cycle efficiency which, by including the hydrogen production stage (electrolysis is currently considered), is estimated to be in the range of 30–40%. The losses are detected during the electrolysis to produce hydrogen, during the storage stage, and finally during the electricity generation process via the fuel cell. The fuel cell component alone, however, may for certain types reach efficiencies of 60% (in cases of high-temperature fuel cells, i.e. molten carbonate fuel cells (MCFC), solid oxide fuel cells (SOFC) or alkaline fuel cells (AFC)). Among the technology advantages that one may encounter are the high energy density due to the use of hydrogen, the low energy cost, the negligible selfdischarge and the wide range of applications, including interaction with RES (Bauen and Hart, 2000; Sørensen, 2000; Agbossou et al., 2001; Duic and Da Graça Carvalho, 2004; Chen et al., 2007). In fact, it has been argued that intermittent RES are the ideal power source for the production of hydrogen (Moriarty and Honnery, 2007).

1. RES and/or grid 2. Electrolysis unit 3. Compressor 4. Hydrogen storage 5. Fuel cell system 6. Electricity consumption

5

4

6 3 2 1

2.14 Fuel cell and hydrogen energy storage configuration.



Electrolyte

Polymer membrane

Potassium hydroxide (KOH)

Concentrated phosphoric acid

Melted Li/K carbonate

Solid oxide ceramic

Fuel cell type

Polymer electrolyte membrane (PEMFC)

Alkaline (AFC)

Phosphoric acid (PAFC)

Molten carbonate (MCFC)

Solid oxide (SOFC)

Hydrogen, natural gas



Pure hydrogen

Hydrogen, methanol

Fuels used

Table 2.3 Different types of fuel cells and main characteristics (FCWAY, 2006)

700–1000

600–800

130–220

70–220

Up to 120

Operating temperature (°C)

Power plants, combined heat and power (up to 10 MW)

Power plants, (up to 2 MW)

Small power plants (up to 1 MW)

Space vehicles, land vehicles, submarines (up to 12 kW)

Cars, buses, portable (up to 250 kW)

Applications range


2.10

61

Electrical energy storage

2.10.1 SMES In a SMES system (Xue et al., 2006), energy is stored in the magnetic field produced when direct current flows through a superconducting coil. For this to happen, the superconducting material of the coil must be cooled adequately so as to show no resistance to the flow of current, enabling the unit to store energy in the magnetic field. The superconducting material currently used is a nobium–titanium alloy, operating at liquid helium temperature. The recommended operating temperature for most systems ranges from 50 to 77 K, while the amount of energy stored is proportional to the inductance of the coil and the electrical current’s square. A typical system is given in Fig. 2.15, including the superconducting coil, the power conditioning systems and the refrigeration unit. SMES systems have the ability of fast response. They may be deeply discharged without any influence on either their operational efficiency or service period, and their lack of moving parts and impressive efficiency value (>90%) are some additional advantages. SMES systems do face some problems related to the stability of the superconducting coil: superconductivity appears to be quite sensitive to temperature variations and critical magnetic field. The main drawback of the technology, however, is the enormous amount of power needed to keep the coil at such low temperatures, combined with the high overall cost for the employment of such a unit (especially in the case of micro-SMES systems). By providing power on a scale of MW with a bridging time of seconds, SMES systems are designed for the treatment of voltage sags and frequency instabilities, e.g. when the output power of a wind farm fluctuates with the wind, the SMES can be 4

5

3

6 2

1. RES and/or grid 2. Source interface ± rectifier 3. Refrigerator unit 4. Superconducting coil containment 5. Inverter & AC interface 6. Electricity consumption

1

2.15 Superconducting magnetic energy storage configuration.


62


controlled to exchange the active power with the AC power grid, and the power fluctuation of the wind farm can be smoothed out (Shi et al., 2008; Ngamroo et al., 2009).

2.10.2 SCs The operational principle of SCs is based on that of conventional capacitors, i.e. when direct current applies, energy storage occurs in an existing electrical field. What separates SCs from common devices is their remarkably increased capacitance. Representative capacitance is around 5 F/cm2 while the corresponding value of conventional capacitors does not exceed 40 μF/cm2. In terms of structure, SCs are strongly reminiscent of batteries, with two electrodes being immersed in an electrolyte solution and kept at distance via a separator. The electrodes are made up of a high surface area porous material and the electrolyte can be either aqueous or organic: differences between the two include a higher energy density in the case of organic electrolytes (Morimoto et al., 1996) but lower costs and a wider temperature range in the case of aqueous electrolytes. A typical configuration is given in Fig. 2.16. Note that, in contrast with SMES and flywheel configurations, SCs do not require any additional power supply (i.e. for a refrigerator unit or vacuum pump). System advantages include the highest of power densities, fast charge and discharge rates, low current leakage, a considerable service period, thousands of cycles per year at deep discharge, operational stability within a wide range of temperatures and high energy efficiency. On the other hand, 4 3

1. RES and/or grid 2. Source interface ± rectifier 3. Supercapacitor 4. Inverter & AC interface 5. Electricity consumption

5 2

1

2.16 Supercapacitor energy storage configuration.



63

although energy density is higher than in common capacitors, it is still among the lowest in energy storage, while cost issues are also a serious constraint for such systems. SCs are destined for power quality applications, also contributing to hybrid storage configurations for the support of RES systems (Khan and Iqbal, 2005; Onar et al., 2006).

2.11

Comparison of energy storage systems (ESSs)

Each of the most established energy storage technologies have a number of advantages and disadvantages that designate the respective range of applications. To provide further insights into and quantification of each system’s main characteristics, a series of comparison charts is presented (see Figs 2.17 to 2.26 below). It must be emphasized that the data used for the compilation of the charts derive from numerous sources, i.e. Boyes, 2000; Kondoh et al., 2000; Cavallo, 2001; Dell and Rand, 2001; Schoenung, 2001; Butler et al., 2002; Makansi and Abboud, 2002; Beurskens and de Noord, 2003; Hubert et al., 2003; Denholm and Kulcinski, 2004; Eyer et al., 2004; Gonzalez et al., 2004; Swanbarton, 2004; Thackeray, 2004; Rydh and Sandén, 2005a,b; Sauer, 2006; Baker, 2008; Hall and Bain, 2008; Ibrahim et al., 2008; Chen et al., 2009; Divya and Østergaard, 2009; ESA, 2009a; Hadjipaschalis et al., 2009; Kaldellis et al., 2009b. Thus, a wide range of data is presented; however, a certain level of uncertainty concerning the validity of information should always be considered, especially since constant development in the field means that data values can change over short periods of time. Available power 1 kW

1 year

Discharge time

1 month 1 week ½ day

10 MW

100 kW

0.01 kW/kW h

1 GW

(1)

(5)

(3)

10 GW (2)

(4)

1 hour

1 kW/kW h

100 kW/kW h

1 min

(1): PHS (2): CAES (3): FA-HS (4): Flow batteries (5): Batteries (6): SMES (7): Flywheels & SCs

1 sec (6) (7) 10 ms kW h

×101

×102

MW h

×101

×102

GW h

×101

×102

TW h

Installed storage capacity

2.17 Power, discharge time and energy capacity ratings (based on Sauer, 2006).


64


2.11.1 Energy storage capacity vs discharge time Figure 2.17 provides a classification of ESSs in terms of energy storage capacity and rated power. More specifically, energy storage capacity is plotted against discharge time, i.e. the period over which the ESS discharges at its rated power, thus the system rated power is given as well, while the power to energy ratio (kW/kW h) is also available. Systems found on the upper right side of the chart (where discharge time and energy storage capacity are considerable), such as PHS, CAES and FC-HS, are ideal for the applications of commodity storage, rapid reserve and area controlfrequency responsive reserve. By contrast, systems found in the lower left side of the chart (where the power to energy ratio is high and the discharge time requirements are low), such as flywheels, SCs and SMES, are suitable for power quality/reliability and transmission system stability applications. Batteries cover a wide range of applications, from power quality to the early stages of energy management, with flow batteries being more appropriate for transmission and distribution deferral. Note that in the chart, a concentrated point of view is provided, considering battery technology as a whole. More information about the performance of actual systems may be obtained from the updated database (November 2008) of the Electricity Storage Association (ESA, 2009a); see also Fig. 2.18. In this context, Na-S comprises the battery technology with the highest discharge time, not influenced by 102 A 10

E

B

Discharge time (hours)

I 1

K F

C

0.1 10–2

10–3

D A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S

F: Li-ion I: VRB K: Zn-Br M: Flywheels N: SC

M

N

10–4 10–4

10–3

10–2

0.1

1

10

102

103

Rated power (MW)

2.18 Power and discharge time ratings for cumulative installed capacity until 2008 (based on ESA, 2009a).


104


65

the rated power output, while the opposite is true for the discharge time of L/A, Ni-Cd and Li-ion batteries. Na-S and L/A demonstrate similar power outputs (up to a scale of tens to hundreds of megawatts) with Li-ion showing the most moderate available power among battery systems (up to hundreds of kilowatts). Ni-Cd batteries on the other hand cover a wide range of power, from a few kilowatts to tens of megawatts. Finally, the power outputs of VRB and Zn-Br are not really affected by variation of discharge time, while VRB extends its power range back to the scale of a few kilowatts as well, in the interstage between customer energy management and power quality applications. Furthermore, SCs apply in conditions of high-rated power (even at the megawatt scale) and minimum discharge time (in a scale of seconds) with flywheels having the ability to satisfy both high-power applications for short duration (high-power flywheels) and considerable time applications at moderate power output (long duration flywheels).

2.11.2 Self-discharge vs recommended storage duration

Days Hours Seconds Minutes

Recommended storage duration

Months

As already discussed, self-discharge is used to express the losses of a storage system during off-duty periods and thus determines the maximum permitted storage duration. In Fig. 2.19, self-discharge of the ESSs under examination is plotted against the recommended storage period. The importance of self-discharge is divided into four areas: negligible and low, for both benign

E

G

A,H

A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S F: Li-ion G: FC-HS

B,I, K,O

F

H: Metal–air I: VRB K: Zn-Br L : SMES M: Flywheels N: SC O: PSB

C

Towards energy management

L D Towards power quality

Negligible

N M

Low

Considerable

Self-discharge importance

2.19 Recommended storage duration vs. self-discharge.


High

66


and very small self-discharge (i.e from 0% to ∼5% per month); considerable, in cases of 5–30%; and high if self-discharge losses exceed 30%. As can be seen, the relation between importance of self-discharge and recommended storage period is evident. Na-S and metal–air batteries, along with bulk energy storage including PHS, CAES and flow batteries, experience zero (in the case of Na-S) or minimum losses, while SCs and flywheels are very much limited by their inherent self-discharge (flywheels may fully discharge over the period of a day). Having a limited storage period in turn excludes these systems from certain applications, like spinning reserve where the periodicity of cycling is very low and where long time intervals between two consecutive cycles are expected. By contrast, provided that other requirements are satisfied as well, these systems may be suitable for power quality applications, where the cycling periodicity is high (with annual duty cycle requirements reaching 1000 cycles/year). On the other hand, bulk ESSs are essential for energy management applications, such as rapid reserve and commodity storage, while depending on their specific features they may serve other purposes as well (e.g. provision of area control-frequency responsive reserve).

2.11.3 Energy and power densities Another aspect of ESSs is covered by an investigation of energy and power density. Figure 2.20 provides both mass and volume energy density, while Fig. 2.21 shows the respective available power densities. From Fig. 2.20, it can be seen that most of the chemical storage media are favoured with high values for both mass and volume energy density, while mechanical and electrical energy storage technologies are determined by considerably lower values. Among these, only flywheels extend to 90 W h/l, also presenting the highest mass density, owing to the use of composite materials. On the other hand, to store considerable amounts of energy in an SC would require enormous system size, while metal–air and fuel cells imply minimum footprint impact and negligible portability concerns. Whereas electrical and small-scale mechanical systems present moderate energy densities, the opposite is valid for both mass and volume power density (Fig. 2.21). Indeed, SCs are determined by remarkably high values, followed by the technologies of SMES and flywheels, while against energy density rates, chemical systems (namely batteries) are not as efficient in terms of power extraction per unit mass or per unit volume.

2.11.4 Service period and number of cycles The lifetime and total number of cycles are also critical for the adoption of an ESS. For this purpose, Fig. 2.22 compares service period (in years) with



67

10 4

10


3

Mass energy density (W h/kg)

250 150 120 90

G H E F

Towards negligible portability issues K

70 50

D

B

M

30

C

I O

10

Towards minimum footprint impact

L

1

A

N

0.1 0.0

H: Metal–air I: VRB K: Zn-Br L: SMEs M: Flywheels N: SC O: PSB

0.1

1

10

30

50

70

90

120

150

250

10 3

10 4

Volume energy density (W h/l)

2.20 Mass and volume energy density.

5000

Mass power density (W/kg)

2000

C: L/A D: Ni-Cd E: Na-S F: Li-ion

G: FC-HS L: SMES M: Flywheels N: SC N

1200 L 900

M G

600

Towards negligible portability issues

300

E D

C

Towards minimum footprint impact

F

100 10

100

1000

10 4

Volume power density (W/l)

2.21 Mass and volume power density.


10 5

68

Stand-alone and hybrid wind energy systems 60

Service period (years)

50 40


H: Metal–air I: VRB K: Zn-Br L: SMES M: Flywheels N: SC O: PSB

A Fewer than 1000 cycles/year

30

B

L

20 16

D

N

I

O E

12 G

M

F

K

8 4

H

C

More than 1000 cycles/year

0 10

100

1000

2000

4000

6000 8000

10 4

10 5

10 6

Number of cycles (@80%DOD)

2.22 Service period vs. number of cycles.

the respective number of cycles. Although chemical energy storage has high energy and power density, most systems are less than 15 years old. By contrast, mechanical and electrical storage (apart from flywheels) may exceed 20 years of service period, with bulk ESSs even reaching 40–60 years. Furthermore, chemical storage, excluding PSB technology, is also limited by the number of cycles, with most of the systems found on the left side of the 1000 cycles per year curve (between 150 and 350 cycles per year on average). Flywheels, SCs and SMES may be fully charged and discharged between 2500 and 3500 times during a year on average, while metal–air batteries show the least attractive lifetime characteristics. Lifetime limitation is also the main disadvantage of L/A batteries, also affecting the life cycle cost of these systems. Overall, by adjusting the results of Fig. 2.8 to the duty cycle requirements of Fig. 2.22, the most suitable ESS for each application can be accrued.

2.11.5 Energy and power costs The capital cost of a system is mainly a synthesis of the energy cost (per unit of storage capacity) and the power cost (per unit of power output). On top of this, BOS components also entail a capital cost while in order to obtain a LC evaluation of the investment, additional data is necessary, such as the fixed and variable M&O cost for the ESS. The above information is


Overview of energy storage technologies for RES A: PHS B: CAES C: L/A D: Ni-Cd E: Na-S F: Li-ion G: FC-HS

10 5 Toward power quality applications

10 4 L

Energy cost (?/kW h)

10 3

D N

800

69

H: Metal–air I: VRB K: Zn-Br L: SMES M: Flywheels N: SC O: PSB

F

M

600

K,O I

400 200

Toward energy management applications

E

C

100 A

H

10

B G

1.0 10

100

200

400

600

800

1000

10 4

10 5

Power cost (?/kW)

2.23 Energy and power cost.

installation-specific and considerable uncertainty underlies the provision of any relative data. Further, economies of scale and numerous suppliers also influence the configuration of the capital cost. In this context, in Fig. 2.23, the energy and power costs of each system are given. ESSs found in the direction of power cost reduction are thought to be suitable for applications where high-power provision is required for short periods of time, while towards the direction of energy cost reduction one may encounter the ESSs being most appropriate for energy management applications (long discharge duration and considerable power). Bulk energy storage including PHS, CAES and FC-HS are determined by the lowest energy costs, while electrical storage, flywheels and metal-air batteries are kept under 400c/kW.

2.11.6 Useful energy, power extraction response and cycle efficiency By considering energy efficiency during discharge and the maximum recommended depth of discharge of an ESS, the actual useful energy extracted from the system can be obtained. Using the information available, the product of these two aforementioned parameters is plotted against the


70


VRB 80% Zn-Br 70%

Towards instant high power injections

Towards higher utilization of energy storage capacity

90%

PHS

10

PSB SMES

Na-S Ni-CD

60%

L/A 50%

1 Flywheels

CAES FC-HS

0.1

SC

40% Metal Air

30% 100 KW h

1 MW h 10 MW h 100 MW h 1 GW h 100 GW h +100 GW h

100

1/4 cycle

Energy storage capacity

Rated power Output (MW)

Maximum depth of discharge × efficiency

100%

0.01 1 cycle

Response time

2.24 Useful energy and power extraction response.

energy storage capacity ratings of certain ESSs (see Fig. 2.24). In this context, systems used for power quality applications where storage capacity is already limited are not currently evaluated. Instead, they are used in the second half of Fig. 2.24 where power rating is compared with system response time. In the left half of the figure, although electrolysis is excluded from the output efficiency, FC-HS still presents the lowest utilization of energy storage capacity among all the ESSs. The need to maintain air pressure inside the storage cavern for CAES, and the inability of L/A batteries to perform rather deep discharges, explain the fact that both leave almost 40% of their energy capacity unexploited. By contrast, flow batteries and PHS allow more than 70% of their capacity to be extracted, with VRB approaching 90% of energy utilization. SMES are thought to provide the highest power output in the shortest time, while flywheels require the entire cycle duration to take up load. Cycle efficiencies of ESSs are provided in Fig. 2.25. Flywheels and electrical storage systems, along with Na-S and Li-ion batteries, clearly exceed 80% while FC-HS and metal-air batteries drop below 50%. The rest of the technologies present efficiency rates ranging from 60 to 85%.

2.11.7 Environmental and safety concerns The environmental impact caused by the implementation and operation of an ESS is a parameter that is hard to quantify, though still of high significance for the realization of such projects. In terms of magnitude, bulk energy storage entails the most considerable impacts. PHS requires the construction of dams and tunnels, the manufacturing of equipment and the



71

Cycle efficiency

100%

80%

60%

40%

SC

Flywheels

SMES

Metal–air

Li-ion

Ni-Cd

Zn-Br

PSB

VRB

L/A

Na-S

FC-HS

CAES

PHS

20%

2.25 Cycle efficiency.

utilization of water resources, while CAES demands cavern formation, the installation of the plant and the natural gas infrastructure as well as consumption of fuel and the production of air emissions. On the other hand, certain chemical storage systems entail the production of toxic wastes (e.g. lead and cadmium disposal) and the production of excessive heat to the surroundings (e.g. Na-S batteries). Safety concerns are encountered in the case of flywheel operation, where the containment structures should be compact enough to withstand a possible burst, or in the case of SMES, where the development of intense magnetic fields may affect the staff employed, or in the case of FC-HS, where high pressure hydrogen storage also implies risks. Although hard to quantify, a measure of the environmental performance of ESSs may be provided by the common energy payback period (Denholm and Kulcinski, 2003). In this context, it is interesting to see that the contribution of energy storage remarkably reduces the energy payback period of RES installations alone via the recovery of otherwise wasted amounts of energy (Kaldellis et al., 2009a), while the respective energy payback period of the entire system, i.e. with the ESS, needs further investigation.

2.11.8 Maturity ESSs can be classified into three main categories according to their maturity, i.e. systems in developing stages (from concept to demonstration), systems already developed (from demonstration to commercial use), and mature systems. L/A batteries and PHS can be found in the category of mature systems, while FC-HS and metal–air batteries are included in the


72

Stand-alone and hybrid wind energy systems Demonstration stage SC

Concept stage SC & flywheels

SC SMES

Flywheels & metal–air

PSB & NaS VRB & ZnBr

Flywheels & metal–air

Mature commercial plants Flywheels & SMES

SMES

Adv. capacitors

L/A PHS& CAES

Flywheels & L/A

L/A

MW × 100 First commercial service Second plant in service Third plant in Preconstruction & Available for service commercial order licensing period Cost of Fourth plant in mature plants service Development Fifth plant in period Estimates Actual costs service Simplified estimation, insufficient data Design & construction period Finalized cost estimate

MW × 10 KW × 100 to MW × 1 Up to kW ×10 Seconds Hours

2.26 Maturity levels, novel concepts and cost distribution.

developing stage category. The rest of the technologies are consequently classified in the developed technologies category. Nevertheless, R&D is constant and new concepts do not allow strict classification in any of the three categories. For example, high-speed flywheels and SCs of tens of megawatts are still in the design or prototype stage. In this context, the maturity curve given in Fig. 2.26 depicts the cost distribution over the various development stages of a typical ESS and presents the stage of development where different technology types can be encountered.

2.12

Future trends

As has already been shown, the application range of most ESSs is delimited by a number of constraints. In this context, the need for large-scale penetration of energy storage, so as to facilitate the oncoming shift to distributed power generation, makes R&D in the field imperative. Energy storage developers and researchers constantly come up with new ideas (Baker, 2008; Hall and Bain, 2008) that aim to both improve the performance of such systems and reduce high procurement costs. Nevertheless, in the case of most ESSs, technological advances relate to incremental changes rather than fundamental steps, meaning that a lot is expected from material science, engineering, processing and fabrication rather than from a thorough review of existing concepts. A summary of future trends concerning the three main categories of ESSs, i.e. mechanical, chemical and electrical, is given below.



73

2.12.1 Mechanical energy storage Development expectations in PHSs are identical to those encountered in the field of turbo-machinery and civil works. Cost reductions in power electronics will encourage the introduction of adjustable speed units and the employment of two-stage machines, while promotion of seawater and underground reservoirs may limit the construction work required. R&D in CAES includes above-ground air storage, adiabatic CAES, small subsurface CAES systems, buried pipe systems, as well as the use of alternative fuels such as biogas, low-british thermal units (BTU) gas, No. 2 fuel oil, and mixtures of hydrogen and natural gas (Nakhamkin, 2007). Flywheels, on the other hand, turn to the use of high strength composite materials (Tzeng et al., 2006) and superconducting magnetic bearings (Koshizuka, 2006) that may allow higher speeds and analogous energy densities.

2.12.2 Chemical energy storage Attention is presently being given to lithium ion batteries, where developments related to lithium–sulphur and lithium–sulphide technologies are expected to bring remarkable increases of energy density. On the other hand, advances in electrodes, plates, seals, membranes and electrolytes are a common expectation for all battery technologies, while cell packaging, state-of-charge estimation and thermal management are also issues of major concern (Jin et al., 2003). For flow batteries, apart from the interest shown concerning energy density and costs, emphasis is currently being given to increase power density through the development of new electrodes, membranes and electrolytes (Hall and Bain, 2008). In the case of FC-HS, worldwide R&D expenditures during 2006 reached $829M (Fuel Cells Bulletin, 2008), reflecting the urgency of shareholders to promote the hydrogen economy. At the same time, the thematic areas of research set by HY-CO (HY-CO, 2009) include hydrogen production, solid hydrogen storage, PEM fuel cell stack/systems, high temperature fuel cells (MCFC and SOFC) and socio-economic aspects.

2.12.3 Electrical energy storage With regard to SCs, emphasis is presently being given to the areas of electrodes, electrolytes and packaging (Zhang et al., 2009), directly related to the development of nanostructured carbons and controlled porosity polymers, the use of ionic liquid electrolytes, and the employment of monolithic electrodes, respectively. On the other hand, for SMES to obtain a dominant position in the area of power quality, constraints deriving from the crystal nature of high-temperature superconductors must be addressed, while the


74


need to improve the management of critical currents and magnetic fields should also be considered (Minami et al., 2001).

2.13

References

Ackermann, T., Andersson, G., Söder, L. 2001. Distributed generation: a definition. Electric Power Systems Research, 57, 195–204. Agbossoo, K., Chahine, R., Hamelin, J., Laurencelle, F., Anouar, A., St-Arnaud, J.M., Bose, T.K. 2001. Renewable energy systems based on hydrogen for remote applications. Journal of Power Sources, 96, 168–172. Anagnostopoulos, J.S., Papantonis, D.E. 2008. Simulation and size optimization of a pumped-storage power plant for the recovery of wind-farms rejected energy. Renewable Energy, 33, 1685–1694. ASCE (American Society of Civil Engineers) 1993. Task Committee on Pumped Storage of the Hydropower Committee of the Energy Division of the American Society of Civil Engineers (Ed.) Compendium of Pumped Storage Plants in the United States, ASCE, New York, USA. Atcitty, S., Ranade, S., Gray-Fenner, A. 1998. Summary of State-of-the-art Power Conversion Systems for Energy Storage Applications (SAND98-2019). Energy Storage Systems Department, Sandia National Laboratories, California, USA. Baker, J. 2008. New technology and possible advances in energy storage. Energy Policy, 36, 4368–4373. Bathurst, G.N., Strbac, G. 2003. Value of combining energy storage and wind in short-term energy and balancing markets. Electric Power Systems Research, 67, 1–8. Bauen, A., Hart, D. 2000. Opportunities for fuel cell-based renewable energy supply in decentralised applications. World Renewable Energy Congress VI, 2551–2554. Bayod-Rújula, A.A. 2009. Future development of the electricity systems with distributed generation. Energy, 34, 377–383. Beurskens, L.W.M., De Noord, M. 2003. Economic issues of storage technologies in different applications. In: STORE 2003: Storage for Renewable Energies, Aix-enProvence, France, October 19–21. Bindner, H. 1999. Power Control for Wind Turbines in Weak Grids: Concepts development (Risø-R-1118(EN)). Risø National Laboratory, Roskilde, Denmark. Blurton, K.F., Sammells, A.F. 1979. Metal/air batteries: their status and potential – a review. Journal of Power Sources, 4, 263–279. Bolund, B., Bernhoff, H., Leijon, M. 2007. Flywheel energy and power storage systems. Renewable and Sustainable Energy Reviews, 11, 235–258. Boyes, J.D. 2000. Overview of energy storage applications. In: IEEE Power Engineering Society 2000 Summer Meeting, Seattle, Washington, USA, July 16–20. Bradshaw, D.T. 2000. Pumped hydroelectric storage (PHS) and compressed air energy storage (CAES). In: IEEE Power Engineering Society 2000 Summer Meeting, Seattle, Washington, July 16–20. Bueno, C., Carta, J.A. 2006. Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable energy in the Canary Islands. Renewable and Sustainable Energy Reviews, 10, 312–340.



75

Bullough, C., Gatzen, C., Jakiel, C., Koller, M., A Nowi, A., Zunft, S. 2004. Advanced adiabatic compressed air energy storage for the integration of wind energy. In: European Wind Energy Conference 2004, London, UK, November 22–25. Butler, P., Miller, J.L., Taylor, P.A. 2002. Energy Storage Opportunities Analysis, Phase II Final Report. (SAND2002-1314). Energy Storage Systems Department, Sandia National Laboratories, California, USA. Carrillo, C., Feijóo, A., Cidrás, J. 2009. Comparative study of flywheel systems in an isolated wind plant. Renewable Energy, 34, 890–898. Cavallo, A.J. 1997. Utility scale baseload wind energy plants. In: American Power Conference, Chicago, Illinois, USA, April 1–3. Cavallo, A.J. 2001. Energy storage technologies for utility scale intermittent renewable energy systems. Journal of Solar Energy Engineering, 123, 387–389. Cavallo, A.J. 2007. Controllable and affordable utility-scale electricity from intermittent wind resources and compressed air energy storage (CAES). Energy, 32, 120–127. Chakkaravarthy, C., Abdul Waheed A.K., Udupa, H.V.K. 1981. Zinc–air alkaline batteries – a review. Journal of Power Sources, 6, 203–228. Charters, W.W.S. 2001. Developing markets for renewable energy technologies. Renewable Energy, 22, 217–222. Chen, F., Duic, N., Manuel Alves, L., Da Graça Carvalho, M. 2007. Renewislands – Renewable energy solutions for islands. Renewable and Sustainable Energy Reviews, 11, 1888–1902. Chen, H., Ngoc Cong, T., Yang, W., Tan, C., Li, Y., Ding, Y. 2009. Progress in electrical energy storage system: a critical review. Progress in Natural Science, 19, 291–312. Chen, H.H., Berman, I.A. 1981. Planning an underground pumped hydro project for the Commonwealth Edison Company. In: 16th Intersociety Energy Conversion Engineering Conference; Atlanta, Georgia, USA, August 9–14. Chicco, G., Mancarella, P. 2009. Distributed multi-generation: A comprehensive view. Renewable and Sustainable Energy Reviews, 13, 535–551. Davies, T.S., Jefferson, C.M., Mayer, R.M. 1988. Use of flywheel storage for wind diesel systems. Journal of Wind Engineering and Industrial Aerodynamics, 27, 157–165. Dayan, A., Flesh, J., Saltiel, C. 2004. Drying of a porous spherical rock for compressed air energy storage. International Journal of Heat Mass Transfer, 47, 4459–4468. Dell, R.M., Rand, D.A.J. 2001. Energy storage – a key technology for global energy sustainability. Journal of Power Sources, 100, 2–17. Denholm, P. 2006. Improving the technical, environmental and social performance of wind energy systems using biomass-based energy storage. Renewable Energy, 31, 1355–1370. Denholm, P., Kulcinski, G.L. 2003. Net energy balance and greenhouse gas emissions from renewable energy storage systems. Energy Center of Wisconsin, Wisconsin, USA. Denholm, P., Kulcinski, G.L. 2004. Life cycle energy requirements and greenhouse gas emissions from large scale energy storage systems. Energy Conversion and Management, 45, 2153–2172.


76


Divya, K.C., Østergaard, J. 2009. Battery energy storage technology for power systems – an overview. Electric Power Systems Research, 79, 511–520. Dufo-López, R., Bernal-Agustín, J.L., Domínguez-Navarro, J.A. 2009. Generation management using batteries in wind farms: economical and technical analysis for Spain. Energy Policy, 37, 126–139. Duic, N., Da Graça Carvalho, M. 2004. Increasing renewable energy sources in island energy supply: case study Porto Santo. Renewable and Sustainable Energy Reviews, 8, 383–399. EG&G. 2004. Fuel Cells Handbook (seventh edition). A report prepared for the US Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory. EG&G Technical Services, Inc. Albuquerque. EIA (Energy Information Administration). 2007. International Energy Outlook, 2007 (DOE/EIA-0484(2007)). Office of Integrated Analysis and Forecasting, US Department of Energy, Washington, USA. ESA (Electricity Storage Association). 2009a. Technologies. Morgan Hill, California, USA. Available at: http://www.electricitystorage.org/site/technologies/ ESA (Electricity Storage Association). 2009b. Applications. Morgan Hill, California, USA. Available at: http://www.electricitystorage.org/site/applications/ European Commission 2001. Directive 2001/77/EC on the promotion of electricity produced from renewable energy sources in the internal electricity market. Official Journal of the European Communities, Brussels, Belgium. Eyer, J.M., Iannucci, J.J., Corey, G.P. 2004. Energy Storage Benefits and Market Analysis Handbook (SAND2004-6177). Energy Storage Systems Department, Sandia National Laboratories, California, USA. FCWAY (Fuel Cells Way). 2006. Fuel Cell Types, General. Available at: http://www. fcway.com Fuel Cells Bulletin. 2008. Fuel cell industry shows growth in jobs, sales, R&D. Fuel Cells Bulletin, 1. Fujihara, T., Imano, H., Oshima, K. 1998. Development of pump-turbine for seawater pumped storage power plant. Hitachi Review, 47, 199–202. Geman, H., Ohana, St. 2009. Forward curves, scarcity and price volatility in oil and natural gas markets. Energy Economics, 31, 576–585. Georgilakis, P.S. 2008. Technical challenges associated with the integration of wind power into power systems. Renewable and Sustainable Energy Reviews, 12, 852–863. Gergaud, O. 2002. Modelisation energetique et optimisation economique d’un systeme de production eolien et photovoltaıque couple au reseau et associe a un accumulateur. These de l’ENS de Cachande cembre. Gonzalez, A., Ó Gallachóir, B., McKeogh, E. 2004. Study of Electricity Storage Technologies and Their Potential to Address Wind Energy Intermittency in Ireland. Sustainable Energy Research Group, Department of Civil and Environmental Engineering, University College Cork. Greenblatt, J.B., Succar, S., Denkenberger, D.C., Williams, R.H., Socolow, R.H. 2007. Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation. Energy Policy, 35, 1474–1492. Grubb, M. 1995. Renewable Energy Strategies for Europe – Volume I, Foundations and Context, The Royal Institute of International Affairs, London, UK.



77

Hadjipaschalis, I., Poullikkas, A., Efthimiou, V. 2009. Overview of current and future energy storage technologies for electric power applications. Renewable and Sustainable Energy Reviews, 13, 1513–1522. Hall, P.J., Bain, E.J. 2008. Energy-storage technologies and electricity generation. Energy Policy, 36, 4352–4355. Hubert, S., Mattera, F., Malbranche, P. 2003. INVESTIRE network – investigation of storage technologies for intermittent renewable energies in Europe. Journal of Power Sources, 116, 287.e40–287.e43. Hull, J.R. 2004. Flywheels. Encyclopedia of Energy, C. Cleveland (editor), Academia Press: New York, USA, 695–704. HY-CO. 2009. Coordination Action to Establish a Fuel Cell and Hydrogen European Research Area. Available at: http://www.hy-co-era.net. Ibrahim, H., Ilinca, A., Perron, J. 2008. Energy storage systems – characteristics and comparisons. Renewable and Sustainable Energy Reviews, 12, 1221– 1250. Infield, D.G. 1994. Wind diesel design and the role of short term flywheel energy storage. Renewable Energy, 5, 618–625. IPCC (Intergovernmental Panel on Climate Change). 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)). IPCC, Geneva, Switzerland. IRES. 2006. International Renewable Energy Storage Conference I (IRES I). World Council for Renewable Energy, Science Park of Gelsenkirchen, Germany, October 30–31. IRES. 2007. International Renewable Energy Storage Conference II (IRES II). World Council for Renewable Energy, World Conference Center of Bonn, Germany, November 19–21. Jin, B., Kim, J., Gu, H. 2003. Electrochemical properties of lithium-sulfur batteries. Journal of Power Sources, 117, 148–152. Joerissen, L., Garche, J., Fabjan, Ch., Tomazic, G. 2004. Possible use of vanadium redox-flow batteries for energy storage in small grids and stand-alone photovoltaic systems. Journal of Power Sources, 127, 98–104. Kaldellis, J.K., Zafrakis, D. 2007. Optimum energy storage techniques for the improvement of renewable energy sources-based electricity generation economic efficiency. Energy, 32, 2295–2305. Kaldellis, J.K., Kavadias, K.A., Filios, A.E., Garofallakis, S. 2004. Income loss due to wind energy rejected by the Crete island electrical network – the present situation. Applied Energy, 79, 127–144. Kaldellis, J.K., Kavadias, K.A., Papantonis, D.E., Stavrakakis, G.S. 2006. Maximizing the contribution of wind energy in the electricity demand problem of Crete island. Wind Engineering Journal, 30, 73–92. Kaldellis, J.K., Simotas, M., Zafirakis, D., Kondili, E. 2009a. Optimum autonomous photovoltaic solution for the Greek islands on the basisof energy pay-back analysis. Journal of Cleaner Production, 117, 1311–1323. Kaldellis, J.K., Zafirakis, D., Kavadias, K. 2009b. Techno-economic comparison of energy storage systems for island autonomous electrical networks. Renewable and Sustainable Energy Reviews, 13, 378–392.


78


Katsaprakakis, D.A., Christakis, D.G., Zervos, A., Papantonis, D., Voutsinas, S. 2008. Pumped storage systems introduction in isolated power production systems. Renewable Energy Journal, 33, 467–490. Khan, M.J., Iqbal, M.T. 2005. Dynamic modeling and simulation of a small wind-fuel cell hybrid energy system. Renewable Energy, 30, 421–439. Kondoh, J., Ishii, I., Yamaguchi, H., Murata, A., Otani, K., Sakuta, K., Higuchi, N., Sekine, S., Kamimoto, M. 2000. Electrical energy storage systems for energy networks. Energy Conversion & Management, 41, 1863–1874. Korpaas, M., Holen, A.T., Hildrum, R. 2003. Operation and sizing of energy storage for wind power plants in a market system. International Journal of Electrical Power & Energy Systems, 25, 599–606. Koshizuka, N. 2006. R&D of superconducting bearing technologies for flywheel energy storage systems. Physica C: Superconductivity, 445–448, 1103–1108. Little, M.M. 2005. Wind energy: promoting a cleaner energy future. Green Trading Markets, 81–90. Lotspeich, C. 2002. A comparative assessment of flow battery technologies. In: Electrical Energy Storage Systems Applications and Technologies International Conference (EESAT2002), San Francisco, USA, April 15–17. Lund, H., Salgi, G., Elmegaard, B., Andersen, A.N. 2009. Optimal operation strategies of compressed air energy storage (CAES) on electricity spot markets with fluctuating prices. Applied Thermal Engineering, 29, 799–806. Maddaloni, J.D., Rowe, A.M., Van Kooten, G.C. 2009. Wind integration into various generation mixtures. Renewable Energy, 34, 807–814. Makansi, J., Abboud, A. 2002. Energy Storage. The Missing Link in the Electricity Value Chain. Energy Storage Council, Saint Louis, USA. Megahed, S., Ebner, W. 1995. Lithium-ion battery for electronic applications. Journal of Power Sources, 54, 155–162. Minami, M., Nakano, T., Akita, S., Kasahara, H., Tada, H., Takahashi, M., Nara, Y., Yamanaka, T., Sakaguchi, H. 2001. Research and development of superconducting magnetic energy storage system: Influence of mechanical properties of Bi-2212/ Ag Rutherford cable to its critical current. Physica C: Superconductivity, 357–360, Part 2, 1323–1326. Moriarty, P., Honnery, D. 2007. Intermittent renewable energy: the only future source of hydrogen? International Journal of Hydrogen Energy, 32, 1616–1624. Morimoto, T., Hiratsuka, K., Sanada, Y., Kurihara, K. 1996. Electric doublelayer capacitor using organic electrolyte. Journal of Power Sources, 60, 239– 247. Morrow, H. 2001. Environmental and human health impact assessments of battery systems. Industrial Chemistry Library, 10, 1–34. Nakhamkin, M.N. 2007. Novel compressed air energy storage concepts. In: Electrical Energy Storage Systems Applications and Technologies International Conference (EESAT2007). San Francisco, USA, September 23–26. Nema, P., Nema R.K., Rangnekar, S. 2009. A current and future state of art development of hybrid energy system using wind and PV-solar: a review. Renewable and Sustainable Energy Reviews, 13, 2096–2103. Ngamroo, I., Cuk Supriyadi, A.N., Dechanupaprittha, S., Mitani, Y. 2009. Power oscillation suppression by robust SMES in power system with large wind power penetration. Physica C: Superconductivity, 469, 44–51.



79

Onar, O.C., Uzunoglu, M., Alam, M.S. 2006. Dynamic modeling, design and simulation of a wind/fuel cell/ultra-capacitor-based hybrid power generation system. Journal of Power Sources, 161, 707–722. Oshima, T., Kajita M., Okuno, A. 2004. Development of sodium–sulfur batteries. International Journal of Applied Ceramic Technology, 1, 269–276. Papantonis, D. 1995. Hydrodynamic Machines: Pumps-hydro turbines. Symeon, Athens, Greece (in Greek). Papathanassiou, S., Boulaxis, N. 2006. Power limitations and energy yield evaluation for wind farms operating in island systems. Renewable Energy, 31, 457–479. Parker, C.D. 2001. Lead–acid battery energy-storage systems for electricity supply networks. Journal of Power Sources, 100, 18–28. Paul, C.B. 1994. Battery Energy Storage for Utility. Phase I – Opportunities analysis (SAND94-2605). Energy Storage Systems Department, Sandia National Laboratories, California, USA. Pepermans, G., Driesen, J., Haeseldonckx, D., Belmans, R., D’haeseleer, W. 2005. Distributed generation: definition, benefits and issues. Energy Policy, 33, 787–798. Perrin, M., Saint-Drenan, Y.M., Mattera, F., Malbranche, P. 2005. Lead-acid batteries in stationary applications: competitors and new markets for large penetration of renewable energies. Journal of Power Sources, 144, 402–410. Razelli, E. 2003. Prospects for lead–acid batteries. Journal of Power Sources, 116, 2–3. Ritchie, A., Howard, W. 2006. Recent developments and likely advances in lithiumion batteries. Journal of Power Sources, 162, 809–812. Rudnik, E., Nikiel, M. 2007. Hydrometallurgical recovery of cadmium and nickel from spent Ni-Cd batteries. Hydrometallurgy, 89, 61–71. Ruetschi, P. 2004. Aging mechanisms and service life of lead–acid batteries. Journal of Power Sources, 127, Issues 1–2, 33–44. Rydh, C.J., Sandén, B.A. 2005a. Energy analysis of batteries in photovoltaic systems. Part I: Performance and energy requirements. Energy Conversion and Management, 46, 1957–1979. Rydh, C.J., Sandén, B.A. 2005b. Energy analysis of batteries in photovoltaic systems. Part II: Energy return factors and overall battery efficiencies. Energy Conversion and Management, 46, 1980–2000. Salgi, G., Lund, H. 2008. System behaviour of compressed-air energy-storage in Denmark with a high penetration of renewable energy sources. Applied Energy, 85, 182–189. Sauer, D. 2006. The demand for energy storage in regenerative systems. In: 1st International Renewable Energy Storage Conference (IRES I), Science Park Gelsenkirchen, Germany, October 30–31. Scheer, H. 2006. Energy Autonomy: The Economic, Social and Technological Case for Renewable Energy. Earthscan/James & James Editions, London, UK. Schoenung, S.M. 2001. Characteristics and Technologies for Long- vs. Short-Term Energy Storage (SAND2001-0765). Energy Storage Systems Department, Sandia National Laboratories, California, USA. Schoenung, S.M., Hassenzahl, W.V. 2003. Long- vs. Short-Term Energy Storage Technologies Analysis. A Life-cycle Cost Study (SAND2003-2783). Energy Storage Systems Department, Sandia National Laboratories, California, USA.


80


Shi, J., Tang, Y.J., Ren, L., Li, J.D., Chen, S.J. 2008. Application of SMES in wind farm to improve voltage stability. Physica C: Superconductivity, 468, 2100–2103. Singh, P., Jonshagen, B. 1991. Zinc-bromine battery for energy storage. Journal of Power Sources, 35, 405–410. Soleille, S. 2006. Greenhouse gas emission trading schemes: a new tool for the environmental regulator’s kit. Energy Policy, 34, 1473–1477. Sørensen, B. 2000. Role of hydrogen and fuel cells in renewable energy systems. World Renewable Energy Congress VI, 1469–1474. Stern, N. 2006. Stern Review: The Economics of Climate Change. HM Treasury, Cambridge, UK. Strachan, N. 2004. Overview of distributed energy. Encyclopedia of Energy, C. Cleveland (editor), Academia Press, New York, USA, 823–839. Sum, E., Skyllas-Kazacos, M. 1985. A study of the V(II)/V(III) redox couple for redox flow cell applications. Journal of Power Sources, 15, 179–190. Suzuki, Y., Koyanagi, A., Kobayashi, M., Shimada, R. 2005. Novel applications of the flywheel energy storage system. Energy, 30, 2128–2143. Swanbarton Limited. 2004. Status of Electrical Energy Storage System (04/1878). DTI Technology Programme, UK. Thackeray, M.M. 2004. Batteries, Transportation Applications. Encyclopedia of Energy, C. Cleveland (editor), Academia Press, New York, USA, 127–139. Tzeng, J., Emerson, R., Moy, P. 2006. Composite flywheels for energy storage. Composites Science and Technology, 66, 2520–2527. Walawalkar, R., Apt, J., Mancini, R. 2007. Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy, 35, 2558–2568. Weisser, D. 2007. A guide to life-cycle greenhouse gas (GHG) emissions from electric supply technologies. Energy, 32, 1543–1559. Weisser, D., Garcia, R.S. 2005. Instantaneous wind energy penetration in isolated electricity grids: concepts and review. Renewable Energy, 30, 1299–1308. Wen, Z., Cao, J., Gu, Z., Xu, X., Zhang, F., Lin, Z. 2008. Research on sodium sulfur battery for energy storage. Solid State Ionics, 179, 1697–1701. Xue, X., Cheng, K., Sutanto, D. 2006. A study of the status and future of superconducting magnetic energy storage in power systems. Superconductor Science and Technology, 19, 31–39. Zhang, Y., Feng, H., Wu, X., Wang, L., Zhang, A., Xia, T., Dong, H., Li., X., Zhang, L. 2009. Progress of electrochemical capacitor electrode materials: a review. International Journal of Hydrogen Energy, 34, 4889–4899.


3 Design and performance optimisation of stand-alone and hybrid wind energy systems E. KONDILI, TEI of Piraeus, Greece

Abstract: Optimisation considerations in a hybrid energy system include the configuration of such a system, the sizing of the individual components and the percentage of load that will be covered by renewable energy sources (RES), depending on the needs of the specific site and various economic and environmental constraints. Therefore, there is a wide optimisation scope when dealing with hybrid energy systems, in their configuration and design as well as in their operation. The specific objectives of the present chapter are to analyse and describe the concepts and the parameters affecting the design and optimisation of hybrid energy systems. The main directions of the present chapter are to (i) identify the parameters determining the optimal design of each individual component as a part of a larger system; (ii) highlight the objectives and constraints governing the optimisation of hybrid energy systems; (iii) give a short review of the methods and techniques typically employed in the optimisation process; and (iv) provide an insight into the future prospects of hybrid energy systems optimisation. Key words: hybrid energy systems optimisation, component sizing, energy systems modelling.

3.1

Introduction: scope and objectives of the chapter

Hybrid energy systems can offer a valuable means of supplying electricity to remote areas. Future success of these systems relies on the continuous research, development and demonstration of renewable energy technologies, featuring improved operating performance, cost reduction and improved reliability. In most cases there is a range of different combinations of components that may be considered for a specific area, each combination exhibiting different technical and economic characteristics, i.e. the configuration of a hybrid energy system is itself an optimisation problem. Furthermore, the most favourable system configuration does not necessarily consist of the partial optima of the individual components and this poses another interesting optimisation problem. Many ideas have been proposed for approaching the optimal operation of hybrid energy systems. However, it should be noted that there is no globally accepted approach or solution to the problem. On the contrary, the problem is very complex, and the site-specific characteristics (e.g. 81 © Woodhead Publishing Limited, 2010

82


geographical location) and technical details of a system affect the application and success of any given solution. The aim of this chapter is to describe the problems of design optimisation for hybrid energy systems. More specifically, the purpose of this chapter is to: •

•

• • •

emphasise the role of energy systems modeling, and highlight the basic energy systems problems that may be approached through model development; identify the parameters that determine the design of each individual component and, specifically, each component as a part of a complete system; define the facets of hybrid energy system optimisation and enumerate potential objectives and constraints governing optimisation; give a short review of the methods and techniques usually employed to optimise energy systems; highlight the research and development trends and future prospects of hybrid energy system optimisation.

3.2

Energy systems modelling

3.2.1 Scope and type of energy models Energy systems models are the mathematical models that are developed in order to represent as reliably as possible various energy-related problems. Lately, these models have become a common means to identify and solve such problems. This trend has been encouraged by the development of robust solution algorithms and capable computing systems. Depending upon the case under consideration, the related mathematical models may concern a wide variety of problems, such as synthesis, design, operation and optimisation, as will be described in more detail later in the chapter. The size and complexity of the model are closely related to the level of detail of the analysis required, the data availability and the scale of the problem. For instance, the model required could be for a specific unit in a plant only, or, on a much larger scale, for use in energy supply planning of a remote geographical area or even a whole country. Figure 3.1 indicates the type of energy models that may be encountered, as well as their implementation and use. Energy models can be developed for the efficient forecasting, planning, design, operation and optimisation of all energy systems. In a recent review (Jebaraj and Iniyan, 2006) in which a wide variety of energy models were analysed, factors such as gross income, gross output, profit, energy quantity, gross national product (GNP)/energy ratio, energy performance and energy production were cited as possible criteria for energy system optimisation.


Design and performance optimisation Problem/model

Comments

Forecasting model

Use of forecasting techniques to predict future energy demand magnitudes

Energy planning models

Planning of energy balance for a long-term horizon in a specific area

Energy supply and demand models

Modelling of each supply source and demand pattern

Behaviour of energy equipment

Analysis of single equipment for energy efficiency calculations

Behaviour of entire energy system

Analysis of an entire energy system describing its physical characteristics and laws

Energy systems optimisation models

One or more optimisation criteria determine the optimal solution of a specific energy system

Exploitation/use of energy models

83

• Conventional energy systems

System synthesis/configuration

• RES

Detailed system and components design

• Hybrid energy systems

Simulation of systems operation System optimisation

3.1 Types and uses of energy models.

It should also be noted that technology, efficiency, supply, demand, employment and resource availability have been used as constraints in these models. Different mathematical programming models may be fully applied to the optimisation of hybrid energy systems at any time, using efficiency and cost factors as critical parameters in formulating an objective approach to optimisation. As mathematical programming models are developed, it is interesting to study in detail the type of optimisation criteria that can be employed in different problematic scenarios (Ostergaard, 2009). These criteria may express economic functions such as net present value, total cost, annualised cost and profit; or alternatively a well-defined performance measure based on the operation of the energy system; or even the consideration of sustainability issues. Furthermore, the physical, various technical and resource limitations of the system under consideration may be very simply


84


embedded in the model. In fact, the possibility of accommodating environmental constraints and costs makes energy modelling a promising approach in the search for solutions to complex optimisation problems.

3.2.2 Synthesis, design and operation energy models As in many other engineering systems, a wide range of synthesis, design and operation problems may be encountered and solved with the use of energy models (Table 3.1). It is interesting here to mention that all these problems may also be approached with other techniques, such as scenario analysis and simulation. The use of integrated energy models assumes that the problem can be

Table 3.1 Definition of specific problem types Problem

Input/data

Expected output

Synthesis/ configuration problem

• System requirements • Resource availability • Alternative considerations • Basic system specifications

• Which components will be included in the system • How these components are linked to each other

Design/sizing problem

• System configuration (results of the synthesis problem) • Detailed system requirements • Basic data (properties, costs, etc.)

• Size and type of each component • Implied investment cost

Operation analysis

• System design • Operating mode • Requirements, technical data • Cost data

Calculation of all the system magnitudes (flows, efficiencies, operating costs, etc.)

Optimisation: It may concern any of the above problems, i.e.

• Criteria to be optimised • Operational, technical and environmental constraints

The one optimal solution of the problem, i.e. the solution that optimises the criteria provided that all the constraints are satisfied

• the optimal configuration • the optimal sizing and design • the optimal operation of a system


Design and performance optimisation

85

described reliably with mathematical relationships, and that its complexity can be encompassed by the available algorithms. The synthesis problem In the synthesis problem the system’s configuration will be determined, i.e. which units will be allocated in the system, and where. This is a very crucial aspect in the design, since there are usually many alternative possibilities related to which individual components will be included in a hybrid energy system.

For example, the synthesis problem of a given hybrid energy system would be to determine: • • • •

the type of renewable energy system (RES) to be included (i.e. wind, photo voltaic (PV), hydro); the number and capacity of RES units to be installed (if used); whether a back-up diesel generator would be included in the system; whether energy storage would be integrated into the system.

In the development of mathematical models for the synthesis problem, integer (binary) variables are defined for this type of decision, i.e. whether or not a component will be included in the system, and how it will be integrated. In modelling terms, this usually leads to an optimisation problem with integer variables, possibly of a non-linear nature. Therefore, synthesis problems usually require the use of mixed-integer linear programming (MILP) or mixed-integer non-linear programming (MINLP) mathematical models. The design problem The design problem addresses the size and number of system components. For example, a hybrid energy system consisting of various components (proposed by the configuration solution to the synthesis problem) may be designed in order to cover a specific demand. The mathematical models expressing the design problem will include as variables the number and size of each individual component, as well as their interconnections. Owing to the frequent interactions between the design and synthesis problems, the two are often faced simultaneously. Therefore, both the structure and main dimensions of the system are often decided together.


86


For example, the design problem of a hybrid energy system would be to determine the size of each given component of the system, so that • a specific load would be satisfied; • the capital costs would be limited; • the environmental impacts would be reduced; • other important variables would be accounted for.

The design problem may also be formulated as an optimisation problem, with a variety of optimisation criteria such as cost minimisation, variables reflecting the size of each component and constraints describing the physics of the system as well as potential limits of the system under consideration. The operation problem Another approach that is extensively used in hybrid energy system design and operation is systems simulation. In this case a model is once again developed, but this specific model attempts to simulate the real system by iterative solutions. Various alternative scenaria are evaluated with parameter values input in sequential iterations, such that the solutions improve incrementally upon each new evaluation. Usually, operational models are simpler than the synthesis – design models and so simulation problems are usually easier to solve. However, the optimal solution cannot necessarily be guaranteed. In modelling an operational hybrid energy system, a wide spectrum of calculation problems may arise, for instance, from imprecision in performance measure estimations, or due to the inherent complexity involved in planning an entire system configuration. This operation problem is critical, given that there are many alternative modes of operating a system, resulting in various performance efficiencies, and satisfying different operational constraints. In each of the above cases, the variables, parameters, constraints and optimisation criteria are different. The following sections will highlight the close relationship between the synthesis, design and operation problems and optimisation models.

3.2.3 Optimisation models The use of optimisation models has vastly improved systems engineering. It has evolved from a methodology of solely academic interest into one that has many interesting practical implementations, and continues to make a



87

significant impact on the industry (Biegler Lorenz and Grossman, 2004; Grossman and Biegler Lorenz, 2004), where optimal solutions to systems engineering problems translate into larger savings in money, energy, labour, water usage, time, etc., and increased competitiveness. The scope for optimisation is wide and may include the plant, the process, a piece of equipment, or any intermediate scale between them. Optimisation of an energy system may focus on a complex combination of units, on individual units, on individual pieces of equipment, or on subsystems within a piece of equipment. There are currently many different types of problems that can be solved through the use of optimisation models, depending on the scope and complexity of the system under consideration. The classification of problem types is in most cases independent of the solution methods being applied, i.e. very different problems may be solved with the same solution method and vice versa. The process of optimisation involves the development of an appropriate mathematical model coupled with an algorithm to solve the representative problem, a process which is usually implemented through optimisation software. The advantages of such optimisation models includes, for example, the possibility of embedding all the characteristics of a system into one problem, with the possibility of determining the optimal solution to that problem virtually guaranteed (if, indeed the optimal solution exists). However, the development of a complete mathematical model that can take all parameters into account is not always easy, and the reliability of the solution obtained depends upon the completeness and the quality of the model. Furthermore, sometimes the model may become so complex that its solution may in turn become equally complex and expensive to realise (for example, in the case of large number of integer variables or in highly non-linear systems). According to the definitions provided above, the synthesis, design and operation problems can easily be approached as optimisation problems, as determined by appropriate variables, optimisation criteria and systems constraints. Since design problems tend to rely more heavily on the predictions of process models, which are non-linear in nature, these problems can be approached as non-linear programming (NLP) and MINLP problems. In operation problems the time-dependent requirements and activities related to planning are most important and so these problems can be approached as linear programming (LP) and MILP problems.

3.3

Synthesis, design and operation of a hybrid energy system

As has already been detailed in other chapters of this book, a hybrid energy system may consist of wind turbines, PV panels, micro-hydro, biomass


88


Anemometer

Wind turbine 2 kW

Thermometer PV array 610 Wp

Pyranometer

Electronic lamps Upper reservoir

Data logger Flowmeter PC

DC current 24 V Data signal Water circuit

Charge controller

Control panel

Thermometer Battery 24 V

Lower reservoir Water pump

3.2 Hybrid renewable energy system with wind–PV–hydro energy storage system (Kaldellis et al., 2009e).

power generator systems, and conventional diesel generators, potentially combined with energy storage systems. A general schematic representation of a hybrid energy system is shown in Figs 3.2 and 3.3 (Kaldellis, 2008; Kaldellis et al., 2009e).

3.3.1 Local conditions and system selection The selection of the system components depends on the local conditions of the area. Because of the stochastic behaviour of most renewable energy sources, the major considerations involved in the design of a hybrid energy system must include the reliability of the power supply under varying weather conditions and the projected cost of energy. The steps to be followed in an integrated synthesis–design project of a hybrid system (Fig. 3.4) can be summarized as follows (Diaf et al., 2007): 1. Estimation and evaluation of the renewable energy potential. 2. Decision on the hybrid system configuration based on a pre-feasibility analysis. 3. Decision on the specific set of components. 4. Hybrid system model development. 5. Sizing and economic optimisation of the system. 6. Simulation results and operational analysis of the resulting system.


Design and performance optimisation Local applications

Wind farm

Control panel Consumption

PV station ESS

3.3 Hybrid renewable energy system with wind–PV–diesel energy storage components (Kaldellis, 2008).

Evaluation of local conditions

System configuration

Hybrid system model development

Sizing of system components

Simulation and operational analysis

3.4 Basic steps for energy system development.


89

90


In order to calculate the performance of an existing system, or to predict the energy consumption or energy generation of a system in the design stage, appropriate weather data are required. Pre-feasibility studies based on weather data determine the availability and magnitude of resources (e.g. wind speed and solar irradiation) which can then be coupled to load requirements for the specific site. The reliability of information concerning sitespecific climate conditions determines to a large extent the quality of the proposed solution. After a pre-feasibility study, the selection of proper sizing of equipment can be made based on the local climate conditions (weather data and wind), required load and capacity.

3.3.2 Optimal design of hybrid energy systems Many different approaches have been followed in the industry in order to apply the basic steps outlined in Fig. 3.4. Sizing considerations for individual components of a hybrid energy system have been considered by many researchers, with many different approaches applied which mainly rely on simulation and scenario analysis. However, the development and implementation of optimisation models for the design stage has not been a common approach to the problem. Furthermore, in most cases the optimal sizing is thought to be a problem to be considered simultaneously with the selection of the set of system components (configuration/synthesis), and the optimal number and type of units in terms of technical and economic concepts. The unit sizing of the integrated hybrid energy system plays an important role in configuring its reliability and economy. A sizing optimisation method can help to guarantee the lowest investment with full use of a PV array, wind turbine and battery bank, so that the hybrid system can work at the optimum condition in terms of financial investment and system reliability. As has already been mentioned, there is no generic model and solution method for the optimal design and sizing of hybrid energy systems. Various tools have been developed and applied to that effect. A review of simulation and design models that have been used can be found in Bernal-Agustin and Dufo-Lopez (2009), although recent works are not cited. The most widely studied hybrid energy systems are PV–diesel, PV–wind, PV–wind– diesel and wind–diesel hybrid systems. Various research groups approach the design problem using either their own simulation and optimisation software tools (Kaldellis, 2008; Kaldellis et al., 2008, 2009a,b,c,d,e), or using commercial software such as HOMER and Hybrid2 (National Renewable Energy Laboratory, 2005). Most studies concern the design and economic aspects of these systems and deal less with control issues. Environmental considerations have also been



91

taken into account in these studies, while some specific case studies are included, usually concerning the application of hybrid energy systems in remote areas. There is a continuously increasing interest in developing models for hybrid energy systems (Deshmukh and Deshmukh, 2008), and of the various techniques considered for the optimal sizing of hybrid energy systems, we could mention: • • • • •

linear programming; probabilistic approach; iterative techniques; dynamic programming; multiobjective optimisation.

Linear programming techniques minimise the average production cost of electricity while meeting the load requirements in a reliable manner, and take environmental factors into consideration both in the design and operation phases.

3.4

Hybrid energy system optimisation techniques

3.4.1 Criteria for hybrid energy system optimisation In order to select an optimum combination for a hybrid system to meet the load demand, evaluation must be carried out on the basis of power reliability and system life-cycle cost. The optimum combination can make the best compromise between the two main objectives: power reliability and system cost. Power reliability analysis Because intermittent resource characteristics (wind strength, solar irradiation) strongly influence energy production, a power reliability analysis is usually a required step in any system design process. There are a number of methods used to calculate the reliability of a hybrid energy system, including: • •

•

loss of load probability (LOLP): power failure time period divided by a given period of time (generally one year); loss of power supply probability (LPSP): probability that an insufficient power supply will result when the hybrid system is unable to satisfy the load demand; unmet load (UL): non-served load divided by the total load of a period of time (normally one year).


92


The most popular method is the LPSP method. The LPSP is the probability that an insufficient power supply will result when the hybrid energy system is not able to satisfy the load demand. The design of a reliable stand-alone hybrid solar–wind system (integrated with energy storage) can be pursued by using the LPSP as the key design parameter. Two approaches exist for the application of the LPSP in designing a stand-alone hybrid solar–wind system. The first one is based on chronological simulations. This approach is computationally intensive and requires the availability of data spanning a sufficient period of time. The second approach uses probabilistic techniques to incorporate the fluctuating nature of the resource and the load, thus eliminating the need for time-series data. System cost analysis Several economic criteria exist for analysing system costs, such as the net present cost (NPC), levelised cost of energy (LCE) and life-cycle cost. The NPC is defined as the total present value of a time-series of cash flows, which includes the initial cost of all the system components, the cost of any component replacements that occur within the project lifetime, and the cost of maintenance, i.e. investment costs plus the discounted present values of all future costs during the lifetime of the system. The system’s lifetime is usually considered to be the life of the elements that have the longest lifespan. The LCE is widely used in the design and optimisation of energy systems. It reflects the cost of generating energy (usually electricity) for a particular system and it is defined as the ratio of the total annualised cost of the system to the annual electricity delivered by the system. This includes all the costs over the system’s lifetime from initial investment and capital costs, to operations and maintenance (e.g. fuel) and financing costs. An excellent analysis of all the economic issues that may be considered for a hybrid energy system is provided in Chapter 4 on feasibility assessment.

3.4.2 Economic and techno-economic optimisation of hybrid energy systems As previously noted, the synthesis, design and operation problems of a hybrid energy system may be expressed as optimisation problems, where certain criteria need to be optimised (minimised or maximised) subject to a set of operational, technical and/or environmental constraints stemming from the physical and operational characteristics of the system. At a general level, hybrid energy systems may be designed from an economic perspective or from a techno-economical perspective; but within



93

these two approaches, several subdivisions can be found. Economic optimisation criteria may include: • • •

total energy systems costs; capacity costs; and societal costs.

Techno-operational optimisation criteria may include: • fuel savings; • CO2 emissions; • reserve/backup capacity; • elimination of excess power generation. The potential optimisation criteria that may be considered for a particular hybrid energy system could, for example, include: • • • •

minimisation of the LCE; maximisation of the uilisation rate of the RES; minimisation of diesel generator fuel consumption; minimisation of diesel generator start/stop frequency.

There is no generally accepted economic design criterion according to which systems are universally analysed, optimised and designed. Numerous papers have been written about the optimum economic designs of PV and/ or wind and/or diesel systems with energy storage in batteries. Usually, the optimum design is carried out minimising the NPC or by minimising the LCE. Diaf et al. (2007, 2008) present an application of a hybrid PV–wind– battery system in Corsica (France) which minimises the LCE. BernalAgustın and Dufo-Lopez (2009) carried out the optimisation of hybrid PV–Diesel–Battery systems by means of genetic algorithms (GA). Shaahid and El-Amin (2009) used the HOMER software for economic optimisation (minimisation of the NPC) of a PV–diesel–battery system to supply a shopping centre located in Dhahran (Saudi Arabia). Yang et al. (2008) present a method for the optimisation of hybrid PV– wind–battery systems which minimise the LCE. The optimisation was carried out by trying component combinations: • • • • •

changing changing changing changing changing

the the the the the

number of PV modules; orientation of PV modules; rated power of the wind turbine; tower height of the wind turbine; and capacity of the battery bank.

Multicriteria decision analysis may be elaborated (Shaahid and El-Amin, 2009); however, generally applicable optimisation models have not yet been


94


developed, representing an area in need of future research. In BernalAgustin and Dufo-Lopez (2009) there is a complete review of optimisation routes, and an evaluation of the software systems that have been developed for the simulation and optimisation of hybrid energy systems. The use of improved software tools should prove very useful to the future development of multicriteria optimisation models.

3.5

Software tools for the simulation and optimisation of hybrid energy systems

Several software tools (simulation programs) are available for designing hybrid energy systems. In this section, three of these systems are presented; namely HOMER, Hybrid2 and HOGA. Table 3.2 shows the capabilities of each system.

3.5.1 HOMER software system The Hybrid Optimisation Model for Electric Renewables (HOMER) software system is a public domain program produced by National Renewable Energy Laboratory (NREL) (2005). It is a time-step simulator using hourly load and environmental data inputs for renewable energy system assessment; it facilitates the optimisation of renewable energy systems based on NPC for a given set of constraints and sensitivity variables. HOMER has been used extensively in previous renewable energy system case studies and in renewable energy system validation tests. Although simulations can take a long time depending on the number of variables used, its operation is straightforward. The program’s limitation is that it does not enable the user to intuitively select the appropriate components for a system, as the algorithms and calculations are not visible or accessible.

Table 3.2 Characteristics of hybrid simulation and optimisation tools (indicatively) (Diaf et al., 2007)

Free download and use PV, diesel, batteries Wind Mini hydro Simulation Economic optimisation Multi-objective optimisation, GAs

HOMER

Hybrid2

HOGA

x x x x x x

x x x x x

x x x x x


x


95

Input information to be provided to HOMER includes: • • • • • •

electrical loads (load data); renewable resources (e.g. solar radiation data); component technical details/costs; constraints; controls; type of dispatch strategy, etc.

HOMER designs an optimal power system to serve the desired loads, performing hundreds or thousands of hourly simulations (to ensure the best possible match between supply and demand). The software performs automatic sensitivity analyses to account for the sensitivity of key parameters to the system design, such as resource availability or component costs.

3.5.2 Hybrid2 software system The Hybrid2 software package is a user-friendly tool which can perform detailed, long-term performance and economic analysis on a wide variety of hybrid power systems. Hybrid2 is a probabilistic/time-series computer model, which uses time-series data for loads, wind speed, solar insolation (solar irradiation) and temperature to predict the performance of the hybrid power system selected by the user. Variations in wind speed and load within each time frame are factored into the performance predictions. The code does not consider short-term system fluctuations caused by system dynamics or component transients. Hybrid2 was designed to study a wide variety of hybrid power systems including three types of electrical loads, multiple wind turbines of different types, photovoltaics, multiple diesel generators, battery storage and four other types of power conversion devices. An economic analysis tool is also included, that calculates the economic value of the project using many economic and performance parameters. The Hybrid2 code employs a graphical user interface (GUI) and a glossary of terms commonly associated with hybrid power systems and comes packaged with tools to assist the user in designing hybrid power systems. Each piece of equipment held in its library is commercially available and uses the manufacturers’ specifications. In addition, the library includes sample power systems and template projects. Two levels of output are provided, a summary and a detailed timestep-by-time-step description of power flows. A graphical results interface (GRI) allows for easy and in-depth review of the detailed simulation results.


96


3.5.3 The HOGA software system Hybrid Optimisation by Genetic Algorithms (HOGA) is a simulation and optimisation program developed in C++ by the Electric Engineering Department of the University of Zaragoza (Spain). It can be used for hybrid energy systems that generate electricity (either direct current (DC) or alternating current (AC)), produce hydrogen or that are applied to water pumping loads (either separately or in combination). Optimisation is achieved by minimising total system costs throughout the whole of its useful lifespan, based on NPC calculations. However, the programme allows for multi-objective optimisation, where additional variables may also be minimised: CO2 emissions or unmet load (energy not served), for example. The hybrid system may comprise the following elements: photovoltaic (PV) panels, wind turbines, hydraulic turbine, fuel cell, hydrogen tank and electrolyser, as well as batteries, battery charge regulator, inverters (DC/ AC converter), rectifier (AC/DC converter), and AC generator (the latter based on conventional or renewable fuel sources, e.g. biogas). All elements may be present simultaneously, and the user may decide to include only some of them as part of the desired system. The software was initially developed for remote systems (i.e. off-grid systems). However, the program allows AC electricity to be bought from sold to the grid (surplus unused energy), or surplus hydrogen produced in the electrolyser and stored in the tank. Simulations are also possible for feasibility studies of zero-consumption renewable energy facilities connected to the electrical grid. Optimisation is available throughout the program both for different element combinations as well as for system control strategies. When the number of possible combinations of components and control strategies is too high, requiring excessive time to enumerate all the possible combinations, a GA technique can help the designer to obtain a good combination (the optimal or a combination near the optimal), in a reasonable run time. The optimisation carried out by means of GAs can be mono-objective or multi-objective.

3.6

Summary of optimisation techniques

Various optimisation techniques such as graphical construction methods, probabilistic approaches and iterative techniques have been recommended by researchers to guarantee the lowest investment with full use of the corresponding energy systems. For example, an interesting review of the most commonly applied optimisation techniques for hybrid systems is given in Zhou et al. (2009). The



97

Table 3.3 Simple summary of the relative merits and demerits of different optimisation methodologies (Diaf et al., 2007) Optimisation techniques

Advantages

Graphic construction method

Probabilistic approach

Disadvantages Only two parameters can be included in the optimisation process

Eliminate the need for time-series data

Iterative technique

Cannot represent the dynamic changing performance of the system Usually results in increased computational efforts and suboptimal solutions

Artificial intelligence methods

Find the global optimum system configuration with relative computational simplicity

Multi-objective design

Can optimise simultaneously at least two conflict objectives

results of this review are presented in Table 3.3, along with the advantages and disadvantages of each employed method. In any case, the scope for further research in the elaboration of such techniques will have a significant impact on the improvement and competitiveness of hybrid energy systems.

3.7

Future trends

Hybrid energy systems represent a very promising sustainable solution for power generation in stand-alone applications. Research and development carried out in these emerging technologies will certainly result in reducing the cost of the systems, despite the complex procedure involved in the design and optimisation of these systems. Optimum resource allocation, based on load demand and renewable resource forecasting, also promises to significantly reduce the total operating cost of the system. In addition to


98


mere cost minimisation criteria alone, it is increasingly important to consider other relevant factors such as minimisation of emissions, or maximisation of systems reliability. The optimisation of the configuration, design and operation of hybrid energy systems is supported by advanced models that describe the systems realistically. Further research into the development of generic mathematical models will facilitate the development and application of reliable and easily accessible multi-objective optimisation tools, such as software programs. The development of a generic, validated and complete methodology for the synthesis and the design of hybrid energy systems – incorporating appropriate planning and standardised models to take into account the characteristics of the location, the suitable hybrid energy system, and all the potential operational and performance scenarios for its application – would significantly improve the implementation of these technologies. The application of modern control techniques (such as a centralised system controller) would further improve the operational performance and energy management of these modular hybrid energy systems, allowing the utilisation of the renewable resource to be optimised.

3.8

References and further reading

Bernal-Agustin, J., Dufo-Lopez, R. 2009, Simulation and optimisation of standalone hybrid renewable energy systems, Renewable and Sustainable Energy Reviews 13, 2111–2118. Biegler Lorenz, T., Grossmann, I.E. 2004, Retrospective on optimization, Computers and Chemical Engineering 28, 1169–1192. Cai, Y.P., Huang, G.H., Yang, Z.F., Lin, Q.G., Bass, B., Tan, Q. 2008, Development of an optimization model for energy systems planning in the Region of Waterloo, International Journal of Energy Research 32(11), 988–1005. Celik, A.N. 2003, Techno-economic analysis of autonomous PV–wind hybrid energy systems using different sizing methods, Energy Conversion and Management 44, 1951–1968. Deshmukh, M.K., Deshmukh, S.S. 2008, Modelling of hybrid renewable energy systems, Renewable and Sustainable Energy Reviews 12(1), 235–249. Deshmukh, S.S., Boehm, R.F. 2008, Review of modelling details related to renewably powered hydrogen systems, Renewable and Sustainable Energy Reviews 12(9), 2301–2330. Diaf, S., Diaf, D., Belhamel, M., Haddadi, M., Louche, A. 2007, A methodology for optimal sizing of autonomous hybrid PV/wind system, Energy Policy 35, 5708–5718. Diaf, S., Nottn, G., Belhamel, M., Haddadi, M., Louche, A. 2008, Design and techno-economical optimisation for hybrid PV/wind system under various meteorological conditions, Applied Energy 85, 968–987. Ekren, O., Ekren, B., Ozerdem, B. 2009, Break–even analysis and size optimisation of a PV/Wind hybrid energy conversion system with battery storage – a case study, Applied Energy 86, 1043–1054.



99

Fung, C.C., Ho, S.C.Y., Nayar, C.V. 1993, Optimisation of a hybrid energy system using simulated annealing technique. Proceedings of the IEEE Region 10 Conference on Computer, Communication, Control and Power Engineering, TENCON 1993, pp. 235–238. Fung, C.C., Iyer, V., Maynard, C. 1998a, Computational intelligence techniques for short term generation scheduling in a hybrid energy system. In S PRICAI’98: Topics in Artificial Intelligence, pp. 272–281. Springer, Berlin/Heidelberg. Available online at: http://www.springerlink.com Fung, C.C., Iyer, V., Maynard, C. 1998b, Short-term generation scheduling of a remote area hybrid energy system using computational intelligence techniques. Proceedings of the IEEE Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 1(1–3), 365–370. Grossmann, I.E., Biegler Lorenz, T.B. 2004, Part II. Future perspective on optimization, Computers and Chemical Engineering 28, 1193–1218. Gupta, A., Saini, R.P., Sharma, M.P. 2006a, Modelling of hybrid energy system for off grid electrification of clusters of villages. Proceedings of the IEEE Conference on Power Electronics, Drives and Energy Systems, (PEDES) 2006, pp. 1–5. Gupta, A., Saini, R.P., Sharma, M.P. 2006b, Optimised application of hybrid renewable energy system in rural electrification. Proceeding of the IEEE Indian Conference on Power Electronics (IICPE) 2006, pp. 337–340. Gupta, A., Saini, R.P., Sharma, M.P. 2007, Design of an optimal hybrid energy system model for remote rural area power generation. Proceedings of the IEEE Conference on Electrical (ICEE) 2007, pp. 1–6. Gupta, A., Saini, R.P., Sharma, M.P. 2008a, Computerized modelling of hybrid energy system – Part I: Problem formulation and model development. Proceedings of the IEEE Conference on Electrical and Computer Engineering (ICECE) 2008, pp. 7–12. Gupta, A., Saini, R.P., Sharma, M.P. 2008b, Computerized modelling of hybrid energy system – Part II: Combined dispatch strategies and solution algorithm. Proceedings of the IEEE Conference on Electrical and Computer Engineering (ICECE) 2008, pp. 13–18. Haque, R.U., Iqbal, M.T., Quaicoe, J.E. 2006, Sizing, dynamic modelling and power electronics of a hybrid energy system. Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering, CCECE, 2006, pp. 1135–1138. Hokaoglu, F.O., Gerek, O., Kurban, M. 2009, A novel hybrid (wind-photovoltaic) system sizing procedure, Solar Energy 83, 11, 2019–2028. Jebaraj, S., Iniyan, S. 2006, Review of energy models, Renewable and sustainable energy reviews 10, 281–311. Kaldellis, J.K. 2008, Integrated electrification solution for autonomous electrical networks on the basis of RES and energy storage configurations, Energy Conversion and Management 49, 3708–3720. Kaldellis, J.K., Zafirakis, D., Kavadias, K., Kondili, E. 2008, An optimum sizing methodology for combined photovoltaic-energy storage electricity generation configurations, ASME Journal of Solar Energy Engineering, SOL-07-1177 131(2), 021010(1–12). Kaldellis, J.K., Simotas, M., Zafirakis, D., Kondili, E. 2009a, Optimum autonomous photovoltaic solution for the Greek islands on the basis of energy pay-back analysis, Journal of Cleaner Production 17, 1311–1323.


100


Kaldellis, J.K., Zafirakis, D., Kondili, E. 2009b, Optimum autonomous stand-alone photovoltaic system design on the basis of energy pay-back analysis, Energy 34, 1187–1198. Kaldellis, J.K., Zafirakis, D., Kondili, E. 2009c, Optimum sizing of photovoltaicenergy storage systems for autonomous small islands, International Journal of Electrical Power & Energy Systems 32(1), 24–38. Kaldellis, J.K., Zafirakis, D., Kaldelli, E.K., Kavadias, K. 2009d, Cost benefit analysis of a photovoltaic-energy storage electrification solution for remote islands, Renewable Energy 34, 1299–1311. Kaldellis, J.K., Spyropoulos, G.C., Kavadias, K.A., Koronaki, I.P. 2009e, Experimental validation of autonomous PV-based water pumping system optimum sizing, Renewable Energy 34(4), 1106–1113. Kélouwani, S., Agbossou, K., Chahine, R. 2005, Model for energy conversion in renewable energy system with hydrogen storage, Journal of Power Sources 140(2), 392–399. Lagorse, J., Paire, D., Miraoui, A. 2009, Sizing optimization of a stand-alone street lighting system powered by a hybrid system using fuel cell, PV and battery. Renewable Energy 34(3), 683–691. Li, C-H., Zhu, X-J., Cao, G-Yi, Sui, S., Hu, M-Ruo, 2009, Dynamic modelling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology, Renewable Energy 34(3), 815–826. Maclay, J.D., Brouwer, J., Samuelsen, G.S. 2007, Dynamic modeling of hybrid energy storage systems coupled to photovoltaic generation in residential applications, Journal of Power Sources 163(2), 916–925. Manolakos, D., Papadakis, G., Papantonis, D., Kyritsis, S. 2001, A simulationoptimisation programme for designing hybrid energy systems for supplying electricity and fresh water through desalination to remote areas: Case study: the Merssini village, Donoussa island, Aegean Sea, Greece, Energy 26(7), 679–704. Mellit, A., Kalogirou, S.A., Hontoria, L., Shaari, S. 2009, Artificial intelligence techniques for sizing photovoltaic systems: a review, Renewable and Sustainable Energy Reviews 13(2), 406–419. National Renewable Energy Laboratory, 2005, HOMER Getting Started Guide Version 2.1, NREL. Nema, P., Nema, R.K., Rangnekar, S. 2009, A current and future state of art development of hybrid energy system using wind and PV-solar: a review, Renewable and Sustainable Energy Reviews, 13(8), 2096–2103. Onar, O.C., Uzunoglu, M., Alam, M.S. 2006, Dynamic modeling, design and simulation of a wind/fuel cell/ultra-capacitor-based hybrid power generation system, Journal of Power Sources 161(1), 707–722. Onar, O.C., Uzunoglu, M., Alam, M.S. 2008, Modeling, control and simulation of an autonomous wind turbine/photovoltaic/fuel cell/ultra-capacitor hybrid power system, Journal of Power Sources 185(2), 1273–1283. Ostergaard, P.A. 2009, Reviewing optimisation criteria for energy systems analyses of renewable energy integration, Energy 34(9), 1236–1245. Razak, J., Sopian, K., Nopiah, Z., Zaharim, A., Ali, Y. 2007, Optimization of renewable energy hybrid system by minimizing excess capacity, International Journal of Energy 1, 77–81.



101

Shaahid, S.M., El-Amin, I. 2009, Techno-economic evaluation of off-grid hybrid photovoltaic–diesel–battery power systems for rural electrification in Saudi Arabia – A way forward for sustainable development, Renewable and Sustainable Energy Reviews 13(3), 625–633. Tuborache, T., Morega, Al. 2008, Optimum design of a wind/PV/diesel/batteries hybrid systems, 2nd International Conference on Moderm Power Systems MPS2008. Uzunoglu, M., Onar, O.C, Alam, M.S. 2009, Modeling, control and simulation of a PV/FC/UC based hybrid power generation system for stand-alone applications, Renewable Energy 34(3), 509–520. Vieira, F., Ramos, H.M. 2009, Optimization of operational planning for wind/hydro hybrid water supply systems, Renewable Energy 34(3), 928–936. Yang, H-X., Zhou, W., Lu, L., Fang, Z-H. 2008, Optimal sizing method for standalone hybrid solar–wind system with LPSP technology by using genetic algorithm, Solar Energy 82, 354–367. Zhou, W., Yang, H-X., Fang, Z-H. 2008, Battery behavior prediction and battery working states analysis of a hybrid solar–wind power generation system, Renewable Energy 33, 1413–1423. Zhou, W., Lou, C-Z., Li, Z-S., Lu, L., Yang, H-X. 2009, Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems, Applied Energy 2009, 87(2), 380–389. http://www.ceere.org/rerl/projects/software/hybrid2 (accessed on 08 October 2009). http://www.hoga-renewable.es.tl/ (accessed on 08 October 2009).


4 Feasibility assessment for stand-alone and hybrid wind energy systems J. K. KALDELLIS, TEI of Piraeus, Greece

Abstract: The present chapter investigates the financial behaviour of hybrid electricity generation wind-based (HEW) systems, taking into account the existing information concerning the initial investment and the maintenance and operation cost of similar small power stations. In this context, an integrated cost–benefit analysis provided leads to the estimation of the payback period, the financial efficiency, the net present value (NPV) and the internal rate of return (IRR) for several HEW installations examined. Subsequently, the reliability impact on the total cost function is investigated, while emphasis is also laid on the socioenvironmental benefits of stand-alone HEW systems. Furthermore, in order to obtain a realistic comparison between the existing fossil fuel-based solutions and the proposed HEW installation, the electricity generation cost of the wind-based hybrid station is estimated. Finally, in an attempt to demonstrate the clear competitive advantages of the proposed HEW solution, selected representative case studies are currently examined, while a detailed sensitivity analysis concerning the financial behaviour of stand-alone HEW systems also provided completes the present study. Key words: hybrid wind energy systems, feasibility assessment, cost– benefit analysis, sensitivity analysis, electricity generation cost.

4.1

Introduction

Hybrid electricity generation wind-based (HEW) systems significantly contribute to the protection of the environment and potentially reduce the dependence of remote consumers on oil imports. However, when considering their annual energy yield, their main disadvantage is the relatively high initial cost, although the efficiency of most renewable energy source (RES)based electricity generation techniques has continually increased, while their cost has remarkably reduced during the past years. It is still reducing at a rate faster than that of any other energy production technology (Masakazu et al., 2003; Albrecht, 2007; Blanco, 2009). Meanwhile, far from decision centres and having limited political influence, isolated consumers are often faced with serious problems, owing to insufficient local infrastructure (Jensen, 2000). In this context, autonomous HEW systems have proven to be one of the most promising and environmentally friendly technological solutions for the electrification of remote 102 © Woodhead Publishing Limited, 2010

Feasibility assessment for stand-alone and HEW systems

103

consumers, especially in the presence of considerable wind potential (Kaldellis, 2004). However, one important technical problem of similar installations results from the highly fluctuating power output of wind turbines, which is generally incompatible with the demand of typical domestic or commercial users (Kaldellis et al., 2005a). To face this problem, an appropriate energy-storage device is usually incorporated, which significantly increases the initial cost of stand-alone installations (Kaldellis et al., 2009a). Alternatively, one may operate a small diesel-electric generator. However, diesel-electric generator sets used up to now, while being relatively inexpensive to purchase, are generally expensive to operate and maintain, especially at partial-load levels (Hunter and Elliot, 1994). In fact, in the absence of energy storage, a high start–stop cycling frequency being evident leads to increased wear of the diesel engine and, therefore, to increased maintenance needs (Beyer et al., 1995). In an attempt to obtain a realistic and environmentally friendly solution, the idea of using a HEW system is hereby examined, considering also any other available RES for the area at each investigation. For example, complementarity between solar and wind energy resources significantly reduces both the inevitable diesel engine operation and energy storage requirements (Kaldellis et al., 2006a). In this context, the main parameters of the proposed hybrid system dimensions should first be determined (Kaldellis, 2002a; Kaldellis and Vlachos, 2005; Kaldellis et al., 2007), in accordance with electricity consumption requirements and the characteristics of the local renewable energy potential. Accordingly, this chapter investigates the financial behaviour of HEW systems, taking into account the existing information concerning the initial investment and the maintenance and operation (M&O) cost of similar small power stations, in view of the remarkable financial support for similar renewable energy applications from government and international resources. More precisely, the next section analyses the first installation cost of a typical stand-alone HEW system, which is followed by an investigation of both the fixed and the variable M&O costs. Furthermore, Section 4.4 presents an integrated cost–benefit analysis, leading to the estimation of the major parameters of the installation, such as the pay-back period, the financial efficiency, the net present value (NPV) and the internal rate of return (IRR). Subsequently, the reliability impact on the total cost function is investigated, while emphasis is also laid on the socio-environmental benefits of stand-alone HEW systems. Furthermore, in order to obtain a realistic comparison between the existing fossil fuel-based solutions and the proposed HEW installation, the electricity generation cost of the windbased hybrid station is estimated. In an attempt to demonstrate the clear competitive advantages of the proposed HEW solution, selected representative case studies are currently examined, while a detailed sensitivity


104


analysis concerning the financial behaviour of stand-alone HEW systems also provided completes the present study.

4.2

First installation cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system

As already mentioned a typical stand-alone HEW system (Fig. 4.1) is based on the following: • •

One or more (usually small) wind converters of No kW. A complementary RES-based generator (e.g. small hydro turbine, photovoltaic (PV) generator). For example, in the current analysis one may

V (m/s)

(°C)

t (h)

rα (kg/m3)

t (h)

o

hin

En (kW h)

N (kW)

V (m/s)

Ek (kW h)

P/Po

t (h)

24 t (h)

00

t (h)

Wind turbine UPS h∝ = 0.95 AC/DC rectifier

Diesel

Charge controller

hx = 0.95

Inverter Po

AC load

nrec = 0.95 he = 0.95 Battery

Photovoltaic array

Q (A h)

U (V) 13 12 11 10 G (W/m

2)

I (A) e

(°C)

W/m2

t (h)

0

25

% 50 Depth of discharge

1

hin

Ew (kW h)

Eλ (kW h)

U (V) c

(°C)

t (h)

t (h)

h/ hp c

(°C)

t (h)

4.1 Proposed autonomous HEW system.


P/Pe

00

24 t (h)


•

•

• • • • •

105

utilize a PV array of z panels (N+ maximum/peak power of every panel) properly connected to feed the charge controller with the voltage and the power required in order to meet the remote consumer load demand. Extensive financial analysis regarding wind–hydro hybrid stations may be found in the following studies (Kaldellis and Kavadias, 2001; Kaldellis et al., 2001, 2006b; Kaldellis, 2007). Optionally one may also utilize a small thermal power engine (dieselelectric generator or a mini gas turbine) based either on biomass (biogas or biofuel) or fossil fuel consumption (Zafirakis and Kaldellis, 2009). An appropriate energy storage device, e.g. a lead-acid battery storage array, able to guarantee ho typical hours of energy autonomy, or equivalently with energy storage capacity Qmax and discharge capacity limit Qmin, or equivalently maximum depth of discharge DODL. An AC/DC rectifier of Nr kW in case that the energy storage installation needs DC current. A charge controller of Nc kW. An optional UPS (uninterruptible power supply) of Np kW in order to guarantee high quality AC electricity generation. A DC/AC inverter of Np kW. Other auxiliary equipment and the non-active part of the installation, including supporting structures, power conditioning devices and cabling.

In order to proceed to the financial evaluation of a similar installation one should take into consideration that the entire investment cost of a HEW energy system (after n years of operation) is a combination (Kaldellis and Gavras, 2000; Kaldellis et al., 2005b) of the initial installation cost and the corresponding M&O cost, considering also the investment residual value. More precisely, as already seen in previous works (Kodossakis and Kaldellis, 1997; Kaldellis, 2002b), the initial investment cost IC0 includes the market (ex-works) price of the installation components (i.e. wind turbine, ICWT; PV-panels, ICPV; battery, ICbat; diesel-electric generator, ICd, and electronic devices ICel, including inverter, UPS, rectifier and charge controller cost) and the corresponding balance of the plant cost, expressed as a fraction f of the wind turbine market price. Thus one may write: IC0 = ICWT + ICPV + ICd + ICbat + ICel + f · ICWT

4.1

According to the analysis of Kaldellis and Kavadias (2007) the ex-works price ICWT of a small wind turbine (rated power No ≤ 100 kW) may be expressed using the following relation: ⎛ a ⎞ IC WT = ⎜ + c⎟ ⋅ N o ⎝ b + N ox ⎠


4.2

106


where the corresponding coefficients may be approached for the European market as: a = 8.7 × 105 (C/kW) b = 621 x = 2.05 c = 700 (C/kW) taking into consideration that the specific cost (C/kW) of small wind turbines is usually higher (1000–1500C/kW) than the one of big commercial contemporary machines (600–800C/kW). The values existing in the market present significant variation due to the numerous small manufacturers producing a large variety of products (Clausen and Wood, 1999; Refocus, 2002). Accordingly, the ex-works price of contemporary PV panels may be estimated (Haas, 2002; Hoffmann, 2006) using equation (4.3), i.e.: ICPV = ζ · Pr · z · N+

4.3

where ζ is a function of z (i.e. ζ = ζ(z)), expressing the scale economies for increased number of PV panels utilized. In the present case ζ may be taken equal to 1. Subsequently Pr is the specific buy-cost (Kaldellis et al., 2009b) of a PV panel (generally Pr = Pr(z.N+)) expressed in C/kWp; see also Fig. 4.2. If a small internal combustion engine is utilized (mainly as back-up option) the diesel-electric generator (rated power Nd) ex-works price ICd is given as:

Pr (1000 × ?/kWp)

ICd = φ · Nd

4.4

12 11 10 9 8 7 6 5 4 3 2 1 0 1.00E-03 1.00E-02 1.00E-01 1.00E+00 1.00E+01 1.00E+02 1.00E+03 1.00E+04 NO(kWp)

4.2 Specific price of existing PV installations.



107

with φ = 150–250 (C/kW), depending also on the technology applied. Small gas turbines are also available; however, their utilization is not very widespread for remote installations. If the installation storage system is a lead–acid battery bank (rated storage capacity Qmax) the corresponding purchase cost ICbat may be approximated (for 24 V, DODL = 75%) by the following relation (Kaldellis, 2002c): ω ICbat = ξ · Q1− max

4.5

with ξ = 5.04 (C/A h) and ω = 0.078. Subsequently, the electronic devices’ (including inverter Np, UPS Np, rectifier No, charge controller No), ex-works cost ICelec is given as: ICel = λ · N 1−τ p + B · No

4.6

where the following values (λ = 483 (C/kW), τ = 0.083, B = 380 (C/kW)) describe the data of the European market at a specific time period. Finally, the balance of the plant cost f · ICWT of the hybrid power station is strongly case-dependent (Kaldellis, 2002b, 2003a) thus the corresponding coefficient f takes values between 0.15 and 0.50, while a typical value of f = 0.30 may be adopted in most cases. Recapitulating, the initial installation cost of a stand-alone HEW system is a function of the wind turbine rated power No, the rated power of the complementary RES generator (for example the number of PV panels z), the energy storage capacity Qmax, the annual fossil fuel consumption Mf as well as of the peak load demand of the installation Np, i.e.: IC0 = IC0 (No, z, Qmax, Mf, Np)

4.7

Finally, in several countries an initial investment cost subsidy is available for RES-based environmentally friendly applications. Actually, γ is the subsidy percentage taking values between 30% and 50%. This is the case for Greece, where according to the current development law (e.g. 3299/04) or the corresponding National Operational Competitiveness Program (Kaldellis, 2002b; Kaldellis et al., 2005b) there is a remarkable financial support of similar environmentally friendly energy production technologies; see also Section 4.7.

4.3

Maintenance and operation cost of a typical stand-alone hybrid electricity generation windbased (HEW) system

During long-term operation, the M&O cost can be split (Kaldellis et al., 1989; Kaldellis, 2003a) into the fixed FCn and the variable VCn maintenance


108


cost. In the present analysis, the fixed M&O cost also considers the fuel cost consumed by the diesel-electric generator. Generally speaking, the annual fixed M&O cost can be expressed (Kaldellis, 2000) as a fraction m of the initial capital invested, including also an annual inflation rate equal to gm describing the annual changes of labour cost and the corresponding spare parts, embracing also any lubricant’s consumption. Subsequently, the fuel consumption cost results by the annual diesel-oil quantity consumed Mf, the current fuel price cf and the oil price annual escalation rate ef. Thus one gets: n n−1 ⎡ 1 + gm ⎛ 1 + gm ⎞ 2 1 + gm ⎞ 1 + gm ⎞ ⎤ FC n = m ⋅ IC o ⋅ ⎢ + +…+ ⎛ +⎛ ⎝ 1+ i ⎠ ⎝ 1 + i ⎠ ⎥⎦ ⎣ 1+ i ⎝ 1+ i ⎠ n−1 n ⎡ 1 + ef ⎛ 1 + ef ⎞ 2 1 + ef ⎞ 1 + ef ⎞ ⎤ + cf ⋅ Mf ⋅ ⎢ + +…+ ⎛ +⎛ 4.8 ⎝ 1+ i ⎠ ⎝ 1 + i ⎠ ⎥⎦ ⎣ 1+ i ⎝ 1+ i ⎠

where i is the return on investment index. The variable M&O cost VCn mainly depends (Kaldellis, 2002c) on the replacement of ko major parts of the installation, which have a shorter lifetime nk than the complete installation. Using the symbol rk for the replacement cost coefficient of each ko major part (battery, diesel-electric generator, inverter, charger, etc.) the VCn term can be expressed as: k = ko ⎧l =lk l ⋅n − l ⋅n ⎫ VC n = IC o ⋅ ∑ rk ⋅ ⎨ ∑ [(1 + gk ) ⋅ (1 − ρk )] k ⋅ (1 + i )( k ) ⎬ ⎩ l =1 ⎭ k =1

4.9

where lk is the integer part of the following equation, i.e.: ⎛ n − 1⎞ lk = ⎜ ⎝ nk ⎟⎠

4.10

while gk and ρk describe the mean annual change of the price and the corresponding technological improvement level for the kth major component of the system. Taking into account that one may introduce the parameter hk defined as: 1 + hk = (1 + gk) · (1 − ρk)

4.11

equation (4.9) reads equivalently as: ⎡ l =lk ⎛ 1 + hk ⎞ l⋅nk ⎤ VC n = IC o ⋅ ∑ rk ⎢ ∑ ⎝ ⎠ ⎥ k =1 ⎣ l =1 1 + i ⎦ k = ko

4.12

In the present analysis one may take into account the diesel-electric generator, the battery bank and the electronics (e.g. inverter and charger) replacement every nd, nb and ne years respectively (e.g. nd ≈ 4–6, nb ≈ 5–7 and ne ≈ 10 years). Applying equation (4.12), one finally gets:


Feasibility assessment for stand-alone and HEW systems VCn = IC0 · Ψ

109 4.13

with:

Ψ = 0 for n ≤ nd = 5 n 1 + hd ⎞ d Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠

for nd + 1 ≤ n ≤ nb = 7

n n 1 + hd ⎞ d 1 + hb ⎞ b + rb ⋅ ⎛ Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠

for nb + 1 ≤ n ≤ 2 nd = 10

2n n n n 1 + hd ⎞ d 1 + hb ⎞ b 1 + he ⎞ e 1 + hd ⎞ d + rd ⋅ ⎛ + re ⋅ ⎛ + rb ⋅ ⎛ Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ for 2 nd + 1 ≤ n ≤ 2nb = 14 n 2n n 2n n 1 + hd ⎞ d 1 + hd ⎞ d 1 + hb ⎞ b 1 + hb ⎞ b 1 + he ⎞ e + rb ⋅ ⎛ + re ⋅ ⎛ + rd ⋅ ⎛ + rb ⋅ ⎛ Ψ = rd ⋅ ⎛ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ ⎝ 1+ i ⎠ for 2 nb + 1 ≤ n ≤ 3nd = 15

⎡ 1 + hd ⎞ nd ⎛ 1 + hd ⎞ 2 nd ⎛ 1 + hd ⎞ 3 nd ⎤ ⎡⎛ 1 + hb ⎞ nb ⎛ 1 + hb ⎞ 2 nb ⎤ + + r + Ψ = rd ⋅ ⎢⎛ ⋅ b ⎢⎝ 1 + i ⎠ + ⎝ 1 + i ⎠ ⎥ ⎝ 1 + i ⎠ ⎥⎦ ⎝ 1+ i ⎠ ⎣⎝ 1 + i ⎠ ⎣ ⎦ 1 + he ⎞ + re ⋅ ⎛ ⎝ 1+ i ⎠

ne

for 3nd + 1 ≤ n ≤ 4 nd = 20

4.14

where rd · IC0 is the diesel-electric generator, rb · IC0 is the battery and re · IC0 is the electronic (inverter, charger, etc.) replacement cost in present values, while hd, hb and he describe the diesel-electric generator/battery/electronics purchase cost-technology improvement mean annual change (technologyinflation rate); see also equation (4.11).

4.4

Cost-benefit analysis of a typical stand-alone hybrid electricity generation wind-based (HEW) system

Using the above analysis and considering that the proposed stand-alone HEW system is going to operate for n years, one may estimate the corresponding total operational cost by combining the initial cost and the fixed and variable maintenance cost in present (constant) values, i.e.: Cn = IC0 · (1 − γ) + FCn + VCn − Yn

4.15

In this context, using the analysis of Sections 4.2 and 4.3 the total cost of the hybrid power station during n years of operation is given as: x n − 1 cf ⋅ Mf yn − 1 ⎡ ⎤ Cn = ICo ⋅ ⎢(1 − γ ) + m ⋅ x ⋅ + ⋅ y⋅ + Ψ ⎥ − Yn − x − 1 y 1 IC ⎣ ⎦ o


4.16

110


where: x=

1 + gm 1+ i

4.17

y=

1 + ef 1+ i

4.18

and

Similarly, Yn represents the residual (salvage) value of the investment, attributed to amounts recoverable at the nth year of the hybrid system life (e.g. value of land or buildings, scrap or second hand value of equipment), along with the experience gained and the corresponding technological know-how. On the other hand, the total income Rn over an n year period – resulting from the operation of a stand-alone hybrid power station – is mainly attributed to the energy production E. This energy yield is normally used to cover the remote consumer electricity needs, while in some cases a portion of energy (surplus) may be sold to the national electrical grid. In order to avoid complicated mathematical equations in the present analysis it is assumed that the entire energy yield is absorbed by the remote consumer, thus any taxation impact is neglected (Kavadias et al., 2000). Details for the economic evaluation of grid connected applications may be found in (Kaldellis et al., 2002, 2005b; Colle et al., 2004; Mondol et al., 2009). Thus, the present value of the total HEW station income (operating for n years) is given as: j=n

1+ e ⎤ Rn = E ⋅ co ⋅ ∑ ⎡⎢ ⎥ ⎣ j =1 1 + i ⎦

j

n−1 n ⎡1+ e ⎛ 1+ e⎞2 1+ e⎞ 1+ e⎞ ⎤ = E ⋅ co ⋅ ⎢ + +⎛ +…+ ⎛ ⎝ 1+ i ⎠ ⎝ 1 + i ⎠ ⎥⎦ ⎣ 1+ i ⎝ 1+ i ⎠

4.19

where co is the current energy effective cost coefficient (C/kW h) usually expressed either as electricity price or as electricity generation cost and e is the corresponding electricity price/cost annual escalation rate. Generally speaking, in order to predict the exact value of the effective cost coefficient, it is important to examine if the hybrid power station will be used autonomously or parallel to the national grid. For this purpose the self-utilization factor s is introduced (Kaldellis et al., 1989), defined as the ratio of the amount of the electricity used directly by the producer to the total energy produced by the hybrid power plant. In case that the energy produced is sold to the national grid the corresponding price ca is determined according to the existing legislation frame. Generally speaking, the effective cost coefficient is given as:


Feasibility assessment for stand-alone and HEW systems co = s · cs + (1 − s) · ca

111 4.20

where cs is the market price for direct self-use of the energy produced. For a stand-alone HEW system s = 1.0. The corresponding gains Gn of the investment investigated (in constantpresent values) are determined using equations (4.15) and (4.19), i.e.: Gn = Rn − Cn

4.21

Besides, in the case that: Gn = Rn − Cn = 0

4.22

the break-even equation of the HEW station is obtained (Elton and Gruber, 1984). Accordingly, the pay-back period n* of the investment is estimated by solving equation (4.22). For this purpose, an iterative solution of the nonlinear break-even equation is required. Subsequently, in order to examine not only the economic viability but also the economic attractiveness of a similar investment, an additional parameter is defined (Kaldellis, 1999) called ‘economic efficiency (η*)’ of the hybrid power station. By definition, η* compares the net gains of the investment over an n (e.g. n = 10 or 20) year period in constant terms with the initial capital invested. Thus one may write:

η*n =

Gn [ICo ⋅ (1 − γ )] − Yn

4.23

The economic efficiency of a wind park may be either negative (investment not viable) or positive, while in the case that η* = 0 one may calculate the pay-back period; see also equation (4.22). For the exact solution of equations (4.22) and (4.23), the accurate value of all parameters appearing in these expressions is required. However, owing to the continuous changes in the value of the governing parameters, a detailed sensitivity analysis of the main variables of the problem (e.g. n*, η10, η20) is suggested, so as to investigate the impact of techno-economic factors on the economic behaviour of similar hybrid power applications and to assure the viability and attractiveness of similar investments (Kaldellis and Gavras, 2000; Kaldellis et al., 2005b). Finally, one of the most common methods to investigate the financial behaviour of an investment is by estimating the corresponding NPV of the investment after a predefined operational period, usually equal (or less) to the service period of the investment (Rothwell, 1997; Liu and Ye, 2003). More specifically, comparing the present value of the total investment cost and the corresponding total revenues, one has the ability to estimate the NPV of the investment after n years of operation, i.e.:


112

Stand-alone and hybrid wind energy systems NPVn = Rn − Cn = Gn or NPVn = E ⋅ co ⋅ q ⋅

qn − 1 + γ ⋅ IC 0 + Yn − IC 0 − q−1

m ⋅ IC 0 ⋅ x ⋅

yn − 1 xn − 1 − cf ⋅ Mf ⋅ y ⋅ − Ψ ⋅ IC 0 y−1 x−1

4.24

where: q=

1+ e 1+ i

4.25

Note that one may also use the non-dimensional form ‘npv’: npv n =

NPVn IC 0

or npv n =

E ⋅ co qn − 1 Y ⋅q⋅ + γ + n − 1− IC 0 IC 0 q−1 m⋅ x ⋅

x n − 1 cf ⋅ Mf yn − 1 − ⋅ y⋅ −Ψ x−1 y−1 IC 0

4.24a

In this context, the IRR of an investment operating during an n year period is predicted (Talavera et al., 2010) by setting the NPVn value equal to zero. Thus we get: IRR = i*, when NPVn (i*) = 0 or npvn (i*) = 0

4.26

For the estimation of the IRR an ‘expert type’ numerical code should be developed, in order to solve the non-linear break-even equation (4.26). Note that equation (4.26) is similar to equation (4.22).

4.5

Reliability impact-loss of load cost of a typical stand-alone hybrid electricity generation wind-based (HEW) system

The reliability of a stand-alone HEW system is usually expressed either using the number of hourly load rejections during a given time period (e.g. 1 year period) or in terms of loss of load probability LLP (Kaldellis et al., 2004; Celik, 2007). Therefore the no-load rejection case – or equivalently the LLP = 0 value – corresponds to total energy autonomy of the system during the complete time period examined. However, in several cases there is a possibility of not exactly fulfilling the load demand for specific time



113

intervals, since the desired system reliability is directly dependent on the applications supported. In these cases the system size (especially the energy storage capacity and the diesel-oil consumption) is significantly reduced, making the stand-alone HEW systems more financially attractive. The key-element of selecting the system dimensions on the basis of minimum permitted reliability (or LLP) is the non-energy (electricity) fulfilment cost parameter A. More precisely, A describes (in C/h) the cost of not covering the electricity demand of the system per hour. In order to obtain the no-load rejection case one simply assigns to A an arbitrary high value (i.e. A → ∞). Of course one should take into consideration that the exact value of A is not easily determined and may also depend on the reliability level (hours with no electricity) selected. In an attempt to include the required system reliability in the proposed analysis, the non-energy (electricity) fulfilment cost function Nn is introduced in the generalized total operational cost function of the system; equations (4.15) and (4.16). Actually Nn describes the corresponding noenergy fulfilment (or alternative energy coverage) cost function for an n year time period, also in constant values. Thus the no-energy fulfilment cost function can be approximated as: ⎛ 1+ a⎞ − 1 1+ a ⎝ 1+ i ⎠ Nn = h ⋅ A ⋅ 1+ i 1+ a − 1 1+ i n

4.27

where α is the time-mean annual change of A (Muselli et al., 1999; Kaldellis, 2003b) and h represents the hours per annum that the consumption is not covered by the existing HEW system. More precisely, one may write: h = 8760 · (1 − R)

4.28

with R being the corresponding reliability level. At this point it is important to mention that in equation (4.27) it is indirectly assumed that A remains constant and independent of h during a year. Substituting equations (4.27) and (4.28) into equation (4.16), one finally gets: x n − 1 cf ⋅ Mf yn − 1 ⎡ ⎤ Cn = IC 0 ⋅ ⎢(1 − γ ) + m ⋅ x ⋅ + ⋅ y⋅ + Ψ ⎥ − Yn + N n x−1 y−1 IC 0 ⎣ ⎦

4.29

Thus, for any reliability level R selected by the consumer, the minimum cost value Cn is predicted and therefore the minimum total cost can be computed based on the A value decided. Recapitulating, for every case analysed, Cn is a function of R, since all the governing parameters of the


114


hybrid system (Qmax, Mf, No, z) are in fact functions of R, hence a minimum Cn value can be estimated according to the desired R level and the assumed numerical value of A (Kaldellis, 2003b).

4.6

Electricity generation cost of a typical standalone hybrid electricity generation wind-based (HEW) system

Using the above analysis and considering that the proposed HEW system produces approximately E kW h per year, one may estimate the corresponding energy production cost ce by dividing the present value of the installation total cost with the corresponding annual electricity production. In order to obtain more realistic results and have the opportunity to compare the present cost of the proposed solution with the current electricity generation values, the corresponding annual escalation rate of the HEW station electricity generation cost p is also included (Kaldellis, 1991). Thus, using equation (4.16) one may write: xn − 1 m ⋅ IC 0 ⎛ x ⎞ x − 1 cf ⋅ Mf ⎛ y ⎞ IC 1−γ + ⋅ ⋅ ce = 0 ⋅ + ⋅ ⋅ n ⎝ z⎠ ⎝ z ⎠ zn − 1 z 1 − E E E z⋅ z−1 z−1 Ψ Υn − zn − 1 zn − 1 E ⋅ z⋅ E ⋅ z⋅ z−1 z−1

yn − 1 y−1 + zn − 1 z−1 4.30

where: z=

1+ p 1+ i

4.31

Bear in mind that the proposed model also includes the diesel-only solution (i.e. IC0 = φ.Nd, No + NRES = 0, rb = 0, Mf = Mmax) as well as the zero-diesel configuration (i.e. ICd = 0, rd = 0, Mf = 0). Analysing the terms of equation (4.30) one may distinguish the impact of the major terms on the electricity generation cost of the HEW station. More precisely, one should take into consideration the critical role of the annual energy consumption (production) of the installation, underlining the significance of the optimum sizing. Accordingly, the first term of the RHS of equation (4.30) describes the impact of the initial cost of the installation (equations (4.1) and (4.7)), taking also into consideration the possibility of state subsidization, the capital cost impact and the corresponding service period of the hybrid power station.



115

At this point it is important to clarify that in case that z → 1.0 (i.e. p ≈ i) the term [(zn − 1)/(z − 1)] of equation (4.30) tends to n, i.e. the service period of the installation, thus equation (4.30) reads: ce =

x n − 1 co ⋅ Mf yn − 1 m ⋅ IC 0 Υ IC 0 Ψ ⋅x⋅ + ⋅ y⋅ + − n ⋅ (1 − γ ) + n⋅E n⋅E x−1 n⋅E y−1 E⋅n E⋅n

4.32

Subsequently, the second term of the RHS of equation (4.30) describes the maintenance cost impact, while the third term presents the impact of the annual diesel-oil consumption. The last two terms of equation (4.30) express the influence of the variable (replacement) cost and the impact of the residual value of the installation on the electricity generation cost.

4.7

Socio-environmental impacts of stand-alone hybrid electricity generation wind-based (HEW) systems

HEW systems are characterized as one of the most environmentally friendly and socially advanced solutions for the electrification of remote consumers worldwide. In most cases they replace heavy polluting diesel-electric generators, thus avoiding the corresponding air pollutants’ emission, limiting oil transportation and minimizing oil imports. On top of these, remote consumers, being far from decision centres and having limited political influence, usually feel abandoned, facing a dramatically insufficient infrastructure. In several cases, their importance is not only based on socio-economic criteria but also on the preservation of national interests. In this context, the proposed HEW solution, although suggesting small systems, may be used to cover the pressing energy needs of these remote consumers and thus improve their quality of life by satisfying vital daily needs through electrification. On the other hand, one should also investigate any potential environmental impacts related to the operation of a similar HEW system. At this point it is important to remember the relatively limited size of the proposed installation, usually based on a small wind turbine, a fair number of PV panels, a rational energy storage (lead–acid batteries) device along with the corresponding electronic components and a diesel-electric generator operating mainly as back-up engine.

4.7.1 Wind turbine Objectively speaking and depending on the landscape characteristics, modern wind turbines – with a hub height of 60–100 m and a blade length


116


of 30–50 m – are a noticeable addition to the scenery (Kaldellis and Kavadias, 2004). However, the size of the specific engines (rated power less than 100 kW) utilized and the remoteness of the area minimize the visual impact. Besides, it is a matter of aesthetics – to a large extent – that configures how people perceive wind turbines fitting into the landscape. Reaction to the sight of a wind turbine is highly subjective, with most remote consumers, however, appreciating them as a welcome symbol of clean and abundant energy to support their activities. Another objective visual impact is the effects of periodic reflections (glinting) and sunlight interruption (shadow flicker) from the rotor blades (European Commission, 1999). Wind turbines, like other tall structures, will cast a shadow (or a reflection) on the neighbouring area when the sun is visible. This becomes a serious problem when turbines are sited very close to workplaces or dwellings, occurring during periods of direct sunlight. These effects may be easily predicted and avoided by carefully considering the machine-site and the surface finish of the blades. A common guideline used is a minimum distance of 6–8 rotor diameters between the wind turbine and the closest neighbour. Another important aspect of wind turbines’ operation is noise emission. Sound emissions from wind turbines may have two different origins, i.e. mechanical noise and aerodynamic noise. Additional analysis reveals (Kaldellis et al., 2006c) that for most turbines with rotor diameters up to 20 m the mechanical component is the dominant one, whereas for larger rotors the aerodynamic component is the significant one. More precisely, mechanical noise may originate in the gearbox, in the drive train (the shafts) and in the electrical generator of the wind turbine. On the other hand, three main categories of aerodynamic noise sources (Berglund et al., 1996; Persson and Ohrstrom, 2002) may be distinguished: •

Discrete low-frequency noise at the blade passing frequency and its harmonics. • Self-induced noise due to direct radiation by the attached boundary layer on the rotor blade, due to flow field separation at the blade trailing edge and finally due to trailing edge instabilities involving quasi-discrete frequencies. • Broadband noise due to interaction between the inflow turbulence and the rotor. For almost all-existing commercial wind turbines operating under normal conditions, the most significant noise source is the self-induced noise of the blades. Generally speaking, no landscape is constantly completely quiet, since birds, animals and human activities create sound. Thus, when the wind meets different objects at a certain speed, it causes a sound. From a technical point of view, as wind speed approaches 6–7 m/s, the noise from the



117

wind in leaves, shrubs, trees, masts, etc. (background noise), will gradually mask any potential sound from wind turbines. Of course, sound reflection or absorption from terrain and building surfaces may change the sound picture in different locations. It is, therefore, important to chart the potential dispersion of sound in different directions (Kaldellis et al., 2003). Other parameters being equal, sound pressure will increase with the fifth (4th to 6th) power of the speed of the blade relative to the surrounding area, which explains why modern wind turbines operate at low rotational speed. On top of that, the energy in sound waves (and thus the sound intensity) drops with the square of the distance from the sound source. Summarizing, the sound pressure level at a distance of 40 m from a typical machine is 50–60 dB(A), about the same level as a conventional speech. Ten years ago, wind turbines were ‘louder’ than they are today. Serious effort has been devoted to create the present generation ‘quiet’ machines, paying much attention to both the design of the blades (Kaldellis et al., 1991; Guidati et al., 1999; Tachos et al., 2009) to avoid boundary layer separation (Kaldellis, 1993a) and to the mechanical parts of the machine. As a result, noise is a minor problem for modern carefully sited wind turbines. Subsequently, taking into consideration the relative limited size of wind turbines used in a HEW and the special land characteristics of a stand-alone installation, it is fairly obvious that the impact on birds is almost negligible, while there is also no serious land occupation by the proposed installation (Kaldellis et al., 2003). The last issue briefly analysed concerns the energy and the materials used during the construction and installation of a wind turbine. Though wind turbines do use energy-intensive materials, such as steel, glass reinforced polyester (fibreglass), and concrete (for foundations), according to three separate European studies (European Commission, 1999) they quickly repay the energy consumed during their construction. As expected, most of the energy used to manufacture the turbine is contained in the rotor and nacelle. But more than one-third of the total energy consumed by the wind turbine is contained in the foundation and the tower of the machine. According to the results obtained, at good windy sites, wind turbines pay for the energy embedded in their materials within the first 3–4 months, while even at poor sites, energy payback occurs in less than a year. Finally, from the data gathered, it is clear that the material inputs required for a wind farm are dominated by the concrete (reinforced) foundations for the turbine and by the steel from which the turbine towers are fabricated. It is conceivable that a wind farm could, on reaching the end of its operating life, be refurbished by installing new nacelles and rotors on top of the existing towers and foundations. This would reduce the material inputs required for the ‘second generation’ wind farm by even more than


118


80%. Lastly, if there is sufficient demand for the secondary raw materials, wind turbines can be regarded as being mainly composed of recyclable materials. The principal unresolved issue from an environmental perspective is the recycling of rotor blades (Holttinen et al., 1999). At this point it is important to mention that the operation of HEW systems contributes (Kaldellis, 2002d; Kaldellis et al., 2008) to CO2 emissions reduction. More specifically, for every kW h produced by the wind turbine almost 700 gr of carbon dioxide (otherwise emitted by the diesel generator) are avoided. Finally, the operation of wind-based power stations strongly contributes to the reduction of SO2 and NOx emissions assumed responsible for acidification agents. The most important quantified effects of acid deposition are upon human health, building materials, historical monuments and commercial forestry. Furthermore, there are major impacts upon ecosystems, both terrestrial and aquatic. According to damage costs derived using previous estimates of acidification (Hohmeyer, 1988), an optimistic value is approximately 6000C per tonne of either SO2 or NOx.

4.7.2 PV generator The PV technology has distinct environmental advantages over conventional technologies for electricity generation. The operation of PV systems does not produce any noise, toxic gas emissions or greenhouse gases. As in the wind turbine case, PV generators contribute to reducing the emission of CO2 along with the additional air pollutants produced from fossil fuels (Tsoutsos et al., 2005; Kaldellis et al., 2008). Although many researchers characterize PV stations as especially land-intensive installations, the actual PV’s land-use requirements are similar to those for coal production and combustion. Besides, PV’s material requirements are extremely low (e.g. 1 MW h/g semiconductor material), while the semiconductor materials used in solar cells do not pose the environmental problems related to other conventional technologies, e.g. uranium and fission by-products (Miles et al., 2005). As with any energy source or product, there are some minor hazards associated with the manufacture, use and disposal of solar cells. Although the PV industry uses far smaller amounts of toxic and flammable substances than many other industries, its use of hazardous chemicals may present occupational and environmental hazards (Moskowitz and Fthenakis, 1991; Patterson et al., 1994). On the other hand, the recyclability of PV systems (at the end of their useful life) adds to the environmental benefits, and can further enhance market support. Also, recycling addresses the public’s concerns regarding certain materials used in PV modules (e.g. Cd, Pb), which can create barriers to market penetration (Fthenakis, 2000).



119

Another important issue related with the application of PV generators is the corresponding energy pay-back (amortization) period (Kaldellis et al., 2009c). More specifically, the life-cycle energy analysis should involve the stages of construction, installation, maintenance and final decommissioning of the plant. Details concerning the energy sustainability of PV stand-alone systems may be found in several references (see for example Alsema and Nieuwlaar, 2000; Raugei et al., 2007; Kaldellis et al., 2009d). According to the available information the energy pay-back period of a PV array varies between 4 and 7 years, depending on the technology applied and the solar potential of the installation area. Besides, the system batteries add another 3.5 to 5 years in comparison with the energy pay-back regarding only the PV modules and the balance of the system (BOS) components.

4.7.3 Battery bank The environmental impacts of the HEW batteries result either from the manufacturing process or due to the operation and final disposal of them (Rydh and Sandén, 2005; Rudnik and Nikiel, 2007). The solid waste generated during the manufacturing process consists of the following: • • • • • •

dross; scrap plates; scrap batteries; scrap plastic material; scrap envelope material; sludge.

Accordingly, wastewater generated during the process is due to the following: • • • •

washing and cleaning of equipment; battery washing; discharge from scrubber; process cooling water being discharged.

Lead and sulphates are the major impurities in wastewater from this facility. The water used for cooling batteries in the wet room (charging section) and the water used to wash batteries consists of sulphate impurity. Air emissions during the process occur as a result of handling of plates, parting and collection of plates and other similar activities, which generate lead dust. The air in the plant is thus contaminated with lead particles. The ventilation system on site takes air from the site and discharges it into the atmosphere. Sulphur dioxide is emitted from the air emissions from the wet room and is discharged through a scrubber into the atmosphere. Although


120


quite rare, during the HEW operation, improper handling of batteries may result in explosion, burn or heat generation. Finally, one of the main disadvantages of batteries as an energy storage option is their end-life disposal issue (Morrow, 2001). The more environmentally friendly solution is based on recycling the batteries, keeping in mind that under no circumstances can batteries be incinerated as this can cause them to explode (Kaldellis and Kondili, 2006).

4.7.4 Electric and electronic equipment The materials used for construction of the electric and electronic equipment in a HEW system are steel, aluminium, copper and regular electronic equipment, which are associated with the standard industrial hazards (Konstantinidis et al., 2001). Thus, one may find the corresponding (generally low or medium) environmental impacts in the international literature.

4.7.5 Diesel-electric generator The utilization of an internal combustion engine is directly related with all the environmental impacts of a typical thermal power station (Spyropoulos et al., 2005). For the specific small diesel-electric generators employed in several applications one may note the leaked or spilled diesel fuels, the corresponding diesel smoke along with the SO2 and NOx emissions, etc. All these contribute to global warming, water and soil pollution, remarkably affecting the life quality and the health status of the nearby communities. Summarizing the analysis of socio-environmental impacts of HEW systems one may state that the environmental attractiveness, the reduction of dependence on energy imports, the existing natural resource preservation as well as the implicit costs of conventional energy systems (e.g. accumulation of dangerous pollutants) have not been yet sufficiently considered in the existing cost–benefit analysis of wind-based systems (Kaldellis, 1993b). Although it is very difficult to quantify all components of social cost, one should not disregard this important parameter during the evaluation of HEW systems. In this context and based on the free market economic theory, the social cost of fossil fuel-based electricity generation has to be introduced in the energy market price, according to the rule ‘the polluter pays for the damage’. This additional amount must be used in order to cover the social cost of the energy production as well as to prevent the environmental deterioration and the local energy resources’ overexploitation. At the same time, the wind energy applications have to be encouraged, due to their net positive



121

social effects. It is therefore possible to subsidize similar investments, paying in advance the expected socio-environmental gains from their operation replacing fossil fuel-based power stations. This is currently the case for all the RES-based applications all around the world. For example, in Greece remarkable initial cost subsidies (up to 60%) are considered by the existing legislation (Kaldellis and Kodossakis, 1999).

4.8

Analysis of case studies of stand-alone hybrid electricity generation wind-based (HEW) systems

In the current section one may find some representative results concerning the financial performance of selected HEW systems. For this purpose three representative wind potential areas have been selected, namely the islands of Kea (medium wind potential), Kithnos (high wind potential) and Andros (very high wind potential). More precisely, Andros is a small to medium-sized island (the second biggest one) of the Cyclades complex (population 12 000, area 384 km2), located in the middle of the Aegean Sea (Fig. 4.3). The local terrain includes several rocky mountains with relatively sharp slopes. The island has one of the best wind potentials in Greece (average wind speed approximately 10 m/s), while on the island

V = 3.2

V = 5.3 Andros

Athens V = 3.2

V = 7.5

Kea

Naxos

Kithnos

Aegean Sea V = 5.3

h = 30 m

4.3 Wind potential map for Aegean Sea area at 30 m height.


122


Probability density function f(V)

there is quite an old wind park of 9 × 225 kW V-27 wind turbines, which has been operating with outstanding results since 1993 (Kaldellis and Kodossakis, 1999). Kithnos is also a small island (1700 inhabitants, area 94 km²) in the Aegean Sea, located approximately 60 km southeast of Athens. The topography of the island is typically Aegean, i.e. gentle slopes, absence of flat fields, low mountains and sparse vegetation. Owing to the insufficient infrastructure (e.g. road network) there are many isolated consumers, who have no access to the local electrical grid. The island has an outstanding wind potential, since in several locations the annual mean wind speed approaches 7 m/s, at 10 m height. Finally, Kea is a small island (2300 inhabitants, area 103 km2) close to Athens. The local topography is similar to Kithnos, while the corresponding wind potential is good enough (annual mean wind speed ≈ 6.0 m/s) to feed contemporary wind turbines for electricity production. Using the available wind speed data (Kaldellis and Tsesmelis, 2002) for a 3-year period, the experimental 3 year mean wind speed probability density function distribution f(V) is created, for all three regions investigated; see also Figs 4.4 to 4.6. It is important to mention that the 3 year period selected includes the maximum calm spells during a 15 year period, where extensive wind speed measurements exist. As expected, the Andros wind potential is quite higher than those for Kithnos and Kea, while the last two regions maintain similar quality wind potential. Of course the zero-wind speed possibility is much higher in Kea (≈20% of the year) than in Kithnos. The zero-wind speed possibility is almost zero (≈2%) for Andros.

0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 0.5

2.5

4.5

6.5

8.5 10.5 12.5 14.5 16.5 18.5 20.5 22.5 24.5 26.5 28.5 Wind speed (m/s)

4.4 Wind potential data for Andros Island.



123


0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5

Wind speed (m/s)

4.5 Wind potential data for Kithnos Island.


0.22 0.20 0.18 0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 10.5 11.5 12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5

Wind speed (m/s)

4.6 Wind potential data for Kea Island.

Finally, in Table 4.1 the annual average wind speed values for all three years and regions examined are presented, along with the corresponding Weibull parameters (i.e. C is the wind speed normalizing factor and k is the corresponding shape factor) and the maximum duration of calm spells. According to the available information, based on detailed measurements of 3 years, one may conclude that Andros possesses an excellent wind potential with very high wind speeds and limited calm spell periods. Subsequently, although no considerable difference is noted, the wind potential of Kithnos is slightly better than that of Kea. Bear in mind that both islands present high wind speeds all year round, although the existence of remarkable calm spell periods cannot be disregarded.



8.892

9.478

1.842

36

C (m/s)

k

Max calm spell duration (h)

34

1.834

10.234

9.565

37

1.83

9.777

9.011

3rd year

178

1.364

6.563

6.4535

1st year

2nd year

1st year

_ V (m/s)

Kithnos

Andros

185

1.337

6.717

6.7855

2nd year

Table 4.1 Annual wind potential characteristics of the remote areas analysed

188

1.264

6.445

6.392

3rd year

165

1.302

5.273

5.482

1st year

Kea

158

1.299

5.165

5.403

2nd year

161

1.313

5.438

5.596

3rd year


125

4.8.1 Comparison of available solutions for a stand-alone system Applying an optimum sizing methodology (Kaldellis and Tsesmelis, 2002) for a stand-alone wind-based system for the island of Andros (high wind potential) the corresponding first installation cost of the system is quite high, approaching 18 000C. In order to check the viability of the proposed wind-based solution to meet the electricity demand of a remote consumer (Etot ≈ 4700 kW h/year) at rational cost, a preliminary comparative study is undertaken including also the possibility to realize an electrical grid connection or to use a small autonomous diesel-electrical generator system. Grid extension solution According to the existing market data the typical grid extension cost for a remote consumer to be connected to the local grid using overhead-medium voltage lines is approximately 10 000C/km. At the same time the current final electricity market price for all Greek consumers is 0.12C/kW h. Neglecting for simplicity reasons the time-variations of the above parameters, the total electricity cost CGC of the consumer after n years of utilizing the extended electrical grid, being at a distance z (km) from the existing electrical network, can be described by the following relation: CGC = 1000 · z + 0.12 · Etot · n

4.33

Use of a diesel engine The most widely applied solution for the remote consumers to fulfil their electrification needs is to install a small internal combustion engine in combination with a small electrical generator. Although the overall efficiency of such a system is quite small (ηd ≈ 20%) the corresponding buy cost is very low (≈150–250C/kW), increasing the short-term economic attractiveness of this solution. On the other hand, the service period life of a wholeyear operating system is taken equal to 6 years and the corresponding M&O cost (mainly due to fuel cost) is between 3100 and 5000C per year. Consequently, selecting a 5 kW autonomous system and accepting a 0.8 to 1.7C/l cost of the diesel oil used (the maximum value takes into account the increased transportation cost), the total electricity cost Cd of the installation after n years of operation is given as: Cdmin = 700 + 3100 · n + Vn

4.34

Cdmax = 700 + 5000 · n + Vn

4.35

or


126


where Vn describes the replacement cost of the diesel engine every 6 years. Wind energy-based stand-alone solution Using the optimum configuration dimensions (under the no-load rejection restriction) of the stand-alone system for every island analysed (e.g. Andros, IC0 = 18 000C) and a 3% (m = 0.03) annual M&O cost coefficient (Kaldellis, 2002b), the total electricity production cost by applying the wind energy solution can be approximated as: CWE = IC0 + m · IC0 · n + V′n

4.36

while V′n term is used to describe the battery replacement cost (e.g. for Andros, 11 700C) every 7 years. For comparison purposes, the calculation results are summarized in Fig. 4.7, for various distances from the existing grid (1 ≤ z ≤ 10 km) and for the three regions investigated. As is clearly stated by the results of Fig. 4.7, for z < 1 km the grid connection is the best choice, for medium to long-term operation of the system (n > 4 years). Accordingly, for Andros Island, the proposed stand-alone system is an economically interesting solution for (2 ≤ z ≤ 4 km), especially when the maximum diesel cost values are taken into consideration. For z ≥ 4 km the proposed stand-alone wind power solution is by far the best alternative, excluding the minimum diesel cost production scenario and short-term operation cases (i.e. n ≤ 6 years). Electrical grid (1 km) Electrical grid (3 km) Electrical grid (5 km) Electrical grid (7 km) Electrical grid (10 km) Diesel (min) Diesel (max) Andros (stand-alone) Kithnos (stand-alone) Kea (stand-alone)

180 000 160 000 Total cost (a)

140 000 120 000 100 000 80 000 60 000 40 000 20 000 0 0

2

4

6

8

10

12

14

16

18

Years

4.7 Total cost comparison of available electrification solutions.


20


127

Subsequently, the proposed stand-alone configuration for Kithnos presents a financially competitive advantage for installation regions being more that 7 km (z ≥ 7 km) away from the local grid and for medium to long-term operation cases (n ≥ 9 years). Finally, for Kea, the financial advantage of the proposed stand-alone wind power system, in comparison with the other options, is validated for relatively long distances from the local electrical network (z ≥ 12 km) and for long-term operation of the system (n ≥ 12 years). In all other cases, the diesel or the grid extension solutions should be preferred on a pure energy production cost basis and under the current techno-economic situation.

4.8.2 Operational years’ impact on the total cost of a wind-based stand-alone system The next case study includes the total cost analysis of a wind-based standalone system (without diesel-oil contribution) for the island of Kithnos. Emphasis is given in order to investigate the impact of operational years of the system on selecting the minimum cost (optimum) configuration. Actually, using the available wind potential data (Fig. 4.5) and applying the optimum sizing algorithm by Kaldellis (2002c), one may find in Fig. 4.8 the no-load (energy autonomy) curve (No–Qmax), combining the minimum wind turbine rated power No and battery capacity Qmax values that guarantee the energy autonomy of the system for the entire 3 year period examined. Accordingly, in Fig. 4.8 the constant initial cost curves (i.e. IC0 = ct) are drawn, using equation (4.1), with Np = 5 kW. The minimum first installation cost solution is based on a 9.5–10 kW wind turbine and on 18 000 A h (24 V, 60 000 30 000 a 36 000 a 42 000 a Zero load points

50 000

Qmax (Ah)

40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.8 No-load rejection configuration on the basis of minimum initial cost (included 40% state subsidization) for Kithnos.


128


75% DODL) battery capacity, while the corresponding initial investment cost (without taking into account the 40% State subsidization) is approximately 70 000C. Subsequently, considering that a similar stand-alone wind power system is being developed to operate during the next 10–20 years, it seems appropriate to investigate primarily the 10 year total energy production cost distribution, if a medium-term evaluation of the available energy autonomy (No–Qmax) combinations is expected. Thus, in Fig. 4.9 the constant C10 curves are also given, pushing the minimum energy production cost configuration to lower Qmax values (= 15 000 A h) and to higher wind turbine sizes (No = 14–15 kW). During the calculations it is assumed that the local economy inflation rate g is 4%, the corresponding market capital cost i is 9% and no remarkable technological improvements concerning batteries occurs (ρb = 0), i.e. battery replacement takes place every 7 years of operation. The minimum 10 year energy cost solution future value is almost 200 000C or 135 100C in constant values. On the other hand, the minimum first installation cost solution is by 8% more expensive than the respective minimum 10 year total cost. Finally, extending our calculation to 20 years (i.e. long-term operation), being the usually acceptable wind turbine replacement period, the corresponding minimum energy production cost C20 value is obtained (Fig. 4.10), for 16 kW wind turbine rated power and 13 500 A h battery capacity. The corresponding minimum 20 year energy cost is 280 700C in constant (present) values.

60 000 100 000 a 150 000 a 200 000 a Energy-autonomy points

50 000

Qmax (A h)

40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.9 No-load rejection configuration on the basis of minimum 10 year cost, Kithnos.


Feasibility assessment for stand-alone and HEW systems 60 000

400 000 E 500 000 E 600 000 E 700 000 E Energy autonomy points

50 000 40 000 Qmax (A h)

129

30 000 20 000 10 000 0 3

5

7

11

9

13

15

17

No (kW)

4.10 No-load rejection configuration on the basis of minimum 20 year cost, Kithnos.

Table 4.2 Optimum stand-alone wind power system dimensions, using variable financial evaluation criteria for Kithnos island Minimum cost solution

Initial cost

10 years cost

20 years cost

Wind turbine size Battery capacity

9.5 kW 18 000 A h

14.5 kW 15 000 A h

16.0 kW 13 500 A h

Recapitulating, the minimum energy production cost scenario is quite different if the evaluation is based on the system initial cost value only, instead of using the system 10 year or 20 year total cost configuration (see Table 4.2). More precisely, for medium or long-term operation, a remarkable increase of the wind turbine rated power occurs, along with a significant decrease of the necessary battery capacity.

4.8.3 Reliability impact on the total cost of a wind-based stand-alone system In the following, the above-described total cost analysis (Section 4.4) applies to two separate cases, for which detailed wind potential data are given (Figs 4.4 and 4.5). More precisely, the first case concerns Andros (excellent wind potential), while the second case examined corresponds to a stand-alone system installed on the island of Kithnos (medium–high quality wind potential). Note that in both cases the analysis is carried out by using one year’s wind speed measurements (Kaldellis, 2003b).


130

Stand-alone and hybrid wind energy systems R = 100% R = 99.9% R = 99.5% R = 99% R = 98% R = 95% 30 000 E 40 000 6 50 000 E 60 000 E 70 000 E Best points

Battery capacity Qmax (A h)

12 000 10 000 8000 6000 4000 2000 0 2000

4000

6000

8000

10 000

12 000

14 000

16 000

Wind turbine rated power No (W)

4.11 The relation between the configuration of a stand-alone wind power system and the 10 years total cost, for variable reliability values; Andros.

Thus, in Fig. 4.11, six distinct numerical curves are drawn representing the zero load rejection solution (R = 100%, h = 0) and the R = 99.9%, 99.5%, 99%, 98%, 95% cases for Andros. More specifically, each point belonging to these curves represents a stand-alone wind power system minimum configuration (i.e. minimum wind turbine rated power and minimum battery capacity) that guarantees a given reliability value for Andros and for a year-long period. In the same figure the corresponding 10 year total cost constant-value curves are also drawn, in order to estimate the minimum total cost solution for every reliability level. All the minimum cost points are represented by the ‘best points’ curve also given in the figure. In fact, there is a remarkable total cost diminution as the required theoretical reliability is decreased from 100% to 95%. This 10 year total cost reduction may be in the order of 55% as the desired system reliability drops from the theoretical value of 100% to 95%. Even for high reliability values (e.g. 99%) the 10 year total cost diminution is significant (almost 20 000C), while the corresponding hours without electricity are less than 100 per year. It is, however, worth mentioning that for moderate reliability decrease – between 100% and 99% – a considerable battery capacity diminution appears (approximately 50%), while the corresponding wind turbine size remains unaffected. For lower reliability values, though, the battery capacity remains almost constant as the wind turbine rated power decreases from 6 to 2.5 kW.



R = 100% R = 99.9% R = 99.5% R = 99% R = 98% R = 95% 70 000 E 90 000 E 110 000 E 130 000 E 150 000 E Best points

40 000 Battery capacity Qmax (A h)

131

35 000 30 000 25 000 20 000 15 000 10 000 5000 0 2000

4000

6000

8000

10 000

12 000

14 000

16 000

Wind turbine rated power No (W)

4.12 The relation between the configuration of a stand-alone wind power system and the 10 years total cost, for variable reliability values; Kithnos.

Similar conclusions apply to Kithnos (Fig. 4.12). According to the calculation results obtained, there is considerable variation of the system optimum size as the desired reliability varies between 95% and 100%. More specifically, the optimum battery capacity declines from 11 900 A h (R = 100%) to only 4300 A h (R = 95%), while at the same time the wind turbine rated power required is also decreased from 12 to 7 kW. Finally, the corresponding initial cost reduction is more than 50%. As in the Andros island case, the reliability decrease – between 100% and 99% – is mainly realized by reducing the optimum battery capacity (from 11 900 to 7500 A h), while the corresponding wind turbine nominal power decreases by less than 1 kW. For inferior reliability, on the other hand, the battery capacity is fairly decreased, while the wind turbine rated power detracts from 11 to 7 kW. Summarizing, one may clearly state that the dimensions and the initial investment cost of a stand-alone wind power system are substantially limited as the required – by the consumer – system reliability decreases from the theoretical value of 100% to a fair value, like 99% or in less crucial applications to 95%. To be more precise, the parameter that usually determines the reliability lower boundary of a stand-alone system is the noenergy fulfilment cost per hour (or the alternative energy coverage cost) of the remote installation.


132


150 000 140 000

Total cost (E)

130 000 120 000 110 000

A=0 A = 10 A = 15 A = 20 A = 30 A = 50

100 000 90 000 80 000 70 000 60 000 50 000 94

95

96

97

98

99

100

Annual reliability (%)

4.13 The impact of system reliability on a stand-alone wind power installation 10 years operational cost for Kithnos.

In this context, the proposed methodology is applied to an autonomous wind-power system operating in Kithnos. The present analysis considers the no-energy fulfilment cost parameter A, while the results obtained are expressed as a function of the system reliability. Thus, in Fig. 4.13 the 10 years system total cost (see equations (4.27) to (4.29)) is given as a function of the desired system reliability, using the hourly no-energy fulfilment cost A as the fundamental parameter of the problem. According to the results of Fig. 4.13, for 0 < A ≤ 25C/h there is an optimum reliability value that minimizes the 10 years system total cost. For higher A values, the analysis ‘dictates’ the maximum technically realized system reliability. On the other hand, for low A values (A → 0) the system reliability cannot be estimated by a similar model, thus other factors may determine the installation characteristics, e.g. in this approach Rmin is set equal to 95%. As a general conclusion from the several representative cases analysed (Kaldellis, 2003b), one may state the following: • •

•

When the A value is approximately identified, there is an optimum system reliability value (R ≈ 98–99.5%) minimizing the system total cost. When the desired (or minimum acceptable) reliability limit is determined by the requirements of the applications supported by the standalone installation, system configuration and total cost is a function of the numerical value of parameter A. In cases that both R and A are given, the optimum system size and total operational cost may be computed, depending mainly on the available wind potential of the installation site.



133

4.8.4 The impact of PV panels on the total cost of a HEW system One of the most expensive components of a HEW system is the battery bank – necessary to guarantee system reliability. Thus, in cases of increased system autonomy, the battery contribution to the initial or the total operational cost is found to be dominant (Kaldellis, 2002c). In addition, batteries should be replaced every 4–7 years (Cherif et al., 2002), thus increasing the operational cost of the system. Hence, in cases with remarkable solar potential, the introduction of a small PV generator (Kaldellis et al., 2000; Muselli et al., 1999), in a stand-alone HEW station is expected to smooth out the system energy production, significantly decreasing the energy storage requirements, without modifying the first installation cost of the system (Kaldellis, 2003c; Rever, 2001). Additionally, the subsidy percentage for small PV systems is normally considerably higher (50–55%) than the corresponding one for small wind power stations (30–40%). On top of that, the operational lifetime of a contemporary PV system is close to 30 years. In this context, in the current section the possibility is considered of reducing the battery size of stand-alone wind power installations – mainly installed in medium – low wind potential areas – by incorporating a small PV generator (Kaldellis et al., 2006a). More precisely, in order to check the viability of the proposed wind– solar-based solution, to meet the electricity demand of a remote consumer at a rational cost, a preliminary comparative study is undertaken, including also the possibility of using a small autonomous diesel-electrical generator system. The most widely applied (to now) solution for the remote consumers is to install a small internal combustion engine in combination with an appropriate electrical generator. Although the efficiency of such a system is quite small (≈20%) the corresponding buy cost is very low (≈150–250C/kW), increasing the short-term economic attractiveness of this solution. Consequently, selecting a 5 kW autonomous system the total electricity cost Cd of the installation after n years of operation is given from equation (4.35). Accordingly, using a wind–PV–battery configuration (Kaldellis et al., 2006a) based (Kithnos) on a wind turbine of rated power 7.5 kW and taking into consideration a 3% (m = 0.03) annual M&O cost coefficient, the total electricity production cost by applying the wind–solar energy solution can be approximated by equation (4.36), where the V′n term is used to describe the battery replacement cost (depending on the PV panels selected) every 7 years. The initial installation cost IC0 of the proposed solution includes the ex-works price of the wind turbine (7.5 kW), the cost of buying the battery and the PV panels (Table 4.3) and the corresponding balance of


134


Table 4.3 Main parameters of the Kithnos stand-alone system analysed Photovoltaic module number


Initial cost (C)

Initial cost (C), including subsidy

Battery replacement cost (C)

0 20 50 100

16 700 13 100 8 800 4 600

58 666 57 898 58 930 66 949

29 333 28 949 29 465 33 475

39 425 31 518 21 840 12 009

160 000 z=0 z = 20 z = 50 z = 100 Diesel

140 000

Cost (E)

120 000 100 000 80 000 60 000 40 000 20 000 0 0

2

4

6

8

10

12

14

16

18

20

Years

4.14 Life-cycle cost analysis: comparison between the proposed wind-solar based and a typical dispatchable diesel generator solution.

the plant cost, including any additional electronic equipment required (equation (4.1)). Bear in mind that similar applications, based on the exploitation of renewable energy sources, are strongly subsidized by the Greek state: up to 50% for combined wind-solar based systems. For comparison purposes, the calculation results are summarized in Fig. 4.14, for various combinations of PV modules and battery capacity (Table 4.3). As is clearly shown by Fig. 4.14, the dispatchable diesel generator scenario presents a financial advantage during the first 5–8 years of operation of the installation, excluding any excessive oil price augmentation. Accordingly, the proposed stand-alone HEW system is by far the best option, especially if the number of PV modules exceeds 20 (i.e. z > 20). On top of these, a significant battery size reduction is encountered for several representative HEW stand-alone systems located in islands of



135

medium–low wind potential, when a small PV generator is introduced to the original wind power stand-alone system, designed to meet the electricity needs of a typical remote consumer (Kaldellis et al., 2006a). On the other hand, the incorporation of PV panels in similar systems may be characterized as a rather expensive improvement, which does not have practical results. Contrary to this general belief, one may prove that this proposal is in fact financially beneficial, if the money saved by the replaced battery capacity counterbalances or overwhelms the introduced PV panels’ ex-work price, under a constant wind turbine rated power value (see also Fig. 4.14). In that case the entire system energy production cost is respectively reduced. One should not disregard the fact that a typical lead–acid battery must be replaced every 4–7 years, while the PV generator has a lifespan of more than 20 years (Masini and Frankl, 2002). This fact is obvious if one compares the z = 0 and the z = 100 solutions of Fig. 4.14. More precisely, the z = 100 solution is definitely less expensive than the z = 0 one on a life-cycle base analysis, although the z = 0 solution (zero PV panel utilization) presents a lower initial cost; see also Table 4.3. This may be attributed to the lower battery replacement cost when using PV panels as well as to the 50% initial cost subsidy by the Greek state. In this context, one may consider a greater ratio value of the battery reduction per PV panel (51Wp) (in absolute terms) than the value calculated by equation (4.37). More specifically, in equation (4.37) one may estimate the marginally considered ΔQ/Δz value according to the following relation:

( ΔQ Δz) =

Γ B

4.37

where Γ is the ex-works price of one (51Wp) PV polycrystalline panel and B is the present value of the purchase cost plus the replacement cost (every 4–7 years) corresponding to the battery capacity (24 V) reduction by 1 A h. More specifically, if the slope of the curve of the decrease in battery capacity versus the PV panel number (ΔQ/Δz) for each wind turbine size tested is steeper than the value of equation (4.37), then the battery replacement by PV panels is a less expensive option. On the other hand, if the corresponding slope is less than the one of equation (4.37), lead–acid batteries should be used instead of additional PV modules (Kaldellis et al., 2006a). Using the available current market prices (e.g. Hawkes, 1997; Haas, 2002), the numerical value of equation (4.37) varies between −250 (battery decrease versus the PV panel increase) and −100. Thus, when the battery reduction rate per PV panel introduced exceeds (in absolute terms) a specific value (e.g. |ΔQ/Δz| = 150), the replacement of lead–acid batteries by PV panels is financially beneficial. For instance, the expense of installing one additional PV panel of 51Wp is below the purchase and replacement


136


25 000 Nw = 5.0 kW Nw = 7.5 kW Nw = 10 kW Nw = 15 kW ΔQ/Δz = −150


20 000

15 000

10 000

5 000

0 0

25

50

75

100

125

150

175

200

225

250

PV panels

4.15 Maximum battery size reduction due to PV panel introduction in a wind-solar stand-alone system of Kea (solar potential type ‘C’).

cost of the battery (greater or equal to 150 A h) module – to guarantee the system energy autonomy – in constant (present) values. In order to get a clear-cut picture of the proposed modification, Fig. 4.15 demonstrates the battery capacity versus PV panels’ variation, resulting for the Kea island HEW system analysed. More specifically, in Fig. 4.15 one may find the (Qmax–z) distributions for selected constant wind turbine rated power values (ranging from 5 to 15 kW). In the same figures the |ΔQ/Δz| = 150 constant slope lines are also drawn. Bear in mind that the precise ΔQ/Δz value depends on the local market prices and the technological time evolution expected. The financial attractiveness of the proposed battery substitution by PV panels is more obvious for stand-alone systems based on relatively small wind turbines (i.e. z ≤ 115 for No = 5 kW and z ≤ 60 for No = 10 kW). Besides, the lower the available wind potential of the stand-alone HEW system location the bigger the PV panel number adopted. On the other hand, in case of better solar potential available more PV panels can be incorporated to replace battery modules. Recapitulating, one may state that the introduction of an appropriate number of PV panels leads to a significant battery size reduction of a winddriven stand-alone system. This reduction is in proportion to the corresponding solar potential and in reverse proportion to the available wind potential. The exact size of the PV power penetration in the wind power stand-alone system should be the result of a detailed cost analysis based on the batteries’ and the PV panels’ market prices, with additional consideration of the expected forthcoming technological improvements of the sector.



137

4.8.5 Operational years’ impact on the total cost of a HEW stand-alone system The next case analysed concerns a wind–diesel system used to cover the energy demand of a remote consumer living in an area of high wind potential; the island of Andros. Applying the proposed solution (Kaldellis et al., 2006d) for the Andros case, one may obtain the (Qmax–No) distribution that guarantees one year energy autonomy for various typical annual oil quantities (i.e. Mf = 0 kg/year up to Mf = 1000 kg/year); see Fig. 4.16. Bear in mind that approximately 2000 kg of oil are necessary in order for the dieselelectric generator to meet the electricity requirements of the specific consumer under investigation without any other additional energy source. Rationally, the dimensions of the hybrid system are remarkably reduced as the contribution of diesel oil is increased. In fact, this reduction is greater when small quantities of diesel oil are used, while for larger oil quantities the battery bank size is slightly decreased for a given wind turbine rated power. Accordingly, the constant initial cost (IC0 = ct) curves are drawn in the same Fig. 4.16, which however does not take into account the annual oil quantity consumed. In this context, one should certainly select the maximum diesel oil consumption solution, since this choice minimizes the initial cost of the hybrid station. In order to obtain a clearer idea concerning the feasibility of a similar HEW system, Fig. 4.17 shows the 10 year cost variation for selected representative cases. More specifically, Fig. 4.17 presents:


25 000

Mf = 0 Mf = 25 Mf = 50 Mf = 100 Mf = 500 Mf = 1000 ICo = 20 000 E ICo = 30 000 E ICo = 40 000 E

20 000

15 000

10 000

5000

0 0

2000

4000

6000 8000 10 000 Wind power (W)

12 000

14 000

4.16 Energy autonomous configuration for a wind-diesel hybrid system, including first installation cost, Andros.


16 000

138


100 000

Mf = 0 (kg/year) Mf = 100 (kg/year) Mf = 250 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Diesel only system

90 000 Ten years cost (E)

80 000 70 000 60 000 50 000 40 000 30 000 20 000 10 000 0 0

2000

4000

6000

8000

10 000

12 000

14 000

Wind turbine rated power (W)

4.17 Ten year cost analysis of a wind–diesel hybrid system, Andros.

Table 4.4 Optimum stand-alone wind power system dimensions, using various approximations, for Kithnos

Wind turbine size Battery capacity 10 year cost in constant values

• • • • • •

10 year cost

10 years (γ = 0)

10 years (rb = 0.1)

10 years (i = 18%)

14.5 kW 15 000 A h 135 000C

15 kW 14 000 A h 180 000C

12.5 kW 16 000 A h 115 000C

17 kW 13 000 A h 154 000C

the autonomous wind–battery solution (Mf = 0 kg/year); the diesel-only solution (Mf = Mfmax = 2000 kg/year); the 5% annual diesel-oil penetration (Mf = 100 kg/year); the 12.5% annual diesel-oil penetration (Mf = 250 kg/year); the 25% annual diesel-oil penetration (Mf = 500 kg/year); the 50% annual diesel oil penetration (Mf = 1000 kg/year).

After a closer inspection of the calculation results and considering the numerical values of Table 4.4 regarding the parameters of equations (4.14) and (4.16), we may state the following comments for the 10 year cost solution: •

The optimum zero-oil solution should be based on a 4 kW wind turbine and 7100 A h battery capacity, while the corresponding 10 year cost is fairly higher than 35 300C.



139

•

In any case the zero-oil solution is slightly more expensive than the diesel-only solution, i.e. by almost 5000C (≈15%), on the 10 year basis. However, external cost is excluded from the data presented. • By increasing the diesel oil contribution the 10 year cost is remarkably reduced, being considerably lower than the diesel-only solution. • For each Mf = ct configuration there is a minimum cost area, which leads to lower battery capacity and wind turbine rated power as the diesel-oil penetration increases. This situation is quite different for a 20 year time horizon. More precisely, even the autonomous wind power solution is less expensive than the dieselonly system operation (Fig. 4.18). Additionally, the 20 year system cost diminishes as the oil penetration increases. This situation is inverted after a minimum cost point is achieved (Fig. 4.19). Thus the optimum configuration system is based on a 2.5 kW wind turbine, 1700 A h battery capacity, 300 kg/year diesel-oil consumption, while the corresponding 20 year cost in present values is 26 300C, less than 50% of the one corresponding to the diesel-only solution. Using Fig. 4.19, the optimum diesel oil contribution that minimizes the 10 year system cost can also be estimated. Hence, the corresponding optimum configuration for the specific hybrid system under investigation is based on a 2 kW wind turbine and a battery bank of 1000 A h, while the annual fuel consumption is 500 kg/year and the minimum 10 year cost approximates 17 300C in present values, being less than 60% of the dieselonly solution (≈29 300C).

140 000


Twenty years cost (?)

120 000 100 000 80 000 60 000 40 000 20 000 0 0

2000

4000

6000

8000

10 000

12 000

14 000


4.18 Twenty year cost analysis of a wind–diesel hybrid system, Andros.


140


10 000 Andros–10 year cost Andros–20 year cost Kea–10 year cost Kea–20 year cost

Mean annual cost (E)

9000 8000 7000 6000 5000 4000 3000 2000 1000 0

0

200

400

600

800

1000

1200

1400

1600

1800

2000

Annual diesel-oil consumption (kg/year)

4.19 Comparison of mean annual cost of typical wind–diesel hybrid configurations on 10 year and 20 year basis.

Finally, for comparison purposes Fig. 4.19 shows the corresponding 10 and 20 year financial results for Kea. Actually, considering the calculation results regarding two extreme wind potential cases, a HEW stand-alone system presents significant cost advantages in comparison with diesel-only or wind power stand-alone systems. These advantages are more obvious for high wind potential areas and for long-term operation. More specifically the mean annual cost for Andros is almost 40% of the corresponding cost of Kea; see Fig. 4.19. Additionally, the minimum 10 year annual cost for Andros is 1700C for Mf ≈ 500 kg/year, while the corresponding value for Kea is 2800C, while the annual diesel-oil consumption is approximately 1250 kg/year. The electricity production cost difference is more obvious in the case of low diesel-oil penetration, due to the wind potential variation between the two islands examined. The same behaviour is also valid for the 20 year operation of the wind– diesel installation. It is interesting to note that the optimum system configuration for both islands is realized for lower diesel-oil penetration than the 10 year optimum solution. On top of this, as time passes the mean annual cost becomes lower for both islands, hence the corresponding 20 year mean annual value is 1300C and 2200C respectively. Note that the optimum solution is moving towards lower diesel-oil contribution values, underlining the competitive advantage of power stations based on renewable energy sources, provided that a life-cycle cost analysis is considered. Interesting conclusions may also be drawn by analysing the 10 year minimum cost distribution (see Fig. 4.20). This figure shows that for low diesel-oil penetration the main cost contribution is due to the high battery cost (including the variable M&O cost battery replacement) and the fixed


Feasibility assessment for stand-alone and HEW systems Wind turbine cost Battery cost M&O cost Variable M&O cost

40 000

Total cost analysis (E)

35 000

141

Electronic equipment cost Installation cost Fuel cost

30 000 25 000 20 000 15 000 10 000 5000 0

0

25

75 100 150 200 250 500 1000 2000 50 Annual diesel-oil consumption (kg/year)

4.20 Total 10 year cost analysis of a typical wind–diesel hybrid system, high wind potential case.

M&O cost. The wind turbine purchase cost contribution cannot be disregarded, as it represents approximately 15% of the total system cost. However, for high diesel-oil penetration, the diesel-oil purchase cost represents over 50% of the entire system cost. On top of this, for optimum system configurations the diesel-oil and the battery bank correspond to 40% and 35% of the total system cost in Andros. Recapitulating, one may state that in this area of high wind potential, a wind–diesel hybrid system presents a competitive advantage in comparison with a diesel-only or a wind-based stand-alone system. The optimum system configuration is based on 2–2.5 kW wind turbines and 1000–1700 A h battery capacity. These solutions save over 75% of the fuel required by a diesel-only system annually in order to obtain full energy autonomy of the installation, while the corresponding 10 or 20 year total operational cost ranges between 60% and 50% of the diesel-only solution respectively.

4.8.6 Electricity generation cost of a HEW stand-alone system The last case study examined concerns the electricity generation cost of a typical stand-alone HEW system. The analysis above (Section 4.6) is applied (Kaldellis and Kavadias, 2007) to typical remote consumers located in Andros. In this context Fig. 4.21 demonstrates the energy production cost of the examined stand-alone system (rated power up to 15 kW) for a 10 year service period of the installation. According to the results obtained, for each Mf value there is a minimum production cost point, which corresponds to a specific system configuration (No, Qmax) that guarantees the


142


3.0


Electricity cost (E/kW h)

2.5 2.0 1.5 1.0 0.5 0 0

2000

4000

6000

8000

10 000

12 000

14 000


4.21 Ten year electricity production cost of a wind–diesel hybrid system, Andros.

remote consumer energy autonomy at a minimum electricity production cost. Also, one may observe that by increasing the contribution of diesel-oil, a remarkable cost decrease is initially encountered. However, after the 500 kg/year value, the corresponding electricity production cost starts to increase, designating the existence of an optimum configuration. Figure 4.21 also shows the diesel-only and the wind-power (Mf = 0) stand-alone systems, both presenting a higher operational cost. Finally, the optimum stand-alone system electricity production cost is below 0.5C/kW h, a value directly comparable with the operation of bigger diesel-only autonomous power stations in several Greek islands (Kaldellis and Zafirakis, 2007). The situation is slightly improved for a 20 year operation (Fig. 4.22), since even the wind-power stand-alone solution (Mf = 0) is more financially attractive than the diesel-only installation. In Figs 4.23 and 4.24 one may find the calculated (Kaldellis and Kavadias, 2007) electricity production cost variation as a function of the wind turbine rated power, for 5, 10, 15 and 20 years of the hybrid system operation and for a low (Mf = 100 kg/year) and a high (Mf = 500 kg/year) annual diesel oil contribution. In both cases one may observe that there is a remarkable electricity cost decrease with the increase of the installation service period, especially in cases of high fossil fuel participation. Accordingly, in Fig. 4.25 the minimum electricity production cost distribution versus the annual oil quantity consumed for various operational periods of the installation is demonstrated. After a thorough investigation of Fig. 4.25 one may state the following:



Electricity cost (?/kW h)

2.5

143


2.0

1.5

1.0

0.5

0 0

2000

4000

6000

8000

10 000

12 000

14 000


4.22 Twenty year electricity production cost of a wind-diesel hybrid system, Andros. Hybrid station electricity production cost variation vs system service period (low oil contribution, Mf = 100 kg/year) 5.0 5 year operation 10 year operation 15 year operation 20 year operation

4.5 Energy cost (?/kW h)

4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0 0

2000

4000

6000

8000

10 000

12 000

14 000

16 000


4.23 Electricity production cost values for variable hybrid system service periods.

•

•

For zero (wind only) or low diesel-oil contribution cases there is a considerable cost decrease between 5 and 10 years and between 15 and 20 years of system operation. The cost decrease between 10 and 15 years is quite small, owing to the increase of the variable M&O cost contribution, e.g. replacement of the necessary major components of the installation.


144

Stand-alone and hybrid wind energy systems Hybrid station electricity production cost variation vs system service period (high oil contribution, Mf = 500 kg/year)

1.8 1.6 Energy cost (?/kW h)

1.4 1.2 1.0 0.8 0.6 5 year operation 10 year operation 15 year operation 20 year operation

0.4 0.2 0 0

2000

4000

6000

8000

10 000

12 000

14 000

16 000


4.24 Electricity production cost values for variable hybrid system service periods. Electricity production cost vs annual diesel-oil consumption for 5, 10, 15 & 20 years operation (Skiros island)


1.8

10 year operation 15 year operation 5 year operation 20 year operation

1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0 0

200

400

600

800

1000

1200

1400

1600

1800

2000

Annual fuel consumption (kg/year)

4.25 Life-cycle hybrid system minimum electricity production cost vs annual diesel-oil consumption.

•

•

The minimum electricity production cost is remarkably decreased between the fifth and the tenth year of operation of the system, being accordingly almost constant up to the twentieth year of operation. There is a significant optimum annual oil consumption decrease (approx. 300 kg/year) when the desired service period of the hybrid station



•

145

increases from 5 to 20 years, leading also to remarkable environmental benefits. In all cases examined, the optimum life-cycle electricity production cost of the wind–diesel system investigated is quite lower than the corresponding value of the small thermal power stations already operating in several tiny Greek islands.

Recapitulating, the proposed wind–diesel–battery (HEW) stand-alone system is a financially viable electricity generation solution that can meet the electricity needs of remote consumers located throughout the Aegean Sea.

4.9

Sensitivity analysis of the financial behaviour of stand-alone hybrid electricity generation windbased (HEW) systems

In the following some representative results concerning the financial behaviour of wind-based stand-alone hybrid systems are demonstrated. For this purpose detailed sensitivity analysis results are taken into consideration.

4.9.1 Sensitivity analysis of the financial behaviour of a wind-only stand-alone system Impact of wind potential In an attempt to investigate the impact of the wind potential on the optimum (minimum 10 year total cost) solutions obtained, two additional cases are analysed on top of the Kithnos island one (Fig. 4.9), using 3 years’ detailed wind speed and meteorological data. More specifically, for the Andros island case (high wind potential, annual average wind speed approximately 9.5 m/s), the corresponding no-load rejection curve is given in Fig. 4.26 along with the corresponding 10 year total cost curves. The minimum 10 year cost energy autonomy wind power-based solution consists of a 5.5 kW wind converter and a 6700 A h battery size. Accordingly, for the island of Kea (medium quality wind potential, annual mean wind speed equal to 5.5 m/s), the minimum 10 year operational cost solution demands (Fig. 4.27), higher wind turbine rated power (19 kW) than Andros and 20 000 A h battery capacity. Recapitulating, according to the results obtained, one may state that both the wind turbine rated power (and cost) and the battery purchase and replacement costs become much greater as the mean average wind speed value of the HEW system location decreases.


146

Stand-alone and hybrid wind energy systems Andros 10 year energy production cost (i = 9%, g = 4%, rb = 0)

16 000

80 000 E 90 000 E 100 000 E Energy autonomy points

14 000

Qmax (A h)

12 000 10 000 8000 6000 4000 2000 0 3

5

7

9 No (kW)

11

13

15

4.26 No-load rejection configuration on the basis of minimum 10 year cost, Andros.

Kea 10 year energy production cost (i = 9%, g = 4%, rb = 0) 90 000 200 000 E 250 000 E 300 000 E Energy autonomy points

80 000 70 000 Qmax (A h)

60 000 50 000 40 000 30 000 20 000 10 000 0 4

6

8

10

12

14

16

18

20

No (kW)

4.27 No-load rejection configuration on the basis of minimum 10 year cost, Kea.

Impact of subsidy The Greek state and the European Commission support clean energy production applications with 20–60% grants, based on the exploitation of available renewable energy resources. This subsidy is given as a percentage of the first installation cost, since all the renewable energy applications are



147

Kithnos 10 year energy production cost (i = 9%, g = 4%, rb = 0, y = 0) 60 000 200 000 E 250 000 E 265 000 E 300 000 E Energy autonomy points

Qmax (A h)

50 000 40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.28 No-load rejection configuration on the basis of minimum 10 year cost, zero initial cost subsidization.

characterized as capital intense ones. Any financial measures taken in favour of renewables only partly quantify (Kaldellis, 1993b, 1997) the remarkable socio-environmental benefits resulting from the substitution of fossil fuels by renewable energy-based power stations. In an attempt to estimate the impact of state subsidization on the minimum 10 year cost solution obtained for Kithnos (Fig. 4.9), the no-subsidization calculation results concerning the 10 year operation of the proposed stand-alone wind power configuration are given in Fig. 4.28. From this figure, it is apparent that cancellation of the subsidization schemes leads to slightly larger wind turbines (∼15 kW) and smaller batteries (14 000 A h), while the corresponding minimum C10 solution approaches 179 000C in present values (Table 4.4). Impact of the improvement of battery technology The battery is one of the most important parts (Drouilhet et al., 1995; Kavadias and Kaldellis, 2000) of an energy autonomous wind power system, (a) storing the energy surplus during windy days for use during high consumption and low wind speed periods, and (b) maintaining the voltage in the system. Besides, the batteries used represent a remarkable percentage rb (up to 80%) of the complete system’s initial cost, hence any technological improvement concerning this sector will significantly ameliorate the economic behaviour of the entire system. In this context, by introducing a 10% annual improvement (ρb = 0.1) of commercial lead–acid batteries


148

Stand-alone and hybrid wind energy systems Kithnos 10 year energy production cost ( i = 9%, g = 4%, rb = 0) 60 000 100 000 E 150 000 E 170 000 E 200 000 E Energy autonomy points

Qmax (A h)

50 000 40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

No (kW)

4.29 No-load rejection configuration on the basis of minimum 10 year cost, battery technology improvement incorporated.

operational characteristics, the medium-term optimum solution is realized (Fig. 4.29), using 12–13 kW wind turbines and approximately 16 000 A h batteries capacity. The corresponding 10 year operational cost is now 114 850C at present value (Table 4.4). Impact of the local economy One of the most important parameters describing the local market situation is the corresponding annual inflation rate. More precisely, the inflation rate expresses the tendency of everyday life cost to increase and it is quantitatively approximated by the average rise in price levels. Also, the value of the inflation rate greatly influences the corresponding capital cost index, since usually the capital cost is the sum of the inflation premium, the pure time-preference and the risk premium (Myddelton, 1995; Kaldellis, 2000). Thus, by using the values experienced within the local economy during the previous decade (1990–99), i.e. i = 18%, g = 10%, the calculation results are summarized in Fig. 4.30. Here, it is obvious that the optimum (10 year minimum cost) solution tends to higher wind turbine rated power (N *o → 17 kW) and lower battery capacity (Q* max → 13 000 A h) values in comparison with the results of Fig. 4.9. However, although the 10 year total cost of the system seems much higher (400 000C) than the present economy situation (200 000C), there is no substantial differentiation in constant values (154 000C versus 135 000C; Table 4.4).



149

Kithnos 10 year energy production cost (i = 18%, g = 10%, rb = 0) 60 000 300 000 E 350 000 E 400 000 E 450 000 E Energy autonomy points

Qmax (A h)

50 000 40 000 30 000 20 000 10 000 0 3

5

7

9

11

13

15

17

No (kW)

4.30 Zero-load configuration on the basis of minimum 10 year cost, local market capital cost impact. Table 4.5 Central values of the main parameters used in the presented sensitivity analysis Parameter Annual mean wind speed (Andros) Return on investment Diesel-oil current price Diesel-oil price annual escalation rate Initial investment cost Fixed M&O cost coefficient Local market annual inflation rate Electricity price annual escalation rate

Symbol _ V i co e IC0 m g p

Numerical value

Units

9.5 8 1.5 6 Equation (4.7) 2 2 3

m/s % C/kg % C % % %

4.9.2 Sensitivity analysis of the financial behaviour of a HEW stand-alone system In the last case study the impact of the key parameters on the electricity production cost of a HEW stand-alone system is examined as a function of the annual diesel-oil consumption. For this purpose, the central values of the problem governing parameters are included in Table 4.5. Impact of wind potential As stated above, several representative types of wind potential have been investigated by Kaldellis and Kavadias (2007); see also Fig. 4.31. According to the results obtained, the wind potential impact is dominant (Fig. 4.32),


150


16 Andros Island

Naxos Island

Skiros Island

Kea Island

Wind speed (m/s)

14 12 10 8 6 4 2

er N ov em be r D ec em be r

r

ct ob

O

pt em

be

us t Se

Au g

Ju ly

e Ju n

M ay

Ap ril

Ja nu ar y Fe br ua ry M ar ch

0

Month

4.31 Monthly average wind speed values.

10 year cost analysis (wind potential impact) 3.0 Andros Island Naxos Island Skiros Island Kea Island

Electricity cost (E/kW h)

2.5 2.0 1.5 1.0 0.5 0 0

200

400

600

800

1000

1200

1400

1600

1800

2000

Fuel mass flow (kg/year)

4.32 Wind potential impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

since the corresponding electricity production cost remarkably decreases as the wind potential improves. This difference is increasing for low dieseloil penetration, while above the value of Mf = 1000 kg/year all distributions are convergent towards Kea (the lowest wind potential case) curve. It is important to note the significant difference between Andros and Kea, since



151

– in anticipation of Mf = 600 kg/year annual diesel oil consumption – the electricity production cost in Kea is more than double the corresponding value of Andros. Finally, one may easily conclude that as the available wind potential becomes more intense, the minimum electricity production costpoint is moving towards a lower diesel-oil contribution. Return on investment index Generally speaking, the return on investment depends on the local market economic wealth and more precisely on the existing investment opportunities, timing of repayment, risk of the investment and any government subsidies (Kaldellis, 2000). In addition, its numerical value varies with the inflation rate of the economy, in order to obtain a positive inflation-free return on investment index. According to the data of Fig. 4.33 – concerning the electricity production cost of a wind-diesel system situated in Andros; see also Table 4.5 – the return on investment index is directly proportional to the electricity generation cost value. Besides, the return on investment index has a greater influence on low diesel-oil penetration cases, owing to the bigger initial capital invested. On the other hand, for diesel-only installations the corresponding impact is minimized. Finally, for every 1% increase of the return on investment index, the corresponding electricity production cost increase is approximately 0.006C/kW h, and for diesel-oil annual consumptions below 600 kg/year.

10 year cost analysis

1.1

Return on investment: i = 4% Return on investment: i = 8%


1.0

Return on investment: i = 12% 0.9 0.8 0.7 0.6 0.5 0.4 0

200

400

600

800 1000 1200 1400 Fuel mass flow (kg/year)

1600

1800

2000

4.33 Return on investment index impact on the electricity production cost of a wind–diesel hybrid stand-alone system.


152

Stand-alone and hybrid wind energy systems 10 year cost analysis

1.2

Diesel-oil cost: co = 1.2 (€/kg) Diesel-oil cost: co = 1.5 (€/kg)

1.0 Electricity cost (€/kW h)

Diesel-oil cost: co = 1.8 (€/kg)

0.8 0.6 0.4 0.2 0 0

200

400

600

800

1000

1200

1400

1600

1800

2000


4.34 Diesel-oil current price impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

Current diesel-oil price The exact value of the current diesel-oil price takes into account not only its market price, but also its transportation and storage cost, which is quite high for stand-alone consumers located in remote islands. In this context, high prices lead to relatively higher electricity production cost values, especially in cases of significant diesel-oil contribution (Fig. 4.34). As a result, the impact is dominant on diesel-oil penetrations exceeding 1000 kg/year, while it is almost negligible for annual diesel-oil consumptions below 200 kg/year, underlining thus the fossil-fuel independence of similar standalone systems based mainly on renewable energy sources. Annual escalation rate of the diesel-oil price The term ‘diesel-oil price annual escalation rate’ is used here to describe the gradual changes of the diesel-oil price annually. As it is obvious from Fig. 4.35, regarding Andros, the electricity production cost of the standalone system investigated is strongly influenced by the corresponding annual escalation rate, in cases of considerable diesel-oil annual consumption. More precisely, the electricity production cost is increased as the diesel-oil escalation rate is amplified. Thus, for each 3% increase of e, the corresponding ce increase is almost 0.1C/kW h. Zero impact is encountered on annual diesel-oil consumption beneath 200 kg/year.



153

10 year cost analysis 1.2 Oil escalation rate: e = 3%

Electricity cost (€/kW h)

1.0

Oil escalation rate: e = 6% Oil escalation rate: e = 9%

0.8 0.6 0.4 0.2 0 0

200

400

600

800

1000

1200

1400

1600

1800

2000


4.35 Diesel-oil price annual escalation rate impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

Battery ex-works price The battery bank is one of the most important components of stand-alone systems, storing the wind energy surplus of high wind speed periods, in order to cover the energy deficit in low wind speed and high-load demand cases. As already stated, a typical lead–acid battery should be replaced every 4–7 years; an extra expense. The battery price impact becomes more important when the diesel-oil contribution to electricity generation is low (less than 500 kg/year) (Fig. 4.36). As expected, the electricity production cost gets lower as the battery price reduces. On the other hand, the battery cost impact is approaches zero as the annual diesel-oil consumption tends to the diesel-only solution. Installation turnkey cost The initial investment cost (turnkey cost) includes the ex-works price of the equipment needed (wind turbines, battery bank, electronic equipment, etc.) and the corresponding installation cost. The application of new technological achievements and the economies of scale decrease most system component prices in the international market. However, several parameters have to be taken into account, in order to foresee the future evolution of the ex-works prices in the local market. According to the results by Kaldellis and Kavadias (2007) (Fig. 4.37), the electricity production cost grows as the turnkey cost of the installation increases. This impact is higher


154

Stand-alone and hybrid wind energy systems 10 year cost analysis

1.2 Battery price base scenario (–20%)

1.0 Electricity cost (€/kW h)

Battery price base scenario Battery price base scenario (+20%)

0.8 0.6 0.4 0.2 0 0

200

400

600

800 1000 1200 1400 Fuel mass flow (kg/year)

1600

1800

2000

4.36 Battery bank purchase price impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

10 year cost analysis 1.2 Turnkey cost base scenario (+10% )

Electricity cost (€/kW h)

1.0

Turnkey cost base scenario Turnkey cost base scenario (–10% )

0.8 0.6 0.4 0.2 0 0

200

400

600

800

1000

1200

1400

1600

1800

2000


4.37 Investment turnkey price impact on the electricity production cost of a wind–diesel hybrid stand-alone system.

for medium–low diesel-oil penetration (up to 600 kg/year) while for higher diesel-oil contribution the corresponding influence is quite restrained. In this context, the electricity production cost decreases by almost 0.07C/kW h for the Andros stand-alone system for each 10% decrease in the installation turnkey cost.



4.10

155

Conclusions

An integrated cost–benefit model is developed that is able to evaluate the financial behaviour of hybrid electricity generation wind-based systems on a long-term operational schedule. The proposed cost–benefit analysis is an integrated tool for the financial evaluation of similar projects, since it suggests calculation methods for each of the system parameters. For this purpose the existing information concerning the initial investment and the maintenance and operation cost of similar small power stations are taken into account. The developed method leads to the estimation of • the pay-back period, • the financial efficiency, • the net present value (NPV) and • the internal rate of return (IRR) of any HEW installation. The main parameters to be predicted are the wind turbine rated power, the corresponding battery capacity and the annual oil consumption required in order to guarantee energy autonomy of the entire stand-alone installation. Subsequently, the reliability impact on the total cost function is investigated, while emphasis is also placed on the socioenvironmental benefits of stand-alone HEW systems. Furthermore, in order to obtain a realistic comparison between the existing fossil fuel-based solutions and the proposed HEW installations, the electricity generation cost is estimated, taking the desired service period of the complete installation into consideration. The last part of the chapter is devoted in an attempt to demonstrate the clear competitive advantages of the proposed HEW solution for selected representative case studies. More precisely, the proposed theoretical model is applied to several typical wind potential cases, possessing annual mean wind speed values between 6.0 and 10 m/s. For all cases investigated, the predicted electricity production cost compares favourably with today’s real electricity production cost data, resulting from the operation of existing autonomous diesel-only power stations. Finally, a quite extensive sensitivity analysis has been carried out, in order to demonstrate the impact of the main techno-economic parameters on the energy production cost of optimum sized HEW power stations. According to the results obtained, one should point out that there is a remarkable decrease in diesel-oil consumption as the desired service period of the hybrid station increases, in order to minimize the corresponding lifecycle electricity production cost. The possibility of remarkably reducing the energy storage requirements of wind-based stand-alone systems by adding a rational number of PV panels has been investigated. On the basis of the calculation results, utilizing long-term real field measurements, one may


156


definitely state that the introduction of a rational number of PV panels in a wind-based stand-alone system remarkably decreases the system energy storage requirements, improves the entire installation reliability, simplifies the corresponding maintenance procedure and strengthens the financial competitiveness of similar renewable energy applications. In view of the uncertain future concerning the oil prices worldwide and associated fossil fuel consumption environmental concerns, an increasing interest in hybrid power stations is being shown in many regions worldwide. Taking into account the detailed and extensive analysis carried out, HEW systems may be the most cost-effective electrification solution for numerous isolated consumers, located in regions of fairly good wind potential. On top of this, subsidy possibilities – granted for example by local authorities or via European Union funds – should greatly increase the economic attractiveness of similar environmentally friendly electricity production applications. Recapitulating, the outcomes of the present chapter are the development of a complete cost–benefit analysis of the proposed system that can be a valuable tool for the evaluation of any similar project and the result that a properly sized HEW system is a motivating prospect for the energy demand problems of numerous existing isolated consumers all around the world.

4.11

References

Albrecht, J., 2007. The future role of photovoltaics: a learning curve versus portfolio perspective. Energy Policy, 35, 2296–2304. Alsema, E.A., Nieuwlaar, E., 2000. Energy viability of photovoltaic systems. Energy Policy, 28, 999–1010. Berglund, B., Hassmen, P., Job, R.F., 1996. Sources and effects of low-frequency noise. The Journal of the Acoustical Society of America, 99, 2985–3002. Beyer, H.G., Degner, T., Gabler, H., 1995. Operational behaviour of wind diesel systems incorporating short-term storage: an analysis via simulation calculations. Solar Energy, 54, 429–439. Blanco, M.I., 2009. The economics of wind energy. Renewable and Sustainable Energy Reviews, 13, 1372–1382. Celik, A.N., 2007. Effect of different load profiles on the loss-of-load probability of stand-alone photovoltaic systems. Renewable Energy, 32, 2096–2115. Cherif, A., Jraidi, M., Dhouib, A., 2002. A battery ageing model used in stand alone PV systems. Journal of Power Sources, 112, 49–53. Clausen, P.D., Wood, D.H., 1999. Research and development issues for small wind turbines. Renewable Energy, 16, 922–927. Colle, S., Abreu, S.L., Rüther, R., 2004. Economic evaluation and optimization of hybrid diesel/photovoltaic systems integrated to utility grids. Solar Energy, 76, 295–299. Drouilhet, S., Muljadi, E., Holz, R., Gevorgian, V., 1995. Optimizing small wind turbine performance in battery charging applications. In: Windpower ’95, March 26–30, Washington, USA.



157

Elton, E.J., Gruber, M.J., 1984. Modern Portfolio Theory and Investment Analysis, 2nd Edition. Wiley, New York. European Commission, 1999. A Plan for Action in Europe. Wind Energy – the Facts. Brussels, Belgium. Fthenakis, V., 2000. End-of-life management and recycling of PV modules. Energy Policy, 28, 1051–1058. Guidati, G., Wagner, S., Parchen, R., Oerlemans, S., Van den Berg, R., Schepers, G., Braun, K., Kooi, J., 1999. Design and testing of acoustically optimized airfoils for wind turbines. In: European Wind Energy Conference and Exhibition, March 1–5, Nice, France. Haas, R., 2002. Building PV markets: customers and prices. Renewable Energy World, 5, 98–111. Hawkes, N., 1997. Influence and trends in lead/acid battery demand, lead supply and prices. Journal of Power Sources, 67, 213–218. Hoffmann, W., 2006. PV solar electricity industry: market growth and perspective. Solar Energy Materials and Solar Cells, 90, 3285–3311. Hohmeyer, O., 1988. Social Costs of Energy Consumption, 1st Edition. SpringerVerlag, Berlin. Holttinen, H., Malkki, H., Turkulainen, T., Bijsterbosch, H., Schmidt, R., 1999. Life cycle assessment of different wind turbine blade materials. In: European Wind Energy Conference and Exhibition, March 1–5, Nice, France. Hunter, R., Elliot, G., 1994. Wind–Diesel Systems – A Guide to the Technology and its Implementation, 1st Edition. Cambridge University Press, Cambridge. Jensen, T.L., 2000. Renewable Energy on Small Islands, 2nd Edition. Forum for Energy & Development, Copenhagen. Kaldellis, J.K., 1991. Cost–benefit analysis concerning the small scale wind turbines installations for the Greek socio-economic environment, including fuel escalation. In: 2nd International Conference on Environmental Science and Technology, September 2–5, Lesvos, Greece. Kaldellis, J.K., 1993a. Parametrical investigation of the interaction between turbulent wall shear layers and normal shock waves, including separation. ASME Transactions, Journal of Fluids Engineering, 115, 48–55. Kaldellis, J.K., 1993b. The impact of social costs of energy consumption on the cost–benefit analysis of wind turbine installations. In: ASME Energy Systems and Ecology ‘ENSEC’93’ Conference, July 5–9, Krakow, Poland. Kaldellis, J.K., 1997. Renewable and thermal energy plants. Comparison on the basis of environmental-social costs & benefits. In: 5th International Conference on Environmental Science and Technology, September 1–4, Lesvos, Greece. Kaldellis, J.K., 1999. Wind Energy Management, 1st Edition. Stamoulis, Athens. Kaldellis, J.K., 2000. Economic viability of wind power investments in Greece, including risk analysis. In: Wind Power for the 21st Century International Conference, September 25–27, Kassel, Germany. Kaldellis, J.K., 2002a. Optimum autonomous wind power system sizing for remote consumers, using long-term wind speed data. Journal of Applied Energy, 71, 215–233. Kaldellis, J.K., 2002b. An integrated time-depending feasibility analysis model of wind energy applications in Greece. Energy Policy Journal, 30, 267–280. Kaldellis, J.K., 2002c. Minimum stand-alone wind power system cost solution for typical Aegean Sea islands. Wind Engineering Journal, 26, 241–255.


158


Kaldellis, J.K., 2002d. Renewable energy sources and the reduction of air pollution: risk assessment in Greece. In: International Conference on the Protection and Restoration of the Environment VI, July 1–5, Skiathos Island, Greece. Kaldellis, J.K., 2003a. Feasibility evaluation of Greek State 1990–2001 wind energy program. Energy Journal, 28, 1375–1394. Kaldellis, J.K., 2003b. An integrated feasibility analysis of a stand-alone wind power system, including no-energy fulfillment cost. Wind Energy Journal, 6, 355–364. Kaldellis, J.K., 2003c. Optimum techno-economic energy-autonomous photovoltaic solution for remote consumers throughout Greece. Journal of Energy Conversion and Management, 45, 2745–2760. Kaldellis, J.K., 2004. Parametric investigation concerning dimensions of a standalone wind power system. Journal of Applied Energy, 77, 35–50. Kaldellis, J.K., 2007. The contribution of small hydro power stations to the electricity generation in Greece: technical and economic considerations. Energy Policy Journal, 35, 2187–2196. Kaldellis, J.K., Gavras, T.J., 2000. The economic viability of commercial wind plants in Greece. A complete sensitivity analysis. Energy Policy Journal, 28, 509–517. Kaldellis, J.K., Kavadias, K.A., 2001. Optimal wind-hydro solution for Aegean Sea islands electricity demand fulfillment. Journal of Applied Energy, 70, 333–354. Kaldellis, J.K., Kavadias, K.A., 2004. Evaluation of Greek wind parks visual impact: ‘The Public Attitude’. Fresenius Environmental Bulletin, 13, 413–423. Kaldellis, J.K., Kavadias, K.A., 2007. Cost–benefit analysis of remote consumers’ electrification on the basis of hybrid wind–diesel power stations. Energy Policy Journal, 35, 1525–1538. Kaldellis, J.K., Kodossakis, D., 1999. The present and the future of the Greek wind energy market. In: European Wind Energy Conference and Exhibition, March 1–5, Nice, France. Kaldellis, J.K., Kondili, E., 2006. Environment and Industrial Development, Volume II, 1st Edition. Stamoulis, Athens. Kaldellis, J.K., Tsesmelis, M., 2002. Integrated energy balance analysis of a standalone wind power system, for various typical Aegean sea regions. Wind Energy Journal, 5, 1–17. Kaldellis, J.K., Vlachos, G., 2005. Optimum sizing of an autonomous wind-diesel hybrid system for various representative wind-potential cases. Applied Energy Journal, 83, 113–132. Kaldellis, J.K., Zafirakis, D., 2007. Present situation and future prospects of electricity generation in Aegean archipelago islands. Energy Policy Journal, 35, 4623–4639. Kaldellis, J.K., Ktenidis, P., Kodossakis, D., 1989. Small size wind energy systemsfeasibility study for the Greek socio-economic environment. In: 2nd European Symposium on Soft Energy Sources and Systems at the Local Level, October 16–21, Crete, Greece. Kaldellis, J.K., Ktenidis, P., Papadopoulos, E., 1991. Future possibilities and aerodynamic limits for the design of advanced wind turbine blades. In: 3rd European Symposium on Soft Energy Sources and Systems at the Local Level, September 11–14, Chios, Greece.



159

Kaldellis, J.K., Doumouliakas, J., Michalis, K., 2000. Optimum stand-alone PV solution, including financial aspects. In: World Renewable Energy Congress VI, July 1–7, Brighton, UK. Kaldellis, J.K., Kavadias, K., Christinakis, E., 2001. Evaluation of the wind-hydro energy solution for remote islands. Journal of Energy Conversion and Management, 42, 1105–1120. Kaldellis, J.K., Kavadias, K.A., Neonakis, J.K., 2002. A probabilistic computational method for the economic evaluation of soft energy applications in course of time. In: 4th GRACM Congress on Computational Mechanics, June 27–29, Patras, Greece. Kaldellis, J.K., Kavadias, K.A., Paliatsos, A.G., 2003. Environmental impacts of wind energy applications: myth or reality? Fresenius Environmental Bulletin, 12, 326–337. Kaldellis, J.K., Koronakis, P., Kavadias, K., 2004. Energy balance analysis of a stand-alone photovoltaic system, including variable system reliability impact. Renewable Energy Journal, 29, 1161–1180. Kaldellis, J.K., Kondili, E., Kavadias, K.A., 2005a. Energy and clean water coproduction in remote islands to face the intermittent character of wind energy. International Journal of Global Energy Issues, 25, 298–312. Kaldellis, J.K., Vlachou, D.S., Korbakis, G., 2005b. Techno-economic evaluation of small hydro power plants in Greece: a complete sensitivity analysis. Energy Policy Journal, 33, 1969–1985. Kaldellis, J.K., Kostas, P., Filios, A., 2006a. Minimization of the energy storage requirements of a stand-alone wind power installation by means of photovoltaic panels. Wind Energy International Journal, 9, 383–397. Kaldellis, J.K., Kavadias, K.A., Papantonis, D.E., Stavrakakis, G.S., 2006b. Maximizing the contribution of wind energy in the electricity demand problem of Crete island. Wind Engineering Journal, 30, 73–92. Kaldellis, J.K., Kavadias, K.A., Kaldelli, El., Kondili, E., 2006c. Analyzing the relation between noise-visual impact and the public attitude towards wind energy applications in Greece. In: International Conference of Protection and Restoration of the Environment, July 3–7, Chania, Crete, Greece. Kaldellis, J.K., Kondili, E., Filios, A., 2006d. Sizing a hybrid wind-diesel stand-alone system on the basis of minimum long-term electricity production cost. Applied Energy Journal, 83, 1384–1403. Kaldellis, J.K., Kavadias, K.A., Koronakis, P.S., 2007. Comparing wind and photovoltaic stand-alone power systems used for the electrification of remote consumers. Journal of Renewable and Sustainable Energy Reviews, 11, 57– 77. Kaldellis, J.K., Kondili, E.M., Paliatsos, A.G., 2008. The contribution of renewable energy sources on reducing the air pollution of Greek electricity generation sector. Fresenius Environmental Bulletin, 17, 1584–1593. Kaldellis, J.K., Zafirakis, D., Kavadias, K., 2009a. Techno-economic comparison of energy storage systems for island autonomous electrical networks. Journal of Renewable and Sustainable Energy Reviews, 13, 378–392. Kaldellis, J.K., Zafirakis, D., Kaldelli, El., Kavadias, K., 2009b. Cost benefit analysis of a photovoltaic-energy storage electrification solution for remote islands. Renewable Energy Journal, 34, 1299–1311.


160


Kaldellis, J.K., Simotas, M., Zafirakis, D., Kondili, E., 2009c. Optimum autonomous photovoltaic solution for the Greek islands on the basis of energy pay-back analysis. Journal of Cleaner Production, 17, 1311–1323. Kaldellis, J.K., Zafirakis, D., Kondili, E., 2009d. Optimum autonomous stand-alone photovoltaic system design on the basis of energy pay-back analysis. Energy Journal, 34, 1187–1198. Kavadias, K.A., Kaldellis, J.K., 2000. Storage system evaluation for wind power installations. In: Wind Power for the 21st Century International Conference, September 25–27, Kassel, Germany. Kavadias, K.A., Neonakis, J., Kaldellis, J.K., 2000. Economic viability of wind farm investments in Greece, using probabilistic analysis. In: Wind Power for the 21st Century International Conference, September 25–27, Kassel, Germany. Kodossakis, D., Kaldellis, J.K., 1997. A complete time-depending economical viability analysis of wind turbine installations. In: FLOWERS’97 World Energy Conference, July 30–August 1, Firenze, Italy. Konstantinidis, P., Skordilis, A., Kaldellis, J.K., 2001. Recycling of electric and electronic waste in Greece: possibilities and prospects. In: 7th International Conference on Environmental Science and Technology, September 3–6, Syros, Greece. Liu, Y., Ye, L., 2003. Economic performance evaluation method for hydroelectric generating units. Energy Conversion & Management, 44, 797–808. Masakazu, I., Masayuki, S., Shoichi, Y., 2003. Present status and future prospects of small-scale hydropower plants. Fuji Electric Journal, 76, 581–585. Masini, A., Frankl, P., 2002. Forecasting the diffusion of photovoltaic systems in southern Europe. A learning curve approach. Technological Forecasting and Social Change Journal, 70, 39–65. Miles, R.W., Hynes, K.M., Forbes, I., 2005. Photovoltaic solar cells: an overview of state-of-the-art cell development and environmental issues. Progress in Crystal Growth and Characterization of Materials, 51, 1–42. Mondol, J.D., Yohanis, Y.G., Norton, B., 2009. Optimising the economic viability of grid-connected photovoltaic systems. Applied Energy, 86, 985–999. Morrow, H., 2001. Environmental and human health impact assessments of battery systems. Industrial Chemistry Library, 10, 1–34. Moskowitz, P.D., Fthenakis, V.M., 1991. Environmental, health and safety issues associated with the manufacture and use of II–VI photovoltaic devices. Solar Cells, 30, 89–99. Muselli, M., Notton, G., Louche, A., 1999. Design of hybrid-photovoltaic power generator, with optimization of energy management. Solar Energy, 65, 143–157. Myddelton, D., 1995. The Essence of Financial Management. 1st Edition, PrenticeHall, Englewood Cliffs, NJ. Patterson, M.H., Turner, A.K., Sadeghi, M., Marshall, R.J., 1994. Health, safety and environmental aspects of the use of cadmium compounds in thin film PV modules. Solar Energy Materials and Solar Cells, 35, 305–310. Persson, W.K., Ohrstrom, E., 2002. Psycho-acoustic characters of relevance for annoyance of wind turbine noise. Journal of Sound and Vibration, 250, 65–73. Raugei, M., Bargigli, S., Ulgiati, S., 2007. Life cycle assessment and energy pay-back time of advanced photovoltaic modules: CdTe and CIS compared to poly-Si. Energy, 32, 1310–1318.



161

Refocus, 2002. Small wind turbines: the unsung heroes of the wind industry. Refocus, 3, 30. Rever, B., 2001. Grid-tied markets for photovoltaic – a new source emerges. Renewable Energy World Journal, 4, 75–87. Rothwell, G.S., 1997. Continued operation or closure: the net present value of nuclear power plants. The Electricity Journal, 10, 41–48. Rudnik, E., Nikiel, M., 2007. Hydrometallurgical recovery of cadmium and nickel from spent Ni-Cd batteries. Hydrometallurgy, 89, 61–71. Rydh, C.J., Sandén, B.A., 2005. Energy analysis of batteries in photovoltaic systems. Part I: Performance and energy requirements. Energy Conversion and Management, 46, 1957–1979. Spyropoulos, G.C., Chalvatzis, K.J., Paliatsos, A.G., Kaldellis, J.K., 2005. Sulphur dioxide emissions due to electricity generation in the Aegean islands: real threat or overestimated danger? In: 9th International Conference on Environmental Science and Technology, September 1–3, Rhodes, Greece. Tachos, N.S., Filios, A.E., Margaris, D.P., Kaldellis, J.K., 2009. A computational aerodynamics simulation of the NREL Phase II rotor. Open Mechanical Engineering Journal, 3, 9–16. Talavera, D.L., Nofuentes, G., Aguilera, J., 2010. The internal rate of return of photovoltaic grid-connected systems: a comprehensive sensitivity analysis. Renewable Energy, 35, 101–111. Tsoutsos, T., Frantzeskaki, N., Gekas, V., 2005. Environmental impact assessment of solar energy systems. Energy Policy, 33, 289–296. Zafirakis, D., Kaldellis, J.K., 2009. Economic evaluation of the dual mode CAES solution for increased wind energy contribution in autonomous island networks. Energy Policy, 37(5), 1958–1969.


5 Stand-alone wind energy systems D. WOOD, University of Newcastle, Australia and P. FREERE, Monash University, Australia

Abstract: This chapter describes small wind turbines and their use in stand-alone power systems with conventional battery storage. Turbine components, such as the generators, blades and towers are reviewed to highlight their influence on turbine performance and safety in standalone operation. A major issue in designing these systems is the proper characterisation of the wind resource, which is complicated by the need to consider the time dependence of the electrical loads to accurately determine the required battery size. Small turbines, less than about 50 kW in rated power, are used for a range of stand-alone applications from small systems for village electrification in developing countries, to larger systems for remote power in western countries. The type and power requirement of the load can have a significant impact on the design of the system and the choice of inverter. Key words: stand-alone power, remote power, wind turbines, battery, system sizing.

5.1

Introduction

The subject of this chapter is stand-alone power systems containing wind generators without photovoltaic (PV) arrays. It is necessary, however, when designing a remote power system to know enough about the relative merits and disadvantages of each renewable technology to make an intelligent choice of which to use. We give some typical comparisons between the two. Most wind-only systems are small and so we describe in considerable detail the technology of small wind turbines. This is done mainly by comparison with large turbines, on the grounds that the reader is likely to be more familiar with that technology, and, if not, it is easier to find information on large turbines than small ones. The International Electrotechnical Commission safety standard for small wind turbines, IEC (2006), defines a small wind turbine as having a rotor area of less than 200 m2, which corresponds to a rated power of 50 kW or less. This is approximately the range we will consider. The turbine’s generator and control system have a large influence on the overall safety and functionality of the complete system. With current microprocessor technology it is possible to integrate turbine control with system 165 © Woodhead Publishing Limited, 2010

166


governance, a combination that was not done even in the 1990s. After discussing the control and electronics we consider the design of remote power systems, focusing on the assessment of the power produced and the estimation of the loads to be powered.

5.2

Stand-alone wind energy systems

Wind speed and direction changes are usually too great for a wind turbine to generate power and supply it directly to a load. The only exceptions are turbines supplying a load requiring only an average amount of power over, say, several days, such as some water pumping applications. In all other circumstances, energy storage or another energy source is required in combination with the wind turbine. In a stand-alone system, the most common energy storage is batteries, but flywheels (Fig. 5.1) and thermal storage are possible. Energy storage using batteries is expensive but can be reduced by adding in other energy sources such as PV systems or diesel generators. Systems with diesel generators do not need to use energy storage at all and the wind turbine reduces the diesel fuel use. Many parts of the world have neither grid power nor secure access to conventional fuels. Renewable energy systems of wind turbines and/or PV

5.1 PowerStore Flywheel System in a wind diesel system at Ross Island, the Antarctic [http://www.pcorp.com.au/index.php?option=com_ content&task=view&id=161&Itemid=198, viewed 21 April 2009].



167

5.2 A small wind–PV system. MG4520 200 W wind turbine with 40 W of PV panels. The vibration of the tower caused a resonance in the PV panel frame, and so they were moved from the wind turbine tower.

systems may then be the preferred choice for remote power. We now briefly compare the two technologies. For stand-alone systems, the choice of wind turbines, PV, or a combination (Fig. 5.2) relates directly to the cost of producing the energy and storing it when there is no wind or sunlight. This simple rule can have different outcomes depending on the climate. For example, where the wind and solar resource are reasonably uniform over the year, such as much of the Australian east coast, then the choice of energy source may well be based only on cost. On the other hand, areas such as West Bengal and parts of Bangladesh have calm, sunny days in ‘winter’ and windy, cloudy days during the monsoon and therefore require hybrid systems. A detailed cost comparison of wind-only and PV-only power systems for the Greek islands, where both resources are abundant, has been given in Kaldellis et al. (2007). The other major consideration is the environment where the energy is to be produced. It may not be practicable to have rotating blades in some built-up areas due to noise or proximity to buildings or people, or rare birds.


168


Reliability may be another consideration – anything that rotates will always need more maintenance than stationary generators such as PV. PV, however, does need maintenance – cleaning is an obvious one. Keeping plants and trees from growing too high and shading the panels is another. Water ingress can also be a problem. Wind turbines will suffer wear of the moving parts and fatigue of cyclically stressed parts (e.g. blades), but PV panels will suffer erosion of the glass from dust and sand, and may suffer from excessive wind loading due to their large surface area. PV panels and turbine towers with guy-wires are susceptible to vandalism. If incremental expansion of the system is a future possibility, then PV must be considered seriously, as it can be incrementally enlarged by adding a few more panels and batteries and possibly another inverter to the grid. To increase the output of a wind turbine, usually requires adding another wind turbine giving a large increase in power production at a high price. The total generating capacity of small wind turbines sold in the USA in 2007 was 9.7 MW for US$42M (AWEA, 2008). This represents an average cost of US$4.33 per watt. The cost varies between US$3 and US$5 per watt and the cost per kW h of production is between US$0.10 and US$0.15. Routine maintenance is quoted as US$0.01 to US$0.05 per kW h. Table 5.1 compares typical costs of small wind and PV electricity. However, we warn the reader that comparative costing is site specific. It is very sensitive to the energy source quality and the ease of installation. If the site is sufficiently windy, then it is cheaper to install a wind turbine than PV panels, but if needed, PV panels can usually be added quite simply.

5.2.1 Pre-feasibility analysis To determine the basic suitability of a stand-alone wind power system, it is necessary to have estimates of the electrical load and the power supply. The existing or future load in kW h per day can be estimated by summing the Table 5.1 Comparative costs of small wind turbine and PV electricity, taken from AWEA (2008) Small wind

Solar PV

Residential (on- or off-grid 2 kW system) US$ per W of capacity US$ per kW h of production (cost of energy)

$3–5 $0.10–0.15

$9 $0.40

Commercial scale (on-grid 50 kW system) US$ per W of capacity US$ per kW h of production (cost of energy)

$3–5 $0.10–10.15

$6.80 $0.27



169

power requirements in kW multiplied by the number of hours per day that each appliance is to be used. It is then necessary to estimate the kW h per day that will be produced by the turbine. The site for the wind turbine of a stand-alone power system is largely determined by the location of the users but there may be flexibility in choosing the specific site. Wind sites that are many kilometres from the user are not useful owing to the extra losses on the long electrical power transmission wires or costs incurred in using step-up (at the turbine) and step-down transformers (at the load). The transformers would allow transmission at a higher voltage, thereby reducing the electrical current and hence the transmission losses. Particularly good sites are often characterised by vegetation leaning away from the prevailing wind direction. Local pastimes such as kite flying or sailing, also give a good indication of windiness – as do walled gardens to protect the garden from the wind. However, anecdotal evidence of windiness can often be skewed by the memory of major wind storms, rather than indicating a general windiness. It is advised that wind measurement be undertaken if no wind speed data are available, or a small wind turbine be installed to ascertain whether it is windy enough to be worth considering further. For many small systems and in developing countries in particular, there may be very limited information on available wind speeds.

5.2.2 Modelling and simulation of stand-alone wind energy systems Manufacturers of larger wind turbines often supply a power versus wind speed curve for the turbine. Some small wind turbine manufacturers supply only the wind speed at which the turbine delivers rated power, in which case it is necessary to use an approximate curve from a similar turbine. Ten minute averages of the wind speed are combined with the wind turbine power curve to determine the energy output of the system over the period covering the averages. A spreadsheet program can be written to perform this. If a Weibull curve for the probability distribution of the 10 min wind speed in the area is applicable, or can be assumed, then it can be used along with the average wind speed to determine the energy output in kW h per day for the period. The Canadian National Research Council RETscreen website (http://www.retscreen.net/) has downloadable, free software to determine the average energy production as described above, along with a large database of wind speed data from around the world, and the power curves for a number of small wind turbines. These issues are considered in more detail in Section 5.6.


170


5.3

Small wind turbine technology

In this section we describe the basic components of small wind turbines mainly by contrast to those of the better known large turbines. However, there are aspects of turbine operation that are common to turbines of all sizes. The main operating parameter is the tip speed ratio, TSR, defined as the circumferential velocity of the blade tips divided by the wind speed. The TSR controls the blade aerodynamics, in particular the angle of attack of the airflow over the blades. This, in turn, sets the lift : drag ratio and therefore the power output (Burton et al., 2001). Most turbines operate with a TSR between 5 and 10, with the lower values typical of three or more blades and the higher values of two blades. Generally it is preferable to operate at constant TSR as wind speed varies, which is the approximate behaviour of the turbine data in Fig. 5.3, and, ideally, that TSR gives optimum power extraction efficiency. It can be a challenging control problem to maintain the optimum TSR as the wind speed varies in the absence of an anemometer – these are usually too expensive for small turbines. Part of a typical power curve (power output versus wind speed) is shown in Fig. 5.3. The major differences between large and small turbines usually occur near the ‘cut-in’ wind speed, the lowest at which power is produced and at the top end where small turbines tend to have a lower ‘rated’ wind speed and the differences in safety mechanisms become important.

5.3.1 The generator The ideal wind turbine generator would start producing power as soon as the wind blew, and its output power would rise with the increase in power as the wind increases. If the output of the generator were shorted, the 6

400 TSR

5

300 4

250 200 150

3 Output power

TSR

Output power (W)

350

2

100 1

50 0

0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Wind speed (m/s)

5.3 Measured wind turbine power curve of a MG4520 200 W wind turbine in a wind tunnel.



171

generator would always slow the turbine to a halt without harming the generator. Finally, the ideal generator must be controllable so that it provides ‘maximum power point tracking’, MPPT, allowing the maximum energy to be extracted. Wind and generator speed In practice, a generator will always have a minimum speed before it produces enough voltage to feed power into a load, such as a battery or an electrical grid. Trying to reduce the speed at which the generator can operate rapidly becomes worthless, as at low wind speeds, there is so little energy anyway (Fig. 5.3). As the generator increases speed, its output voltage will increase and it will feed extra power into the load. However, rarely will it automatically match the optimum power/speed characteristics of the rotor so as to extract the maximum amount of energy from the blades and wind. Generally, for stability reasons, a wind turbine and its generator will be designed so that its speed is higher than the optimum speed, thereby reducing the chance of stalling. Types of generators Most small stand-alone wind turbines (typically under 5 kW) use permanent magnet generators, PMGs Figs 5.4 and 5.5. They have the advantage of not requiring external excitation. PMGs are the simplest to use as they need only a rectifier to produce a direct current (DC) voltage for a battery, but magnets are brittle and many are temperature sensitive. Synchronous generators (like car alternators) are similar to PMGs, but need a field current to produce the magnetic field, so are intrinsically less efficient. Furthermore, at low wind speeds, the energy from the wind may not be enough to provide the field winding current. Nevertheless, it is

5.4 Simple permanent magnet generator (PMG). Left: rotor. Right: one of three stator windings.


172


5.5 Ironless stator between permanent magnet rotors from a Rutland 910 turbine.

possible to get satisfactory performance from these generators if they are connected through a rectifier to batteries. However, if they are connected to an AC load with a constant frequency, the speed of the turbine must be held constant and the efficiency is compromised unless a rectifier and inverter are added. Both squirrel cage and doubly excited induction generators are AC generators. They can, with a suitable controller, charge batteries. Self-excitation reduces their efficiency, but the squirrel cage induction generator in particular is very robust. However, the output power of both generators is usually very sensitive to both the generator speed and the load level. Hence they require a sophisticated controller to keep the voltage and power output within useful bounds. Squirrel cage induction machines have neither brushes nor cogging torque, but often require a gearbox as they often rotate too rapidly to match the optimum TSR of the rotor. Three phase generators are usually preferred to single phase generators, as the former are smaller and usually cheaper for the same power output. All the generators mentioned above produce alternating current, AC. It is possible to get DC generators, but they use a commutator and brushes and require regular maintenance of the brushes and commutator, hence they are generally not suitable for small wind turbines. Other generator types are usually too specialised and expensive for small turbines, although this may change as different technologies mature and other generator manufacturers enter the market. Friction and cogging torque Some generators require a positive torque to get them turning and there may be additional frictional torque in the drive train, particularly if a gearbox is used. For whatever reason, a turbine not turning when the wind is blowing suggests a faulty or bad design, and damages the reputation of the manufacturer. Even if the turbine is turning, it may not be generating



173

sufficient voltage to feed a battery or AC supply. However, this situation is usually less obvious to spectators. The resistive torque may be caused by: • • • •

high friction bearings (especially sealed bearings before being run in); brush friction; cogging torque, especially in permanent magnet generators; oil seals between generator and gearbox.

As noted above, PMGs are preferred for small turbines as they do not need gearboxes and have no brushes. Depending on the design, they may have significant cogging torque. Cogging torque is not inevitable, but a sophisticated magnetic design is required to minimise it. One technique to remove cogging torque is to use an iron-free rotor or stator. Often this entails generator windings embedded in resin, with no iron path for the magnetic flux through the rotor. This certainly removes the cogging torque, but reduces the magnetic flux and can lead to overheating of the windings as much of their heat dissipation is now through resin.

5.3.2 Other features of small turbines In comparison to large turbines, smaller turbines usually: • • • •

operate at higher rotational speeds for the same wind speed; have a tail fin to orient them into the wind which can lead to large gyroscopic (precessional) loads on the blades and main shaft; have smaller tower heights and experience lower average wind speeds; employ simpler and cheaper safety systems which can compromise their ability to withstand high winds. In this section we concentrate on the aerodynamic aspects of safety.

The higher rotational speed means a higher maximum frequency of blade rotation and a greater opportunity for exciting tower resonances and a larger number of blade fatigue cycles per unit time. Small blades also experience higher centrifugal loads as these depend on the product of the tip radius and the square of the rotational speed. Tail fins and yaw behaviour Small turbines typically either have a tail fin or the rotor is placed downwind of the tower and the coning of the blades provides yaw stability. It is often said that downwind rotors do not perform well in yaw but we are unaware of any detailed study of their behaviour. However, there can be noise and other problems associated with tower interactions on downwind machines. The behaviour of a ‘free yaw’ arrangement for upwind rotors is


174


approximately that of a second order linear system, e.g. Bechly et al. (2002), and this can lead to high yaw rates and large gyroscopic moments on the blade root and main shaft. These loads are proportional to the product of blade inertia, angular velocity and yaw rate, and can be the largest loads on a small turbine according to the ‘simple load model’ of IEC (2006), see Wood (2009). Particular care is needed in designing the main shaft and it is usually necessary to use a high-grade steel with a high ultimate strength. Towers Towers for small turbines vary from the self-supporting tubular to lattice towers used for large turbines to guy-wired towers. Many towers are lowered and raised using a gin pole, cable and tractor or hand winch. In order to obtain development approval for installation of a turbine, the purchaser may have to demonstrate that the turbine and tower are designed to withstand the maximum wind speed for the site. This speed is usually given in the appropriate national standard for wind loads and is influenced by the ‘importance’ of the structure, usually defined in terms of the structure’s threat to human life if it fails. The importance level for a turbine may differ between a remote and an urban site. In Australia, for example, the maximum wind speed does not vary much apart from being significantly higher in areas susceptible to cyclones (the southern hemisphere equivalent of hurricanes and typhoons). However, cyclones and such like are predictable, and an acceptable protection against them is to lower the turbine before they are due, provided the turbine and tower will survive the non-cyclonic maximum wind speed. Guyed towers are the most common, probably because of the low purchase price and the space taken by the guy-wires is often not a problem for remote installations. On the other hand, the guy-wires often must resist the overturning moment on the base of the tower and so may require large foundations, which can be costly and a transport problem for remote sites. Lattice towers are easier to transport (when unassembled) but tend to have a lower service life than pole towers. Tubular, stand-alone towers require a smaller foundation but are usually heavier than the other types and therefore more expensive to purchase and transport. Hot-dipped galvanised tubular towers usually have the longest service life, and this can be a major consideration if the turbine is located near the ocean. The tower natural frequency is important for two main reasons. The first is that many national standards require a ‘dynamic analysis’ of tower safety if the natural frequency is less than, say, 1 Hz. Secondly, if this frequency falls within the range of blade frequencies, then tower resonance is possible. Because of the large frequency range of small blade operation, it



175

is very difficult to avoid this situation. On the other hand, blade frequencies vary with wind speed so the opportunities for setting up resonance are limited. The optimum tower height for a small turbine, defined as that giving the maximum output power per unit capital cost, is typically 18–33 m depending on turbine size and wind regime (Wood, 2001). Part of the reason for these low heights is that small turbines are usually rated (for maximum power) at lower wind speeds, between 10 and 15 m/s, than large turbines and so the increase in wind speed as height increases causes the output power to be limited and then the turbine to be shut down for a larger proportion of the time. Wood’s (2001) analysis required many simplifications and ignored transportation and installation costs, and so probably over-estimates the optimum height, but it is clear that the best height is considerably lower for a small turbine than a large one. This, of course, lowers the average wind speed which may already be compromised by the need to site the turbine close to its load. In our experience a hub-height mean wind speed of 5 m/s is a good one for a small turbine. Low average wind speed requires good starting performance to extract the maximum possible energy. Starting and low wind speed behaviour Low wind performance is usually gauged by the cut-in wind speed, but Wright and Wood (2004) documented the well-known fact that the ‘starting’ wind speed for blades initially at rest can be much higher than the ‘stopping’ wind speed at which the blades come to rest. For their 500 W turbine with a PMG which had a cogging torque of 0.36 N m, the former was around 4.5 m/s and the later 2.5 m/s, giving a cut-in wind speed of 3.5 m/s, which appears respectable but means the turbine may not be producing power at the average wind speed of many sites! The high starting wind speed is unfortunate but understandable because small turbines rarely use blade pitch adjustment – another major difference from large turbines – which means typical angles of attack on stationary blades are very high, and the resulting aerodynamic torque is low. Two further aspects of starting are important. First, it is a slow process; the starting time scales as the inverse square of the wind speed (Wright and Wood, 2004), and secondly, the starting aerodynamic torque is determined largely by the root section of the blade, rather than the tip region where most power is produced. In combination with the poor performance of thick aerofoils at the low Reynolds numbers typical of small blades at low wind speed, this means that the appearance of a well-designed small blade near the root is very different from that of a large blade, where thick profiles are used to blend with the circular attachment section. Large blades are designed


176


primarily to maximise power extraction efficiency, but this leads to poor starting performance (Wood, 2004). Fortunately, the different origins for starting and power-producing torque allow sacrificing a small decrease in efficiency against a major improvement in starting (Wood, 2004). Starting time also depends on blade inertia, as does the gyroscopic load during yaw. Thus there are two reasons, particular to small turbines, to minimise blade inertia, but this must be balanced against the increased responsiveness to gusts which may make the turbine harder to control. Overspeed protection Small wind turbines do not usually have automatic brakes for emergency stopping. The most common safety mechanisms are turbine pitch-up or tilt-up and furling; the latter is shown in Fig. 5.6. Pitch-up requires hinging the turbine in the horizontal plane behind the turbine’s centre of mass so that the moment about the hinge of the rotor thrust at high wind speed will exceed the restoring moment due to gravity. Some damping is usually required. Pitching is compromised by the fact that the gyroscopic moment on the main shaft may either assist or resist pitching depending on the sign of the yaw rate, so it is possible to have a situation where the turbine needs to pitch for safety but is prevented from doing so by rapid yaw in the wrong direction. Furthermore, it appears that the gyroscopic moment increases more rapidly than the moment due to thrust as turbine size increases.

5.6 Wind turbine fully furled but still rotating due to a changing wind direction.



177

Pitching is, therefore, much more common on very small turbines. Furling requires an offset between the rotor axis and the yaw axis (the tower) and a collapsible tail fin. At sufficiently high wind speed, the yaw moment on the rotor exceeds that due to the fin and the rotor collapses towards the tail and yaws out of the wind. Furling often causes the rotor to have a mean yaw error during normal operation with a significant loss of power. Since the yaw moment due to the tail fin depends largely on the wind speed, it is difficult to ensure that furling protects against both high thrust at high wind speeds when the turbine is operating normally and runaway from a failure such as loss of load at moderate wind speeds. Furling can also be associated with very high gyroscopic loads if the direction of the collapse is also the direction of the turbine yaw (Wright and Wood, 2007). Stopping the turbine Either for protection of the turbine, or for maintenance of the turbine, it is necessary to be able to stop the blades rotating or to reduce their speed to an acceptably low value. The methods are a manually operated brake or furling, or lowering a hinged tower such that the blades are parallel to the ground. Most useful is a (possibly manual) brake as it allows work to be done up the tower, but in those circumstances, it is necessary that the turbine is locked in yaw, or else any wind direction change will possibly dislodge the tower-top maintenance worker.

5.4

Control and electronics

Wind turbine controllers are designed to provide appropriate electrical power to the load and to protect the turbine, and possibly also to protect the load. The main control issues are: • • • • • • • •

adequate voltage output; generator current limit; generator speed limit; power maximisation (MPPT); high wind speed protection (if this is not done aerodynamically); parking when power is not needed; temperature limits (generator, controller); lightning strikes.

It is possible that all of the above control functions are regulated by a single microprocessor. However, some secondary control is required in case the microprocessor fails. The electrical output from the generator is usually three phase AC with a variable voltage and variable frequency. This is usually rectified


178


(converted to DC) and the energy stored in a battery. The DC loads (often lamps) can be fed directly from the battery, perhaps using a charge controller. An inverter is used to convert the battery DC voltage to fixed frequency and voltage AC, as found in ordinary mains electricity connected houses (e.g. 230 V/50 Hz, 110 V/60 Hz).

5.4.1 Controller A typical wind turbine controller incorporates the rectifier which is often associated with MPPT. A controlled rectifier also ensures that there is a current limit to protect the generator. The controller may also monitor the battery condition to ensure that the battery is not overcharged and is charged appropriately at other times (including boost charging, cell equalisation charging, etc.). The controller is often used as the primary form of overspeed protection, say by limiting or reducing blade speed when the rated (maximum) power has been produced. In this case, the ‘aerodynamic’ controls of pitch-up or furling are used only when there is an electrical fault. Some small turbines rely entirely on electronic overspeed protection.

5.4.2 Inverter The inverter will endeavour to produce the correct output voltage and frequency, and (usually) also monitors the battery. If the battery charge is too low, the inverter will turn off to protect the battery. However, DC loads may not be directly monitored and the battery may suffer damage from excessive discharge. Inverters generally use pulse width modulation (PWM). The result of PWM is a sinusoidally chopped square wave which must be filtered to produce a good approximation to a pure sine wave. When powering magnetic loads that are designed to work from sinusoidal voltages, such as an electric motor, the quality of the voltage is critical. Cheap inverters produce nearly square wave output, which can cause excessive heating in magnetic loads. However, many modern items have electronic power conditioners. For example, modern washing machines use variable speed drives, and computers have power electronic power supplies. These are insensitive to the voltage waveshape and can manage with a near square voltage. However, a near square voltage is likely to cause more electromagnetic interference and hence it is to be avoided in general.

5.4.3 Generator heat issues Excessive current in the generator leads to overheating and possibly a meltdown of the insulation, followed by melting of the wires. If a short



179

circuit occurs in the windings, there will be a large current flowing, usually leading to rapid braking of the generator (and risk of damaging the blades). This may be followed by the melting of the copper wires and the end of power generation. Under these circumstances, the turbine will be unloaded and then speed up, possibly to the point of self destruction by the centrifugal forces on the blades. Since it is usually air cooled, the generator current capability is dependent on the air temperature, wind speed, thermal resistance from the wires to the air and also the heat loss in the generator. It is usual to rate the current carrying capacity of the generator at 25 °C ambient in free air and thermal characteristics can be measured as in Fig. 5.7. Typically the generator will be enclosed to protect it from the weather. Under a hot sun, the ambient temperature may well be far above 25 °C. A combination of direct sun, high ambient air temperatures and high wind speeds may require substantial derating of the generator. For example, recent weather in Melbourne, Australia combined these aspects: 46 °C ambient temperature, with direct sun (train rails exposed to the sun reached 75 °C) and wind speeds reputedly up to 180 km h. Inexpensive wind turbines with simple controllers and limited overspeed protection will be damaged unless the operator has the good sense (and is present) to turn them out of the wind or park the blades. Most wind turbine controllers will have a current limit, to protect the generator and power electronics by limiting the power output from the turbine, causing the rotor to accelerate. Usually in the design stage, the operating temperature is calculated for the design environment. An improvement is to use temperature sensors in the generator to either provide a warning or protect the generator. In low-power generators, it is possible to use polyswitches (or similar) which react to the temperature by going open circuit above a certain temperature. The difficulty is, again, that in protecting the generator, the load is removed from the turbine, which will now overspeed. Even simple protection is usually ignored for small turbines on the grounds that high power is usually produced only when there are high winds to provide sufficient cooling. Against this argument is the fact that small generators tend to be less efficient than large ones and therefore produce more heat per unit power.

5.4.4 Current limiting Most power electronic controllers limit the current by either briefly turning off the rectified DC output (as in a DC–DC converter) or turning off part of the AC waveform (i.e. phase control). For synchronous generators, the excitation can be adjusted. Then the controller can have a much smaller


180

Stand-alone and hybrid wind energy systems Generator temperature curve

Temperature (°C)

45 40

Gen. shell

35

Gen. face plate

30

Gen. shaft

25 20 0

(a)

20

40

60

80

100

120

Time (min)

(b)

5.7 (a) Measurements of the temperature rise of a 200 W, 12 V PMG with no moving air with phase current of 6.5 ± 0.2 Arms. Ambient temperature = 20.9 °C. (b) Positions of temperature measurement indicated by arrows. Note that the generator has a 3 mm thick plastic cowling and the faceplate is covered by the blade attachment and a nose cone.

power rating than if it controls the full output of the generator. However, for small turbines, the associated cost reduction may not be significant. A simple method of current control used in some micro-wind turbines, has been to sense the temperature of the generator winding, and if it is too high, to insert an extra impedance thereby reducing the current.



181

5.4.5 Generator overspeeding In the generator, the centrifugal forces from overspeeding can cause the rotor windings or magnets to fly off (depending on how the magnets have been attached to the rotor). Then these windings or magnets may jam the rotor, forcing it to stop rapidly, which may further damage the blades or gearbox. Some high-performance magnets are inflammable, and may catch fire once their protective coating is damaged. The first and foremost way of controlling the turbine speed is to keep the generator electrically loaded. Under normal circumstances, this works well until the current limit is reached. Then the electrical load must be reduced and the turbine will speed up. When aerodynamic overspeed protection does not work, another control mechanism is needed. If the controller has a sufficiently powerful microprocessor, it could detect overspeeding and operate a motor to turn the turbine out of the wind, or a brake to stop the turbine, or drastically increase the load on the generator temporarily to stall the turbine. Only the last of these can be implemented at no extra hardware cost.

5.4.6 Maximum power point tracking For nearly all turbines, the curve of turbine power coefficient (which can be interpreted as the efficiency of power generation) when plotted against the TSR has a single maximum at the ideal TSR. Thus for a given power output from the turbine there are two possible operating speeds. Usually, the turbine operates at the higher speed. However, by slowing the turbine, the generator voltage may not be enough to supply the load. Hence, it may be necessary to use a different load that is designed to operate at lower than the load voltage. To reach this new load-speed operating point, the turbine must go over the maximum power point and hence the current rating will surely be exceeded. For this brief time, the over-current protection must be turned off. MPPT algorithms have a large literature, and a wide range of different strategies has been investigated, e.g. Hong et al. (2009). MPPT generally aims to keep the TSR close to the ideal value as the wind speed changes without measuring the speed. However, the requirement is to maximise the output of the whole wind turbine, which includes the generator, whose efficiency may depend on the power output. A simple concept is to measure the output power and adjust the output power of the generator in whichever direction is required to increase the output (hill climb method). For a small turbine, in practice, the wind speed can be so variable that the output of the turbine is changing very quickly and trying to follow the changes may result in a time lag which removes any benefit. Consequently,


182


some average turbine speed that is expected to maximise the output must be aimed at.

5.4.7 Controller protection Turbine controllers for most remote power systems are located in the same building as the batteries and have similar requirements for protection. For example, they must be kept dry; we have heard significant anecdotal evidence that moisture in controller enclosures can also be a major problem. In hot conditions, the electronic components must be well shaded and ventilated. The sensitivity to the heat depends on the grade of electronic component used and their lifetime usually reduces as the temperature increases.

5.4.8 Lightning strikes The energy in lightning strike (see Fig. 5.8) is such that a direct strike cannot be totally protected against. Furthermore, IEC (2006) exempts small wind turbine blades from having lightning protection, whereas the corresponding standard for large turbines mandates the use of conducting strips in the blades, see section 10.3 of Burton et al. (2001) for more details. However, nearby strikes which induce electric currents can be mitigated by providing a path for the lightning. It has been claimed that using magnetic devices as

5.8 Lightning damage at the trailing edge near the tip of a Westwind 20 kW wind turbine blade at the CSIRO Energy Centre, Newcastle. The blade was repaired at the University of Newcastle and is back in service. Photo courtesy of Phil May, Solartec Renewables.



183

the main control element, together with spark gap arrestors, would assist to protect any control electronics, but as yet there appears to be little direct evidence, and only a little anecdotal evidence. Large turbines also have electrical connections from the conductors to an earthed tower conductor, the idea being to conduct the lightning safely to ground, with no room for a spark gap to appear, and produce heat. Similar protection can be added to small turbines but is often avoided, probably due to cost reasons, and often justified by the lower tower height; the frequency of lightning strikes is proportional to the square of the hub height plus blade radius, see for example, Rachidi et al. (2009).

5.4.9 Unforeseen conditions Control of a wind turbine is principally to maximise power output during normal operation and to protect the turbine, nearby people and property, in extreme conditions or after component failure. The desire to extract the maximum amount of energy from the turbine will be compromised by the need to maintain the turbine in working order. Hence at times, the turbine will have a reduced output in order that it may survive better and have a longer lifetime. Despite the best intentions and design, there will be circumstances where a manual decision and intervention is required to turn the turbine out of the wind, to lock the blades or to lower the tower. Even then, there may be circumstances that cannot be predicted or designed for; examples include an earthquake of an unheralded magnitude, wind speeds causing flying debris to bombard the turbine to destruction, or when an operator breaks a wrist from backlash in a faulty manual brake when trying to stop a turbine in a storm and the turbine is destroyed.

5.5

Stand-alone power systems

Stand-alone power systems are needed where there is no grid electricity (Fig. 5.9), or the grid electricity is unreliable. The basic electrical system is shown in Fig. 5.10. However, the uses of the electricity and hence the required power, may be different in the different situations. In an area that has never had grid electricity the first requirement is usually for lighting. A clear, bright light that does not produce heat is very welcome. This allows for reading, discussion, some hand work and trade at night. With the introduction of low-power LED lights it is possible for a small wind turbine to provide enough light for a moderately sized village. An example is the Practical Action wind turbine installations in Nepal (Shrestha, 2009), using turbines rated at less than 1 kW. Next to be powered are usually communication and entertainment systems – radios, televisions, etc. Further thoughts often turn to powered equipment such as a circular


184


5.9 Stand-alone wind turbine with PV in Nepal.

AC loads DC loads

+

Three phase Rectifier/ generator controller output

Battery

Inverter

5.10 Basic electrical system for a stand-alone, battery wind power system.

saw or electric rice cooker or similar. In tropical areas or for remote medical clinics, a refrigerator is highly desirable. In western countries, turbines smaller than 1 kW are usually seen on yachts. Larger stand-alone power systems often provide power for houses that are so far from the grid that the cost of grid connection is prohibitive; in Australia, 1 km of power lines costs around A$15 000, but this applies only to level ground and does not include the cost of transformers if necessary. A well-designed energy-efficient house that does not use electricity for cooking or heating will typically require 10 kW h per day or less. This could be supplied by a 2 kW wind turbine at a good site. A fascinating study



185

of the attitudes of remote power system users, with some good lessons for equipment suppliers, and system designers is given by McHenry (2009). Bigger stand-alone systems for hotels and tourist resorts are described by Dalton et al. (2009). In areas that have an unreliable electricity grid, maintenance of lifestyle tends to be the priority, leading to the ‘need’ to operate a refrigerator, washing machine or plasma TV. Then the addition of electric lighting is a small increment in load and would usually be added as a matter of course. For private households and other low-level electricity users, battery energy storage may be adequate – it is quiet and the maintenance level is low (but necessary). A wind or PV system to charge the battery may last for many years before it needs replacing. The only likely issue is that as people become accustomed to electrical energy, they would like more. Hence the availability of an upgrade path should be considered from the beginning. Industry requires larger amounts of power especially to start machines. Induction motors can take up to seven times their rated power to start, and hence for an industry, a diesel generator may be the cheapest way of providing the power, especially if the waste heat can also be used. With rising fuel prices and the uncertain availability of fuels in some countries, fuel saving may be critical. All forms of renewably powered stand-alone power systems are likely to increase rapidly in popularity.

5.6

Further aspects of system sizing

The stand-alone system that will be dealt with here is that of a wind turbine and lead–acid battery system. While lead–acid batteries will continue to be used for some time yet, other energy storage technologies are being actively explored (Sullivan et al., 2008), either as new types of batteries (Joerissen et al., 2004), improved charging methods, or flywheels, and fuel cells (Nelson et al., 2006). Specific examples of system sizing based on knowledge of the wind resource have been considered by Kaldellis (2004) and Roy et al. (2009) amongst others. Here we will give general guidance, because many systems, especially small ones, are installed at sites for which the wind resource has not been thoroughly characterised. The sizing of the system depends firstly on the electrical load and the demand management possibilities. The electrical load determines the amount of energy required and the demand management possibility determines whether part of the load can be moved to windier periods or be eliminated altogether. The next stage is to balance the wind generator output and the battery energy storage. Storing energy in a battery is roughly 75% efficient – Roy


186


et al. (2009) assumed charging and discharging efficiencies are both 90%, and there are additional losses in the inverter, etc. – so better use is made of the wind energy if it can be used as it is generated. By measuring the wind speed over a suitable interval, or by using a small and inexpensive wind turbine, an estimate can be made of the wind energy resource, as an average over 1, 3 or 5 days, and then per month and per year. A good starting point is to assume five days of energy storage in the batteries at 75% efficiency, and then to determine the wind turbine size that would be required in the least windy five days of the year. It is then possible to vary the capacity of the wind turbine and batteries and compare the cost. If significant demand management is possible, then a reduction in capacity of the system can be adopted and re-optimised for size. The determination of average power output as explained in Section 5.2, gives no information as to the energy storage required. This can only be determined accurately by knowing the time dependence on the wind speed and the loads. For example, Roy et al. (2009) used hourly average wind speed data and an assumed hourly load. The RETscreen software, mentioned in Section 5.2.2, can be used for battery sizing.

5.6.1 Power output Determination of the power output is a major issue for small wind turbines on small towers. Monitoring as done for large wind turbines may cost a significant fraction of the turbine cost, and much of the available wind data, from meteorological bureaux, nearby airports, etc., is usually measured at the standard height of 10 m. Extrapolating from 10 m to, say, 20 m can introduce errors, largely because the two common ‘laws’ for the height dependence of the wind speed, the log law and the power law, may not hold so close to the ground, especially if there are ‘roughness’ elements, such as trees and buildings of similar heights on either the monitored site or the proposed site of the turbine. Furthermore, the wind resource can vary significantly over horizontal distances of the order of tens of metres. It is also likely that climate change over the life of a stand-alone system will have a significant influence on its performance. Extreme winds are expected to increase in probability, but this may be associated with a decrease in the wind resource for most of the time.

5.6.2 Wind maps and software A number of countries have wind maps, for example the UK Department of Trade and Industry map1, which gives wind speeds down to 10 m, 1

A good introduction to the DTI database is through: http://www.bwea.com/noabl/.



187

but does not account for the local roughness. Another extremely useful resource is the previously mentioned Canadian National Research Council RETscreen website. There are some very useful rules-of-thumb for determining the best location for a small turbine, such as DoE (2005), and cheap anemometers and data loggers are available in many electrical stores. (Note that one recently purchased cheap anemometer and datalogger measured average wind speeds, but only recorded hourly maximum and minimum wind speeds.) Computational modelling of airflows for small turbine siting is in its infancy and the current high cost may well decrease substantially with time. Note that none of these resources are likely to give the time-ofday dependence of the wind speed.

5.6.3 Practical wind energy measurement As an alternative to the methods described above, it may be better and cognitively inspirational to install a small and inexpensive turbine, and measure the power produced. Inexpensive turbines usually charge batteries, and hence a simple method of measuring the energy output from the turbine is to apply a lamp to the battery. If over a few days the battery voltage sits under 12 V, then the wind turbine cannot produce the required amount of energy. Then a smaller wattage bulb should be tried. Conversely, if the battery voltage sits above 13 V, a higher wattage bulb can be tried. Over a year of testing in this manner, the power production can be determined, perhaps on a month by month basis, and data from nearby weather stations can be used to correct for seasonal variations.

5.6.4 Wind speed probability distribution and capacity factor Most turbine manufacturers supply power curves on their websites, and knowing the average wind speed allows a probability distribution of the wind to be approximated. The default probability distribution used in wind energy is the Weibull distribution, see section 2.4 of Burton et al. (2001), and Celik (2003). The default value of the ‘shape parameter’ is two. Summing the product of the wind speed probability and the power output at that wind speed gives the average power output of the turbine. The ratio of average to rated power is the ‘capacity factor’ and is the most important piece of information for any installation. For large wind turbine farms, this factor can reach 0.4–0.5, but values closer to 0.20 are typical for small wind turbines. Software packages such as RETscreen also determine the average power output and may save time by having the power curve and assumed probability distribution.


188


5.6.5 Battery sizing In designing a complete remote power system, sizing the battery bank to ensure that there is always sufficient backup power may sometimes cost significantly more than a system designed for a more realistic, 95–100%, reliability (Kaldellis et al., 2007). In considering the reliability of the supply, an analysis should be performed as to what are the essential loads, desired loads and luxury loads. The essential loads must operate nearly every day for the stand-alone system to be worthwhile. The desired loads can wait a few days for the wind to blow. The luxury loads can wait for the days of plenty of wind and stored energy. For household use, the essential loads may be considered to be lighting and perhaps a radio, refrigerator and water pump. The desired loads may be a television and electric power tools, whereas the luxury loads may be a video projector or an electric popcorn maker. These loads have three electrical issues associated with them: • • •

the energy consumed per day; instantaneous power used; the current required to start.

The energy consumed over a day determines the size of the wind turbine and the battery storage. The power the load uses determines the rating of the inverter and the wiring size. The electrical current required to start a load determines the peak power rating of the inverter, size of battery and may affect the wiring size. The size of the battery is affected in this last situation by its ability to supply the starting currents. The maximum load, where all loads come on simultaneously, can usually be avoided with dramatic reductions in the cost of a system. For a small stand-alone PV system known to the authors, the choice was between using the refrigerator or the washing machine. Certainly the refrigerator did not cool properly while the washing machine was being used, but controlling the load in this manner halved the required power rating of the inverter and the distribution wiring. From the other perspective, windy weather is the time to use high-power appliances. The advantages are that the energy does not have to be stored before it is used (reducing energy storage losses), and if the batteries were fully charged, then this power would otherwise be wasted. It is a life style modification, but not usually particularly onerous – no more than waiting for a sunny day to wash and dry the clothes.

5.7

Conclusions

This chapter describes the basic technology of small wind turbines and their use in stand-alone power systems with battery backup. In particular we



189

surveyed blade design especially for low wind speed performance, the choices for generator, controller and inverter. Turbine safety was a main consideration. The size of the power system is critical to its conception and design. These issues were addressed in terms of typical applications and the loads associated with them. It was emphasised that it is often difficult and expensive to determine the wind resource, but there is good worldwide information for many locations and useful general rules for best siting of a turbine. The design of a stand-alone wind turbine power system is based on the expected energy required and the expected wind speeds. Despite the available information, such expectations may be wrong for unforeseen reasons, and in our experience, often in the most difficult direction. One of the uncertainties is that of the climate change – what effect it will have on average wind speeds is uncertain, but in some areas, it appears that cyclonic and tornado activity is expected to increase significantly. In the worst case this may mean that the average wind speeds may decrease, but the peak wind speeds may increase, making it difficult to decide how to choose the turbine. In our experience, the best approach for relatively small systems is to start with a smaller and inexpensive turbine, battery and inverter system to power a limited number of loads to determine if this performs as expected. It will test if there is enough wind, if the battery storage is sufficient, if the loads are manageable and if the system can start the load, such as the particular refrigerator in use.

5.8

References

AWEA (2008). American Wind Energy Association Small Wind Turbine Global Market Study 2008. Bechly, M.E., Gutierrez, H., Streiner, S., Wood, D.H. (2002). Modelling the yaw behaviour of small wind turbines, Wind Engineering, 26, pp 223–239. Burton, T., Sharpe, D., Jenkins, N., Bossanyi, E. (2001). Wind Energy Handbook, John Wiley & Sons. Celik, A. (2003). Energy output estimation for small scale wind power generators using Weibull representative wind data, Journal of Wind Engineering and Industrial Aerodynamics, 91, pp 693–707. Dalton, G.J., Lockington, D.A., Baldock, T.E. (2009). Case study feasibility analysis of renewable energy supply options for small to medium-sized tourist accommodations, Renewable Energy, 34, pp 1134–1144. DoE (2005). Small Wind Electric Systems, US Department of Energy. Available online at http://www.windpoweringamerica.gov/pdfs/small_wind/small_wind_ guide.pdf. Accessed: March 2009. Hong, Y.Y., Shiue-Der Lu, S.-D., Ching-Sheng Chiou, C.-S. (2009). MPPT for PM wind generator using gradient approximation, Energy Conversion and Management, 50, pp 82–89.


190


IEC (International Electrotechnical Commission) (2006). 61400–2 revision 2, 2006, Wind turbines – Part 2: Design requirements for small wind turbines, obtainable from www.iec.ch. Joerissen, L., Garche, J., Fabjan, Ch., Tomzic, G. (2004). Possible use of vanadium redox-flow batteries for energy storage in small grids and stand-alone photovoltaic systems, Journal of Power Sources, 127, pp 98–104. Kaldellis, J.K. (2004). Parametric investigation concerning dimensions of a standalone wind-power system, Applied Energy, 77, pp 35–50. Kaldellis, J.K., Kavadiasa, K.A, Koronakisb, P.S. (2007). Comparing wind and photovoltaic stand-alone power systems used for the electrification of remote consumers, Renewable and Sustainable Energy Reviews 11, pp 57–77. McHenry, M.P. (2009). Why are remote Western Australians installing renewable energy technologies in stand-alone power supply systems?, Renewable Energy 34, pp 1252–1256. Nelson, D., Nehrir, M., Wang, C. (2006). Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems, Renewable Energy 31, pp 1641–1656. Rachidi, F., Rubinstein, M., Montanyà, J., Bermúdez, J.-L., Rodríguez Sola, R., Solà, G., Korovkin, N. (2009). A review of current issues in lightning protection of newgeneration wind-turbine blades, IEEE Transactions on Industrial Electronics, 55, pp 2489 – 2496. Roy, A., Kadere, S.B., Bandyopadhyay, S. (2009). Application of design space methodology for optimum sizing of wind–battery systems. Applied Energy doi:10.1016/j. apenergy.2009.04.032. 86, pp 2690–2703. Shrestha, R.Y. (2009). Small scale wind energy market and institutional model for Nepal, Wind Engineering, 33, pp 109–122. Sullivan, P., Short, W., Blair, N. (2008). Modeling the benefits of storage technologies with wind power, Wind Engineering, 32, pp 603–615. Wood, D.H. (2001). An improved determination of the optimum tower height for a small wind turbine, Wind Engineering, 25, pp 191–196. Wood, D.H. (2004). Dual purpose design of small wind turbine blades, Wind Engineering, 28, pp 511–527. Wood, D.H. (2009). Using the IEC simple load model for small wind turbines, Wind Engineering, 33, pp 139–154. Wright, A.D., Wood, D.H. (2004). The starting and low wind speed behaviour of a small horizontal-axis wind turbine, Journal of Wind Engineering and Industrial Aerodynamics, 92, pp 1265–1279. Wright, A.D., Wood, D.H. (2007). Yaw rate, rotor speed and gyroscopic loads on a small horizontal axis wind turbine, Wind Engineering, 31, pp 197–209.


6 Hybrid wind–diesel energy systems G. BHUVANESWARI and R. BALASUBRAMANIAN, Indian Institute of Technology (Delhi), India

Abstract: Hybrid wind–diesel systems are gaining importance from both technical and economic points of view as they are capable of supplying electricity to remote locations, islands and mountainous areas, located far from the high-voltage grid. This chapter deals with certain salient points concerning the wind–diesel systems such as the circumstances under which such a hybrid wind–diesel system may be installed, system overview, design considerations, selection of generator ratings and control schemes especially under varying wind velocities and fluctuating load conditions, functioning of a battery energy storage system (BESS) and modelling as well as simulation aspects of the complete hybrid wind–diesel system. Key words: hybrid wind–diesel system, wind energy conversion system (WECS), battery energy storage system (BESS), control strategies, permanent magnet synchronous generator (PMSG), induction generators.

6.1

Introduction

The emerging trend of utilizing renewable energy resources for electricity production to the maximum extent has become necessary to limit greenhouse gas emissions and to combat the climate change and global warming menace. Globally, many countries are already intensely engaged in installing new power stations using renewable energy resources. Wind energy is one of the most promising options adopted globally because this technology is mature and has already been installed in large quantities (Jenkins et al., 2000; Masters, 2004; Richardson and McNerney, 1993). Many of these installations are operated under grid-connected mode. However, there is also a strong case for setting up many new stand-alone and hybrid wind–diesel/ wind–photovoltaic power systems and operate them in a decentralized mode to meet the loads at remote locations far from the main grid and in islands (Kaldellis and Kavadias, 2007). Such situations are widely prevalent in developing countries. This can also relieve the main grid from getting over-stressed and also reduce the usage of fossil fuels in the thermal power plants. In those sites where a good wind regime exists, it is extremely beneficial to install wind energy conversion systems (WECS) that can meet the base load demand. 191 © Woodhead Publishing Limited, 2010

192


One of the limitations of WECS, however, is the fact that the wind velocity continuously fluctuates both in the daily cycle as well as seasonally. In view of this, it is desirable to have a hybrid system which will supplement the output of the WECS so that most of the energy needs are met. Wind– diesel systems are appropriate for those locations where diesel oil is available easily (Freris, 1990). The diesel-based generation system has to be used sparingly due to cost and greenhouse gas emission considerations. Also, diesel engines have to be run at a minimum of about 20–40% of their full capacity because of technical limitations of running them on lower loads and because of economic considerations. Further, while operating in conjunction with WECS, diesel engines cannot be turned on and off very frequently because such switchings will lead to fatigue stress developing on the engine mechanical parts. There is also a finite minimum start-up time requirement for these diesel engines. This necessitates the use of an energy storage mechanism that would be able to meet the shortfall when the wind velocity is on the low side even to meet the base load demand. When these energy storage systems are employed, they would be very useful for storing some of the generated power when the wind velocity is high and the load on the system is low. One of the mature and wellestablished technologies for energy storage is the battery energy storage systems (BESS) (Barton and Infield, 2004; Borowy and Salameh, 1997). The coordinated functioning of these three components, viz. the WECS, diesel generation system and BESS, could be a profitable option from both technical and economic points of view. BESS can also aid in real and reactive power control of the entire distributed generated system to achieve optimum power factor at each of the buses (Chiang et al., 1995; Lin et al., 1992) in the system. This chapter presents the salient features of a modern wind–diesel hybrid generation system, its modelling aspects and some simulation studies on this system. The main emphasis of the chapter is on different electrical generators provided with appropriate power electronic converters in these hybrid systems and the corresponding control configurations.

6.2

Overview of wind–diesel generation system

Figure 6.1 shows a schematic diagram of a typical hybrid wind–diesel generation system. The modern trend in wind turbine design is to capture the maximum energy available in the wind through maximum power point tracking (MPPT) even in fluctuating wind velocity conditions (Mutoh and Nagasawa, 2006; Wai et al., 2007). There are also techniques available now to make the mechanical coupling between the wind turbine and the generator gearless, thereby avoiding mechanical transmission loss and increasing the efficiency and reliability of the system.


Hybrid wind–diesel energy systems Pulses

g A B C

+

–

–

Pulses

Tm A

N

B C

C

m

S

Permanent magnet synchronous machine

Machine-side converter

A B C

A B C

Load-side converter

g A B

+

Generator speed(pu) Tm(pu) pitch angle(deg)

wind speed

g A

Excitation

g

1

g

1

m

2

1

m

g m

2

m

Pm

A SSM

E

2

A B C

Diesel engine

pitch angle

wind speed(m/s) Wind turbine

Pulses

B5

193

A B C

B C

+ + –

Battery charger /inverter

B C

Wound field synchronous machine

High- Mediumpriority priority load load

Lowpriority load

Tm - mechanical torque input to the machine Pm - mechanical power input E - excitatian voltage TM(pu) - torque in per unit

6.1 Block schematic of a hybrid wind–diesel energy system with battery energy storage.

The generator used in these systems can be a synchronous generator or an induction generator. Many of the modern WECS use either a permanent magnet synchronous generator (PMSG) or a doubly-fed induction generator (DFIG). The PMSG has permanent magnets in its rotor to provide the necessary magnetic field in the air gap of the machine. The stator will carry the three phase armature winding where the voltage is generated. Induction machines can be used in the generating mode provided the excitation is available from the stator terminals either by means of excitation capacitors (if it is a stand-alone generator) or by means of grid connection. In a squirrel cage induction machine, the stator terminals are only accessible and the rotor is short-circuited. So, the excitation has to be supplied only through the stator terminals. On the other hand, in a wound rotor induction machine which can function as a DFIG, both stator and rotor terminals are accessible and hence the excitation can be provided from either or both of them. Power electronic controllers are used to optimize the output of these generators under varying wind velocity conditions to implement MPPT. These power electronic controllers also play a vital role in the independent control of the real power and the reactive power flow in the system so that the best possible efficiency and power quality are obtained. One desirable feature of these schemes is that the diesel generator will be put in service only when the prevailing wind velocity is insufficient to meet even the essential loads on the system. The preferred choice of generator that is used with the diesel engine is a synchronous generator. A


194


restriction imposed on the diesel engine is that it has to necessarily operate at a minimum loading of about 20% for smooth running of the engine and also for keeping the engine efficiency at a reasonable level. Also, frequent switching on and off of the diesel engine is avoided in these systems to prevent undue fatigue stress developing in the engine. It is often not possible to match the chronological variations in the load demand with that of the variations in the wind velocity. This calls for an energy storage system which may be implemented with the help of a flywheel (mechanical energy storage) or a battery bank (electrical energy storage). In this chapter, BESS, which is a well-established technology, is considered. The power electronic controller used in the BESS should be able to support bidirectional power flow to enable charging and discharging of the batteries as required. Wind–diesel systems are normally implemented in remote locations and hence the reliability requirements of all the loads to be supplied by these systems may not be stringent. So, in the studies that have been presented here, the loads have been divided into three components, viz. an essential or high-priority load (e.g. a medical support system); a medium-priority load (e.g. industrial, agricultural and commercial loads); and a low-priority load (e.g. domestic loads). This kind of a load division will facilitate achieving the best economic operation with the required reliability for the different types of loads.

6.3

Wind turbine sizing in a hybrid wind–diesel scheme

When electrical power is to be supplied to an island or an isolated location far from the electrical grid, distributed generation (DG) is the only option available. Diesel-based power generation is very simple to install in these cases; but to reduce greenhouse gas emissions and also to cut down the operational cost, wind power generation can be a very appropriate option to supplement the electrical power generation from the DG set, especially when the wind regime in that particular geographical location is good (McKenna and Olsen, 1999). Before installing a hybrid wind–diesel system, it is essential to investigate the variation of wind speed in that geographical location with respect to various seasons and also the daily variation on an hourly basis in each season. The average wind speed should be such that with a feasible size of the wind turbine, the electrical power generated should be able to meet at least the essential load requirements. The equation that governs the relationship between the wind velocity and the generated power (Masters, 2004) is: P = 0.5(ρAυ3)ηCp


6.1

Hybrid wind–diesel energy systems

195

where ρ is the air density, which is about 1.225 kg/m3 at a pressure of 1 atm and 15 °C, A is the area swept by the turbine blades in a horizontal axis wind turbine and is given by 0.25*π D2 where D is the diameter of the turbine blade, υ is the average wind speed, η is the efficiency of the gear box and the generator put together, which may be about 0.7 (considering the efficiency of the gear box to be 0.75 and the efficiency of the generator to be 0.93) and Cp is the measure of the rotor efficiency, which is about 0.5 at best. With the help of the above equation and depending upon the power requirement for the essential load, the wind turbine rotor diameter has to be calculated. This must then checked to see if it would be feasible to install such a wind turbine in the available space at an appropriate height.

6.4

Wind–diesel systems: design considerations

The starting point for the design of a wind–diesel system could be to assess the amount of load to be supplied by the distributed generation system. An attempt has to be made to meet the high-priority loads with WECS and BESS despite the variations in the wind velocity; this would be one of the preferred designs as this will minimize diesel oil consumption. As mediumpriority load includes agricultural, commercial and industrial loads, they will occur mainly during the day. Low-priority domestic load requirements will be considerable during the evenings and early morning hours. The diesel engine should be able to support any additional requirement that arises in the form of lower priority loads and also any shortfall caused by wind velocity falling to low values. There are two basic operational strategies possible to be adopted for the hybrid wind–diesel–BESS system. One strategy is to run the diesel engine on a continuous basis. However, in this case, the saving in the diesel fuel consumption will be minimal, despite the fact that the main purpose of installing a hybrid wind–diesel system is to reduce the operational cost. Also, a minimum amount of load should be there on the diesel engine all the time for optimizing its fuel consumption. The other operating strategy is to run the diesel engine on an intermittent basis as and when the requirement arises. In this case, the major problem will be due to the limitations on the frequency of on/off cycling of the diesel engine and also the wear and tear of the DG set caused by frequent switching. To avoid these problems, it is better to have an energy storage system installed. When the wind velocity is below cut-in speed, the energy storage system should be in a position to meet the high priority load demand. Although there are several


196


types of energy storage systems (Kaldellis et al., 2009), BESS is one of the preferred methods of energy storage because of its ability to provide quick response, smaller size as compared to mechanical energy storage systems, and the space required is less.

6.4.1 Load assessment As mentioned in the previous section, the loads can be categorized into three groups, namely, high-priority, medium-priority and low-priority. To start with, it is essential to estimate the quantity of each of these loads and their respective variations on a daily and seasonal basis. The wind velocity profile also has to be studied with respect to its daily and seasonal variations. By comparing the chronological load variation and wind velocity variation profiles, one would be able to assess the mismatches that are likely to occur between these two. This will be the main consideration for fixing the capacity of the energy storage system that is to be installed.

6.4.2 Resource assessment The wind velocity data of the proposed site for the hybrid wind–diesel system have to be collected, analysed and studied thoroughly. The quantum of the high-priority load to be met will be the main criterion for arriving at the rating of the wind turbine to be installed. The design parameters of the wind turbine, such as the rotor diameter and the gear ratio, will be determined based on the wind velocity profile and the rating of the wind turbine (Kaldellis et al., 2006). Diesel oil is available at almost all sites, of course, at a high cost. This is why it was advocated earlier that the diesel oil consumption should be minimized as much as possible by making use of BESS.

6.4.3 Storage requirements As wind velocity fluctuations in the time range of seconds are also common, it is possible that the wind velocity may become too low even to meet the high-priority load requirements. Under such situations, BESS can play an important role. Similarly, when wind velocity reaches high values, BESS can store the excess power generated. Further, the diesel engine has restrictions in terms of minimum allowable loading on it (about 20–40% of its rating) and the frequency of switching it on and off. Also, sudden reductions in the load demand may occur in the system during the time period when the diesel engine is in operation. During such occasions, the presence of BESS will prove to be beneficial.



6.5

197

Components of a hybrid wind–diesel system

The main components of a hybrid wind–diesel power generation system are: • • • • •

wind turbine with associated controls; diesel engine; electrical generator with power electronic controllers; loads; BESS.

6.5.1 Wind turbine The wind turbine should be controlled to capture as much energy as possible from the wind, within safe power limits of the generator and turbine, during low wind speed as well as high wind speed conditions. During low wind velocity conditions, for a given wind turbine design, the tip speed ratio has to be brought to a particular value to achieve MPPT. This is done by adjusting the electromagnetic torque of the generator by employing suitable power electronic controllers so that the quadrature axis component (the torque-producing component) of current in the three phase AC generator is appropriately manipulated. During higher wind speed conditions (greater than the rated wind velocity for which the turbine is designed), pitch control is activated so that generator capacity is not exceeded and the currents and voltages are within safe limits. During this condition, the generator will operate under constant power control.

6.5.2 Diesel engine The diesel engine will have a drooping speed vs. power characteristics which will be controlled by the governor action. The model used for the diesel engine can be a first order model with a single time constant representing the relationship between the fuel consumption rate and the mechanical torque production. The speed governor action is represented by a speed regulation percentage and an integral controller gain. The integral controller helps in eliminating the frequency error during steady state conditions.

6.5.3 Generators Normally the generators used along with the diesel engines are wound field three phase synchronous generators. The terminal voltage of the


198


synchronous generator is maintained at the rated value by means of excitation control. The frequency control is achieved by means of the governor control mechanism of the diesel engine. The generator that goes with the wind energy conversion system could be an induction generator (IG) or a PMSG. If a nigh-rated WECS is being considered (which is very often operated in grid connected mode), then DFIG could be a suitable option. The advantages of using a DFIG are (i) the electrical power generation is possible at sub-synchronous as well as at super-synchronous rotor speeds depending upon the wind velocity; and (ii) the total output of a DFIG could be as high as the sum of the power carrying capacity of the stator winding and that of the rotor winding, when the rotor is driven at super-synchronous speeds. Since the rotor-induced voltage is at slip frequency it has to be converted to grid frequency with the help of a converter–inverter combination before being fed to the grid. The power electronic converters have to be controlled appropriately to achieve real power and reactive power balance under both sub-synchronous and supersynchronous rotor speeds. The power electronic converters in the DFIG scheme are connected to the rotor terminals and they handle only slip power and hence their rating can be much less than that of the overall rating of the induction machine. This results in large amount of savings in the cost of the power electronic converters. For relatively smaller ratings, squirrel cage induction generators (SCIG) with self-excitation capacitors could be an option. However, the machine can work in the generating mode, but only at super-synchronous rotor speeds. If the system is to be capable of generating over a large range of varying wind velocities, then it is essential to insert a converter–inverter circuit, between the stator terminals and the grid. In this case, suitable control has to be provided for these power electronic converters which should be designed for the full rating of the induction machine. The modern trend is to use PMSG as a direct driven (gear-less) generator along with the wind turbine especially in the distributed generating environment (Spooner and Williamson, 1996). The major advantage of using PMSG is that there is no need for supplying DC excitation to the rotor and it can be constructed with large number of poles so that even at lower wind velocity conditions, 50 Hz can be generated without gears. However, if the generator has to produce electricity for varying wind speeds, a converter–inverter circuit has to be inserted between the stator terminals and the load. The rating of these power electronic converters should be the same as that of the full rating of the PMSG. Appropriate controls have to be provided for the power electronic circuits to regulate the frequency and voltage and also to maintain the power factor of the generator at unity. The power electronic controllers also aid in achieving MPPT for varying wind velocity conditions.



199

6.5.4 Loads As mentioned earlier, the loads have been categorized into three types in accordance with their priorities. The loads can be passive loads such as lighting and heating loads; they can be active loads consisting of industrial drives; they can be non-linear loads drawing harmonic-rich currents such as rectifier-fed DC motors, fluorescent lamps with electronic ballasts or power supply systems feeding computers or other medical electronic systems. Depending upon the studies that are undertaken for the system, the loads can be modelled suitably. If simple domestic loads have to be modelled, they are represented by a constant power lumped load. If power quality at the distribution level has to be studied for industrial drive kind of loads, a detailed model of the drive unit has to be adopted. The generator should be in a position to supply all these loads and still maintain a power factor of unity. This will be made possible by making use of power quality conditioners.

6.5.5 BESS The BESS consists of a bidirectional power electronic converter, a current limiting impedance and a battery bank. The voltage rating and the amp–hour capacity of the battery are to be decided based on the chronological variations of the wind velocity and the load. Further, the charging– discharging rates of the battery technology chosen should be able to cope with the actual chronological variations in the wind velocity and the connected essential load. The battery technology is constantly being researched and improved to increase the energy density and reduce the charging time. The technologies that are commonly used are lead–acid and nickel– cadmium batteries. But these two have energy densities of the order of 0.05 kW h/litre and 0.1 kW h/litre respectively. Although the nickel– cadmium battery is three times as expensive as the lead–acid battery, it has a better storage capacity than the former. Silver–zinc batteries are also available in the market which are prohibitively expensive but their energy density is comparable to that of nickel–cadmium batteries. However, their charging rate seems to be better than the other two battery technologies (Freris, 1990).

6.6

Control strategies for wind–diesel generation systems

In wind–diesel generation systems, the major goal is to use diesel oil sparingly, despite the varying wind velocity conditions, and yet meet the essential load demand. For this purpose, a suitably rated BESS has to be installed


200


so that any shortfall in the demand can be met by this storage unit. So, the BESS has to be connected to the generator by a bidirectional converter and appropriate control strategy has to be adopted for controlling this power electronic converter. Different generators used with WECS call for different kinds of control strategies. For example, if a DFIG is used with a control requirement only on real power, then a current-linked configuration with thyristorized rectifier and inverter can be used. If it is necessary to have control on both real and reactive power flow from the generator, then voltage source converters have to be employed. The following sections discuss the control schemes for different types of generators and also for the BESS.

6.6.1 PMSG control with diode rectifier and chopper The block diagram of a PMSG with a diode rectifier, a gate turn-off (GTO) thyristor based chopper and an inverter is shown in Fig. 6.2. The PMSG is coupled to the wind turbine and its variable frequency three phase output is rectified with the help of a diode rectifier. Owing to the variations in the wind velocity, the output voltage magnitude and frequency of the PMSG will change. This will result in variations in the diode rectifier output which has to be boosted or stepped down for maintaining the DC link voltage at the inverter input terminals at a preset value. This DC voltage is converted into a fixed frequency (50 Hz) three phase sinusoidal voltage by means of a sinusoidal pulse width modulated (SPWM) inverter for feeding the load which requires a fairly regulated sinusoidal three phase voltage. The duty ratio control of the DC–DC converter is directly linked to the wind velocity, so it can be adjusted to achieve MPPT. The control schematics for the chopper and the inverter are shown in Fig. 6.3. The chopper duty ratio is adjusted such that the machine adjusts its electromagnetic torque for maximum power deliverability for a given wind velocity so that the tip Tm

w_Wind Tm

Gen_speed

A

w_Turb

N

B

Gen speed

m

S

Wind turbine

C Permanent magnet synchronous machine

Load A B C

Wind_velocity

duty_ratio A

+

+

B C

g A

–

Rectifier

–

B

DC–DC converter

g A B – C Inverter +

6.2 Control scheme for PMSG with chopper and inverter.


Pulses

Hybrid wind–diesel energy systems Diode_rect_output

+

Wind_velocity

_

[Duty_ratio]

PI

MPPT DC voltage comparator

201

Discrete PI controller Id_actual Iq_actual

Voltage_of_input_capacitor

+

IdIq PI

Ref_capacitor_voltage

_ Capacitor voltage PI controller comparator1

-K-

vdvq

Id_Ref

IdIq_Ref

Iq_ref

Current regulator

-K-

P1 Uref P2

[Pulses_to_inverter]

DQ to ABC conversion Discrete three-phase PWM generator1

6.3 Control scheme for the chopper and inverter.

speed ratio of the wind turbine is modified to achieve MPPT (Ohyama et al., 2007). The inverter firing is controlled such that the DC input voltage to the inverter is maintained at a specified value and hence the output voltage and frequency of the inverter are appropriately regulated. The actual three phase currents of the inverter are measured and converted into two phase equivalent in the stationary reference frame itself by means of Clarke’s transformation. Subsequently, these two phase currents in the stationary reference frame are converted into direct and quadrature axes components of currents (Id and Iq) in a synchronously rotating reference frame by means of Park’s transformation. To generate the reference values of direct and quadrature axes components of inverter current, the following procedure is adopted: the DC link capacitor voltage is sensed at regular intervals and compared with a specified reference value; the output of this comparator is passed through a PI controller to determine the reference value of the direct axis component current (Id ref) through the inverter. The reference value of the quadrarture axis component of the inverter current (Iq ref) is set in accordance with the desired power factor. These two reference currents (Id ref and Iq ref) are compared with the actual currents drawn by the inverter (Id and Iq) in a comparator and appropriate devices in the inverter are fired such that the errors in the two components of current are nullified. Thus, the voltage and frequency output of the inverter are regulated.

6.6.2 PMSG control with voltage source inverters While using a diode rectifier and a chopper as mentioned in the previous section, the rectifier will draw harmonic-rich currents because of the presence of the chopper and a filter capacitor at its output DC terminals. This problem could be eliminated if two voltage source converters are used in cascade, one on the machine side and another on the load side. Such a scheme is shown in Fig. 6.4. This scheme is termed as a voltage-linked


202

Stand-alone and hybrid wind energy systems Tm

w_Wind

Wind_velocity

A

Tm

m

N

w_Turb

Gen_speed

B

Wind turbine

Gen_speed

S

C PMSG A B C

Load

g

Pulses

g +

Pulses

+

A

A

B

_

_

C

B C

Machine-side converter

Load-side converter

6.4 Control scheme for PMSG with voltage linked converters.

+

Generator_speed Wind_velocity

-K-

–K–

PI

_

MPPT Desired speed Speed generator comp arator

Discrete PI controller

–K–

Torque Ref

Iq_ref Id_ref

IdIq_Ref [To_machine_side_conv] P1 –K– Uref DQ to ABC P2 conversion1 Discrete three-phase Current PWM Generator1 Regulator

vdvq

IdIq

Id_pmsg Iq_pmsg Id_actual Iq_actual Voltage_of_input_capacitor Ref_capacitor_voltage

+

IdIq PI

_ Capacitor voltage comparator1

PI controller

–K–

Vdvq

Id_Ref

IdIq_Ref

Iq_ref

Current regulator

–K–

P1 Uref P2

[To_load_side_conv]

DQ to ABC conversion Discrete three phase PWM generator1

6.5 Control scheme for the machine-side converter and the load-side converter.

scheme since the input and output converters are connected through a capacitor whose voltage is maintained constant by controlling the firing pulses to the load-side converter (Chinchilla et al., 2006). The control schemes for both the machine-side and load-side converters for this case are shown in Fig. 6.5. The machine-side converter is controlled such that MPPT is achieved for varying wind velocities in the complete range from the cut-in velocity to the rated wind velocity after which the pitch control gets activated. The quadrature axis component of current for the machine-side converter is adjusted such that appropriate value of electromagnetic torque is developed by the generator; the machine and the turbine speeds are adjusted to achieve the tip speed ratio corresponding to the maximum possible power extractable for a given wind velocity. The direct axis component of the current of the machine-side converter is adjusted to a specified value to achieve the desired power factor. When the wind speed becomes high, the pitch controller takes over to ensure that the



203

machine still operates within its rated power and also within its safe speed limits. The load-side converter is triggered in order to maintain the voltage across the DC link capacitor at a constant value. This control is similar to the one discussed in the previous section where the PMSG is controlled with the help of a chopper and inverter.

6.6.3 Control schemes for induction generators The DFIG as well as SCIG are commonly used for wind energy conversion systems because of their ability to operate with varying turbine speeds. The major advantage of DFIG is that it can feed power to the grid (in the case of grid-connected WECS) or to the load (in the case of stand-alone WECS), both from the stator and from the rotor sides. The stator side can be connected to the load directly but the rotor voltage-which is at slip frequency has to be converted to grid frequency by a voltage-linked or current-linked converter cascade. The ratings of these converters could be lower and will correspond to slip power; so it results in a major saving in the cost of the converters. But, in SCIGs, the converters have to be rated for the full-load rating of the machine. If the machine has to be operated only over a small speed range, i.e. from synchronous speed (ωs) to (1 + smax) ωs where smax = slip at maximum torque, then there is no need for using a converter–inverter combination at the stator terminals of the SCIG before connecting it to the grid or to the load. If the SCIG has to operate over a wider range of wind velocity, it becomes absolutely necessary to connect it to the grid/load through a converter–inverter cascade. DFIG control using current linked converters The current linked converter cascade for the DFIG control is shown in Fig. 6.6. As this uses thyristorized converters on the rotor side as well as on the load side, there is no possibility of controlling the reactive power. But real power control can be achieved successfully. This current-linked controller can be operated successfully only for sub-synchronous motoring operation and super-synchronous generating operation. This is because the voltage can be reversed in both the converters depending upon the firing angle range at which each of them is operating, whereas the current cannot be reversed. For example, if the machine is operating in sub-synchronous motoring mode, the rotor side converter will be operating at a delay angle less than 90°, that is it will function as a rectifier. The load-side converter will be operating as an inverter at a delay angle which would be greater than 90°. In the super-synchronous generating mode, both these converters will reverse their roles. The firing scheme for the rotor-side converter should



Gen_speed

m

Tm

a

A

b

B

c

C

w_Wind

Wind_velocity

w_Turb

Gen_speed

Tm

Wind turbine

Load A B C

204

Doubly_fed induction machine

Pulses

g

g +

A B

Pulses

+ A

_

_

C

B C

Rotor-side converter

Load-side converter

6.6 Current-linked converter-based controller for DFIG.

be capable of handling variable frequency and variable phase sequence as the voltage at the rotor terminals will be at slip frequency; the phase sequence of this voltage would be ACB, when it is operating in supersynchronous mode if originally it was having a phase sequence of ABC in the sub-synchronous mode. The grid-side converter has to be triggered with the help of a phase-locked loop (PLL) that would synchronize the triggering signals with a 50 Hz three phase sine-wave generated by an independent oscillator. DFIG control using voltage-linked converters If the DFIG has to be operated in all four quadrants of operation namely, sub-synchronous motoring and generation as well as super-synchronous motoring and generation, it is essential to use voltage-linked converters in its rotor terminals as shown in Fig. 6.7. When the machine is operating in sub-synchronous generation mode, the wind velocity will be low, so some slip power will be fed to the rotor through the converter–inverter combination. Both the mechanical power fed by the wind turbine and the electrical power fed by the converters to the rotor in the form of slip power will be converted into electrical power which will be fed to the grid through the stator terminals. On the other hand, when the wind velocity is high, the machine will be operating in super-synchronous generating mode, wherein the slip power will be flowing from the rotor terminals with the help of the power electronic converters to the load/grid. At the same time, the stator will also be feeding the generated electrical power to the load/grid.



Pulses

m

Tm

a

A

b

B

c

C Doubly-fed induction machine

w_Wind

Wind_velocity

w_Turb

Gen_speed

Tm

Wind turbine

g

g +

Pulses

+

A B

Load A B C

Gen_speed

205

A

_

_

C

B C

Rotor-side converter

Load-side converter

6.7 Voltage-linked converter for the control of DFIG.

The control scheme for the voltage-linked converter for the DFIG is similar to that discussed earlier for the PMSG case and hence is not repeated here. Pena et al. (1996) have proposed a vector control scheme for the gridside converter for the control of active power and reactive power drawn from the supply. MPPT can be achieved in the voltage-linked converterbased control scheme for a DFIG by controlling the rotor-side converter suitably (Datta and Ranganathan, 2002, 2003). Control of SCIG The SCIG can be tied to the grid/load directly, if it is operating within a small range of wind velocity values. It will generate a variable frequency voltage and current if it is being operated at wide speed ranges by a wind turbine. This has to be converted into 50 Hz voltage and frequency before being fed to the load. This calls for a converter–inverter combination to be connected at its stator terminals. The scheme is shown in Fig. 6.8. The control of this is similar to that of the scheme discussed earlier for the voltage-linked converters for PMSG.

6.6.4 Control of energy storage units The energy storage unit considered here is a battery energy storage system (BESS). The BESS is connected to the common bus which couples the terminals of both DG and the generator in WECS. The BESS is connected to this bus by means of a bidirectional power electronic converter as


206


Wind_velocity

Tm

w_Wind

A

Tm w_Turb

Gen_speed

m

Gen_speed

B Wind turbine C Squirrel cage induction machine A B C

Load

Pulses

g

g

Pulses

+

+

A

A

B

B

–

–

C

C

Stator-side converter

Load-side converter

6.8 Control scheme for the squirrel cage induction machine.

From power electronic converter Id_actual Id_actual Total_power_generated

+

IdIq PI

Load_power_demand

– Power comparator

PI controller

-K-

P1 vdvq

Id Ref

IdIq_Ref

Iq_ref

Current regulator

-K-

[To_Battery_charger]

Uref

P2 DQ to ABC conversion Discrete three phase PWM generator

6.9 Control scheme for a BESS.

depicted in Fig. 6.1. The control for the BESS unit is shown in Fig. 6.9. The real power control is achieved by means of controlling the direct axis component of current of the power electronic converter and the reactive power control is by means of the quadrature axis component of current. The actual three phase currents of this power converter are converted into direct and quadrature axes components of currents by means of Clarke’s and Park’s transformations as explained in Section 6.6.1. The difference between the total power generated by the wind–diesel system and the power demand from the load side is computed and this is the input signal to the control unit of the BESS. This is passed through a proportional-integral (PI) controller to compute the direct axis component of current for the power electronic converter. The quadrature axis component of current is specified based on the desired power factor. The triggering signals for the devices in the power electronic converter are generated by the current controller based on the difference between the reference values of Id and Iq and the actual values of Id and Iq of the converter respectively. It is worth noting that this converter is capable of acting as a rectifier or an inverter.



6.7

207

Modelling and simulation of wind–diesel systems

A model developed in the MATLAB/SIMULINK environment for a combined wind–diesel–BESS scheme is shown in Fig. 6.10. For the system illustrated here in the simulation studies, the sizing of various components has been done as follows: The average wind speed considered is 9.5 m/sec. The highest priority load to be met is considered to be 30 kW. The wind turbine should be able to generate this required power of 30 kW at the average wind speed. For this purpose, the wind turbine rotor diameter can be calculated by making use of equation (6.1) as: 30 × 10 3 0.5 × η × 1.225 × 0.5 × 9.53 2 = π ( D 2)

A=

‘D’ being the diameter of the swept area. Then, the value of D can be deduced to be 14.4 m. There should be enough space with a reasonable vertical clearance to install such a wind turbine. To calculate the ratings of the battery, the following calculations can be done Let the load be supplied with a 3-phase AC voltage of 240 V (line to line). When it is converted into DC using a PWM rectifier, the output DC will be about 250 V. For a 30 kW load to be met by a 250 V battery for a duration of one hour, the A h capacity of the 250 V battery should be 120. Initially the battery will go through constant current charging and subsequently, to maintain the battery voltage at the rated value, it will go through constant voltage charging. While discharging through the PWM inverter to meet the high priority load during low wind velocity conditions, it can give a back-up time up to a maximum of 1 h while feeding a 30 kW load. When the medium priority load of 25 kW is also turned on, the wind power generation unit alone will not be capable of meeting the complete load demand of 55 kW in which case the diesel engine has to be turned on. If the load happens to be less than the total generation, then battery will get charged. The rating of the DG set is also considered to be 30 kW, so that even at 40% load it would be able to generate about 12 kW. When the lowest priority load of about 20 kW is also on, then the wind generator, diesel generator and BESS together will have to meet the total load. In the model created for the wind–diesel system, the highest priority load itself has been taken in two parts consisting of 11 kW and 16 kW respectively. The wind energy conversion system uses a PMSG rated for about 27 kW with its terminal voltage being close to 230 V (line to line) when the wind velocity is at the rated value of 10 m/s. At this wind velocity the generator runs at the rated speed of 3000 rpm. If the wind velocity changes, the MPPT algorithm adjusts the speed of the generator by adjusting the


Loads

B C

B C

B

B C C Three-Ph1

A

A

[I_pmsg]

[V_pmsg]

B2

–

+ C

IBGT Inverter

C

B

A

g

Vdc

[V dc]

lab

Pwm controller

Three-Ph

A

A

Vabc pulse

B5

B3

Vf_

pm

–

+

[cont]

D1

[Vdc]

-K-

[V d1]

Vdc1

+ v–

L1

O

Rad to RPM1

c

i + –

w

Vt

Vt

Pm

m

vtref

wref

+

[Wind]

[V d1]

1 Vtref(pu)

1.0

wref(pu)

–

C

B

A

Bridge

C1

+

[Wind]

S

N

-K-

m

Pu to Nm

MPPT

I_Bat

V_Bat

Q_Wind

[cont]

Bus

[Gen_speed]

Chopper duty controller

pulses Vdc_ref

I_Bat Q_Bat P Q measurement

V_Bat

I_wind Q_Load

P_Bat

[I_pmsg]

I_Load

V_wind Q_DG

Vdc

P_Load

P_DG

V_Load P_Wind

I_DG

V_DG

[V_pmsg]

[I_Load]

[V_Load]

[I_DG]

[V_DG]

selector Permanent magnet synchronous machine

C

B

A

Tm

Wind Turbine

Wind speed (m/s)

Pitch angle (deg) Tm (pu)

Generator speed (pu)

R

Diesel Engine Speed&Voltage Control

Vf (pu)

Pmec(pu)

Wind speed

+ –v Vdc

Clock

[Gen speed]

C IBGT Inverter1

B

A

g

pulse

Synchronous machine pu standard

C

B

A

m

[V_DG]

[I_pmsg]

I_Bat

V_Bat

[I_DG]

[V_DG]

[DG_speed]

6.10 Complete SIMULINK model for the wind–diesel system with BESS.

[I_Load]

[V_Load]

B4

A B C

g a k



209

developed electromagnetic torque or the quadrature axis component of current of the generator. This results in a variation in the magnitude and frequency of voltage generated by the machine and hence, it is converted into DC and then back to AC of a specified frequency with the help of an inverter before being fed to the load. As the output of the inverter is in the form of a pulse width modulated (PWM) voltage waveform, it has to be converted into a sinusoidal voltage by connecting an inter-phase reactor of a few millihenries (mH) of inductance. The rating of the diesel generator employed is also chosen to be 30 kW to match the sum of priority 2 and priority 3 loads put together. The diesel engine is controlled by a governor mechanism. It is coupled to a normal (wound field) synchronous generator whose output is perfectly sinusoidal. The two generators from the DG and the WECS are paralleled together to feed the loads from a common bus. The battery charger is connected to the same bus where the load is connected. The battery charger has the ability to allow bidirectional power flow. So, if the wind velocity is low and the generated power is less than the load demand, then the battery would discharge to supply the additional power required by the load. On the other hand, if the wind velocity is high enough and the load is lower, then the battery would get charged. A set of simulations have been run on this system and the results are presented in the following sections.

6.7.1 A combination of WECS–BESS feeding a load The first simulation has been done only with a WECS–BESS combination feeding a load. The total load connected to the system has been considered as a combination of two loads, one of 11 kW and another of 16 kW capacities respectively. The circuit breaker that connects the 16 kW load is first closed at 0.6 s, once the system reaches steady state after the initial starting transients; the same circuit breaker is subsequently opened at 1 s. The wind velocity varies from the original value of 10 to 8 m/s at 0.8 s. It remains constant at the value of 8 m/s until 1.4 s, after which it changes back to 10 m/s. The power variations for this simulation case are shown in Fig. 6.11, which clearly show that the battery feeds power to the load when the load demand is higher and wind velocity is at 8 m/s. This happens from 0.8 to 1 s. For rest of the duration, the battery power is negative which means it is getting charged. Figure 6.12 gives the response of the PMSG that is coupled to the turbine for the variations in the wind velocity. It may be observed from this figure that as the velocity decreases, the voltage and current outputs of the generator diminish. The torque and the speed also are suitably varied by the controllers according to the wind velocity variations so that the maximum power output for that wind velocity is achieved. The


Load power

PMSG output

Battery output

210 2

Stand-alone and hybrid wind energy systems × 104

0 –2 –4 4

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0.2

0.4

0.6

0.8

1 Time (s)

1.2

1.4

1.6

1.8

2

× 104

2 0 –2 2

× 104

1 0

6.11 Power variations: battery output, WECS output and load consumption in Watts.

voltages at the PMSG terminals are converted in to DC and then back to AC at 50 Hz with the help of a PWM inverter and hence the voltages are in the form of a PWM waveform.

6.7.2 A combination of WECS–DG–BESS feeding a load The next set of simulations has been done with the DG set also in service. The disturbances given to this system are wind velocity changes and the load changes. The response of the system for these disturbances is shown in Fig. 6.13. The total load included in the system for this case amounts to 40 kW. The DG is capable of generating a real power of 30 kW. The generator that is connected to the diesel engine is a wound field synchronous machine and the engine speed is regulated with the help of a governor. The total load on the system is 15 kW to start with. From 0 to 0.4 s, the load remains constant at 15 kW and the WECS generates about 25 kW of power at a wind velocity of 10 m/s. The DG generates a power of about 6 kW (20% of its rated power). So, the excess power generated, which is about 16 kW, is utilized in charging the battery. At 0.4 s, the wind velocity reduces to 8 m/s. The generated power from the WECS decreases to 12 kW due to which the battery is able to tap a power of only 3 kW. At 0.6 s, the load increases to 22 kW; at this point, the battery releases its energy and is able to meet the shortfall in the generated power that amounts to 4 kW. The load increases further to 26 kW at 1 s and


Wind velocity (m/s)

Hybrid wind–diesel energy systems (a) 12 10 8 6 0

Vs (V)

1000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0.72

0.74

0.76

0.78

0.8

0.82

0.84

0.86

0.88

211

2

(b)

0

–1000 0.7

Is (A)

200

(c)

0

–200

0.6

0.8

1

1.2

1.4

1.6

Te (N m)

(d) 0 –50

–100

Speed (rad/s)

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0.8

1 1.2 Time (s)

1.4

1.6

1.8

2

(e)

340 320 300 280 260 240 220 0.2

0.4

0.6

6.12 Response of PMSG for wind speed variations: (a) wind velocity, (b) PMSG line to line voltage, (c) PMSG phase current, (d) electromagnetic torque, (e) speed of the PMSG.

DG steps up its generation by another 4 kW. Now the BESS, DG and WECS together are able to meet the load demand. At 1.1 s, the wind velocity changes to 10.5 m/s and hence the WECS is able to deliver about 30 kW of power. At this point, the BESS starts storing energy again. At 1.2 s, there is a reduction in the load demand and the DG power is also brought down to 20% of its rating. The excess power generated goes towards battery charging.

6.8

Conclusions

Hybrid wind–diesel power generation systems are gaining a lot of importance because of their suitability from both economic and technical points


Battery power

212

Stand-alone and hybrid wind energy systems × 104 1 1 –1 –2 0.2

0.4

0.6

0.8

1.0

1.2

1.4

0.4

0.6

0.8

1.0

1.2

1.4

0.4

0.6

0.8

1.0

1.2

1.4

0.4

0.6

0.8

1.0

1.2

1.4

Load power

DG power

PMSG power

× 104 3.0 2.5 2.0 1.5 1.0 0.2 14 000 12 000 10 000 8000 6000 0.2 × 104 2.5 2.0 1.5 0.2

Time (s)

6.13 Power flow in watts from WECS, DG and BESS during load changes.

of view especially for meeting loads at remote locations such as the rural areas far away from the main high-voltage grid, mountainous areas and islands. In this chapter, a description of some modern hybrid wind–diesel systems including salient considerations in the design of such systems, different state-of-the-art types of generator such as PMSG and DFIG used for WECS and the control strategies adopted in them have been presented. The salient aspects of modelling and some typical simulation results have been presented for the system for varying wind velocities and varying load conditions. The key factor in the control of a hybrid wind diesel system is to minimize the diesel fuel consumption, simultaneously maintaining the power output of the diesel engine at least at a specified minimum permissible value whenever it is running and at the same time limiting its on–off switching durations within the permissible range under both varying wind and load demand conditions. It would be advantageous if the wind power generator could be controlled in such a manner as to achieve the maximum power capture by the wind turbine rotor under varying wind velocity condi-



213

tions. For this purpose, various control schemes have been suggested in the literature and some of them have been discussed in this chapter. Considering that wind power generation itself is a growing and fertile area of research, it is difficult to do justice to all the aspects of this topic in the limited space available. The idea was to cover most of the salient aspects so that the reader can build on this further.

6.9

Future trends

The future electrical energy systems can be visualized as a combination of entities having two distinct structures, one set consisting of highly efficient thermal power plants of both coal as well as gas-fired types operating with almost zero greenhouse gas emissions, nuclear power plants and major storage type hydro-power plants interconnected by high voltage transmission lines and the other set consisting of DG systems utilizing renewable/ non-conventional energy resources such as wind, solar, small hydro and agro/small industrial wastes, interconnected through medium/low-voltage distribution lines and feeding local loads. The wind–diesel system, which is the subject of this chapter, falls under the second category. Though these systems are normally called wind–diesel systems, they may also use biofuels, produced from the locally available wastes/energy plantations grown locally. More and more of this type of system are expected to be installed in the future because they hardly pollute the atmosphere, keep the transmission losses at a low level because of proximity of the loads to these generators. Incidentally this also becomes an effective method for the disposal of wastes generated. When many of these distributed resources are operated together, it becomes necessary to address a host of technical issues, such as optimal operation of these systems, coordination of protection systems of these DGs, stability of these systems and the quality of power generated by these systems. Coordinated control of wind and diesel power generation systems along with the energy storage unit is a challenging task. The controllers for these systems have to be designed not only to operate properly and stably in steady state for various wind and load conditions that arise in the system, but should also be able to yield good dynamic response while going from one steady state to another steady state. For ensuring this, as these systems evolve, one can think of a more sophisticated control system, which will be able to tune their control parameters on-line using self-tuning regulators, fuzzy logic controllers or neuro-fuzzy controllers. Some of these have been attempted in the literature (Ko et al., 2003; Mufti et al., 1996), although actual implementation of these intelligent algorithms in WECS are still rare. Looking into the development of new types of generating scheme, switched reluctance generator and permanent magnet brushless generators are


214


seriously being explored in WECS especially for lower power generation capacity. This has opened up a lot of new avenues for research in design and development of variable speed generation systems using the abovementioned generating schemes. BESS is also undergoing a lot of changes due to new materials that are being used in the battery technology that reduce the charging time and increase the storage capacity. Some of these will change the face of wind–diesel system completely.

6.10

References

Barton J P and Infield D G, ‘Energy storage and its use with intermittent renewable energy’, IEEE Transaction on EC, Vol. 19, No. 2, June 2004, Pages 441–448. Borowy B S and Salameh Z M, ‘Dynamic response of a stand-alone wind energy conversion system with battery energy storage to a wind gust’, IEEE Transactions on EC, Vol. 12, No. 1, March 1997, Pages 73–78. Chiang S, Huang S C and Liaw C M, ‘Three-phase multi functional battery energy storage system’, IEE Proceedings on EPA, Vol. 142, No. 4 July 1995, Pages 275–289. Chinchilla M, Arnaltes S and Burgos J C, ‘Control of permanent-magnet generators applied to variable-speed wind-energy systems connected to the grid’, IEEE Transactions on EC, Vol. 21, Issue 1, March 2006, Pages 130–135. Datta R and Ranganathan V T, ‘Variable speed wind power generation using doubly fed wound rotor induction machine – A comparison with alternative schemes’, IEEE Transactions on EC, Vol. 17, No. 3, Sept 2002, Pages 414–421. Datta R and Ranganathan V T, ‘A method of tracking peak power points for a variable speed wind energy conversion system’, IEEE Transactions on EC, Vol. 18, No. 1, March 2003, Pages 163–168. Freris L L, Wind Energy Conversion Systems, Prentice Hall International (UK) Ltd, 1990. Jenkins N, Allan R, Crossley P, Kirschen D and Strbac G, Embedded Generation, IEE Press, 2000. Kaldellis J K and Kavadias K A, ‘Cost–benefit analysis of remote hybrid wind–diesel power stations: Case study Aegean Sea islands’, Energy Policy, Volume 35, Issue 3, March 2007, Pages 1525–1538. Kaldellis J K, Kondili E and Filios A, ‘Sizing a hybrid wind–diesel stand-alone system on the basis of minimum long-term electricity production cost’, Applied Energy, Volume 83, Issue 12, December 2006, Pages 1384–1403. Kaldellis J K, Zafirakis D and Kavadias K, ‘Techno-economic comparison of energy storage systems for island autonomous electrical networks’, Renewable and Sustainable Energy Reviews, Volume 13, Issue 2, February 2009, Pages 378–392. Ko H S, Niimura T and Lee K Y, ‘An intelligent controller for a remote wind-diesel power system – Design and dynamic performance analysis’, Proceedings of the PES General Meeting, Vol. 4, July 2003. Lin C E, Shiao Y S, Huang C L and Sung P S, ‘A real and reactive power control approach for battery energy storage system’, IEEE Transactions on Power Systems, Vol. 7, No. 3, August 1992, Pages 1132–1140.



215

Masters G M, Renewable and Efficient Electric Power Systems, Wiley-Interscience, 2004. McKenna E and Olsen T L, Performance and Economics of a Wind–Diesel Hybrid Energy System, National Renewable Energy Laboratory, 1999 www.nrel.gov/ docs/fy99osti/24663.pdf Mufti M, Balasubramanian R and Tripathy S C, ‘Self-tuning control of wind–diesel power systems’, Proceedings of the IEEE Conference PEDES, 1996, Vol. 1, Pages 258–264. Mutoh N and Nagasawa A, ‘A maximum power point tracking control method suitable for compact wind power generators’, Proceedings of the IEEE Power Electronics Specialists Conference (PESC), 18–22 June, 2006, Pages 1–7. Ohyama K, Arinaga S and Yamashita Y, ‘Modeling and simulation of variable speed wind generator system using boost converter of permanent magnet synchronous generator’, European Conference on Power Electronics and Applications, 2–5 Sept 2007 Pages 1–9. Pena R, Clare J C and Asher G M, ‘Doubly fed induction generator using back to back PWM converters and its application to variable-speed wind-energy generation’, IEE Proceedings on EPA, Vol. 143, No. 3, May 1996, Pages 231–241. Richardson R D and McNerney G M, ‘wind energy systems’, Proceedings of the IEEE, Vol. 81, Issue 3, March 1993, Pages 378–389. Spooner E and Williamson A C, ‘Direct coupled permanent magnet generators for wind turbine applications’, IEE Proceedings on EPA, Vol. 143, No. 1, January 1996, Pages 1–8. Wai R J, Lin C Y and Chang Y R, ‘Novel maximum power extraction algorithm for PMSG wind generation system’, IET EPA, Vol. 1 (2), 2007, Pages 275–283.


7 Hybrid wind–photovoltaic energy systems G. NOTTON, University of Corsica, France

Abstract: Photovoltaics (PVs) offer consumers the ability to generate electricity in a clean, quiet and reliable way by a direct conversion of solar light energy into electricity. This chapter begins with a brief presentation of solar and wind resources while special attention is given to their complementarity. After discussing their design, each subsystem is presented and the calculation of kW h cost is discussed. Optimal methods for hybrid system sizing are shown while, finally, two cases studies which implement and illustrate the various points discussed in the chapter are presented. Key words: wind/photovoltaic system, solar and wind energy resources, optimal sizing method.

7.1

Introduction

Solar and wind energy resources vary greatly over time and do not usually match with the time distribution of the load; thus photovoltaic (PV) or wind energy systems alone must be oversized if each system is used separately, leading to high electrical energy costs. Integrating solar and wind energy into the same system attenuates fluctuations in the power produced, improving total system performance and reliability, and significantly reducing the size of storage required. Wind–PV systems combine both wind and PV technologies, often coupling them to an engine generator and an energy storage system. Of course, the sizing of such a system is much more complicated than that of a single source system due to the higher number of variables and parameters to be considered in the optimal design. The design of such a system involves the determination of optimum values for the wind turbine’s rated power, PV array peak power and storage capacity (and sometimes also the engine generator characteristics) that meet the required reliability conditions for the system. In the following sections, a brief presentation of both solar and wind renewable energy sources, and of each subsystem, is given before some consideration of the optimal sizing of such a hybrid system is presented.

7.2

Renewable energy resources and their potential

Solar and wind power are accepted as dependable and widely available renewable energy sources. No renewable system can be implemented 216 © Woodhead Publishing Limited, 2010

Hybrid wind–photovoltaic energy systems

217

without a precise study of the available resources due to the high variability of the two sources. In a wind–PV energy system, the problem is doubly important because of the presence of two energy sources. A good summary of the energy model is given by Jebaraj & Iniyan (2006).

7.2.1 The solar energy resource In any solar energy conversion system, knowledge of global solar radiation is extremely important for the optimal design and forecasting of the system’s performance. Solar radiation arrives at the surface of the Earth in a spectrum of wavelengths, ranging from X-rays to radiowaves, the sun radiating as a black body at 5777 K; however, 99.9% of the emitted energy is between 0.2 and 8 μm. Each wavelength possesses a distinct ability to work and effect system transformation. In PV conversion, the range of useful wavelengths is between 0.35 and 1.1 μm for a silicon PV cell but, generally, global solar irradiation is measured in its entire spectrum for PV system studies (using a pyranometer or silicon irradiance sensor). The difference noted in the levels of spectral irradiance between the top of the atmosphere and the surface of the Earth is due to the absorption taking place by various chemical compounds, such as O2, O3, H2O and CO2, by aerosols and by Rayleigh scattering (Fig. 7.1). Owing to atmospheric effects, solar radiation at the Earth’s surface consists of two components: beam radiation from the sun (without change of direction), and diffuse radiation received from the

Direct normal spectral irradiance (W m–2 mm–1)

2200 2000 Extraterrestrial 1800

Black body at 5777 K

1600

Rayleigh attenuation

1400 1200 1000

Atmosphere without aerosols (b = 0) Air mass = 2 O3 = 0.35 cm (NTP) Precipitable water = 2 cm Extraterrestrial solar irradiance = solar constant = 1367 W m–2

O3 H2O

800 600

O2 H2O

400 200

Direct solar flux reaching the ground

H2 O

H2O,CO2 H2O,CO2 H2O 0 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00 3.25 3.50 3.75 4.00 O3 Visible

Wavelength (mm)

7.1 Direct normal spectral irradiance and identification of various molecular absorbers.


218


Solar radiation reflected back to space Thin clouds Beam

Cloud

Beam Anisotropic diffuse radiation on horizontal surface

Aerosols

Beam

Sky diffuse

Normal

b Air molecules

Ground-reflected diffuse

Inclination

7.2 Attenuation of the extraterrestrial radiation and components of the solar radiation onto a tilted surface.

sky; when a solar collector is inclined, it receives a third component: ground reflected radiation (Fig. 7.2). The total radiation received on a tilted surface Gβ is expressed by (Iqbal, 1983): Gβ = Gb,β + Gr,β + Gd,β

7.1

where Gb,β is the beam radiation on the tilted surface; Gr,β is the diffuse reflected radiation on the tilted surface; Gd,β is the sky diffuse radiation on the tilted surface. The components of solar irradiance for clear and cloudy skies are shown in Fig. 7.3. Horizontal solar global radiation is the form most commonly measured. Depending on the objective (whether for sizing, behaviour simulation, etc.) the time-step of useful meteorological data varies from hourly to monthly average daily data. Solar irradiation on non-horizontal surfaces is much less available, and is difficult to model owing to the effect of diffuse radiation anisotropy over the sky’s dome. Converting solar irradiation from the horizontal to the inclined plane is realized by using accurate models for monthly average values (INES, 2009) while less reliable methods are used for data measured on an hourly basis (Notton et al., 2006). Additionally, solar data can also be found on the web (NASA, 2009; NREL, 2009; UMass Lowell, 2009). Knowledge of the sun’s position allows two useful pieces of information to be obtained: the solar panels’ optimal inclination and a solar diagram for the estimation of shading on the PV modules. Solar position is computed from various angles, such as declination, zenith angle and hour angle (Iqbal, 1983). Optimal inclination depends on latitude, φ, on the seasonal



219

1000

Solar irradiance on tilted plane (W m–2)

03 April b = 30°

21 August b = 45°

900 800 700 600 500

Sky diffuse

400 Beam 300 Ground-reflected diffuse

200 100 0 5

6

7

8

9 10 11 12 13 14 15 16 17 18 6

7

8

9 10 11 12 13 14 15 16 17 18

Hours

7.3 Two examples of tilted solar irradiances.

distribution of the load and on the meteorological conditions of the site. For long periods without marked seasonal weather conditions, the estimation of the inclination effects are based on calculations from extraterrestrial or beam irradiation, in order to avoid the use of a diffuse radiation model; in these conditions, the inclination depends on φ and is, for a yearly, winter and summer optimal solar irradiation, equal to φ, φ + 10 ° and φ − 10 °, respectively (Duffie & Beckman, 2006). Figure 7.4 shows the impact of the inclination angle (for Ajaccio, France) on extraterrestrial and ground solar irradiation. It is useful to estimate shading with a solar diagram: Fig. 7.5 traces the apparent path of the sun in the sky for a given latitude, point by point, indicating hours, in the actual local solar time. To put the masks of the sun path on the diagram, the height and azimuth of a few important points were measured, including a house and a tree, as shown in Fig. 7.5. In this example, the house will shade the PV system form the sun at solar noon on 21 December. There are numerous PV sizing softwares that integrate the calculation of a solar mask.

7.2.2 Wind energy resource The wind energy per unit of cross-sectional area during a time period Δt is: Ewind =

1 ρa v3 Δt 2


7.2

220


12.5 Inclination of PV modules 11.5 0°

10°

20°

30°

40°

50°

60°

70°

80°

90°

10.5

Solar irradiation (kW h m–2)

9.5 8.5 Daily total solar irradiation on the earth's surface

7.5

Ajaccio, France Latitude = 41°55’36’’ N

6.5 5.5 4.5 3.5 2.5 1.5

Daily extraterrestrial solar irradiation Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Year

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Year

7.4 Influence of panel inclination on monthly mean values of solar irradiations from extraterrestrial and ground estimated data (for Ajaccio).

80

Zenith Latitude: 41.92° 11 Summer solstice

10

Equinox

N

50

11 10

8

40

9 7

11 30

8

6 5

W

60

9

S Winter solstice

Solar time

21 June

Solar altitude (°)

E

12 70

10 9

7 6

22 March 21 September

20 10

8

21 December

0 –140 –120 –100 –80 –60 –40 –20 0

20

40

60

80 100 120 140

Solar azimuth (°)

7.5 Position of the sun and solar diagram for shading estimation.

where air density ρa = 1.23 kg m−3 at 15 °C and at sea level. Ambient temperature, pressure and air humidity influence the air density. This energy cannot be entirely used by a wind turbine, because only the energy available between the cut-in and cut-out speeds is useful. The energy output of a wind turbine is obtained by coupling the wind speed probability distribution and the turbine’s power curve, as seen in Fig. 7.6. The Weibull probability density functions are commonly used and widely adopted (Celik, 2003; Chang & Tu, 2007; Ngala et al., 2007; Bagiorgas et al., 2007; Elamouri & Ben Amar, 2008; Kaldellis, 2008); this function is a special case



221

900

2.0 800 700

1.5

Power curve

500

Wind turbine 2 kW Production: 2435 kW h Number of running hours at nominal power: 1218 h Average power: 0.278 kW Production coefficient Cp: 0.139

Wind distribution (hours/year) k=2

400 300

Vaverage = 4 m s–1 at 10 m

1.0

Power (kW)

Hour/year (kW h)

600

0.5

200 100

Production 0

0

0

1

2

3

4

5

6

7

8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Wind speed (m s–1)

7.6 Coupling between wind distribution and wind turbine power curve for production estimation.

of the gamma distribution and is characterized by its probability density function f(v) and cumulative distribution function F(v) in the following formulae: k v f ( v) = ⎛ ⎞ ⎛ ⎞ ⎝ A⎠ ⎝ A⎠

(k −1)

⎡ v k⎤ exp ⎢ − ⎛ ⎞ ⎥ ⎣ ⎝ A⎠ ⎦

⎡ v k⎤ F ( v) = 1 − exp ⎢ − ⎛ ⎞ ⎥ ⎣ ⎝ A⎠ ⎦

7.3 7.4

where A is the scale parameter (in m s−1), k is the unit-less shape parameter and v the wind speed. The most common method to calculate Weibull distribution parameters is based on the use of Equation 7.4, performing the logarithm calculation twice for the two terms of the equation and then employing a least-squares fit method to calculate the two coefficients, A and k (Fig. 7.7), characterizing the wind potential of a site. Since most available wind speed measurements are made near the ground (generally at 10 m) it is necessary to extrapolate the wind speed profile within the surface atmospheric boundary layer. The most common extrapolation is based on a power-law velocity equation preferred by engineers for its mathematical simplicity (Justus et al., 1976; Zoumakis, 1993): v ⎛ z⎞ =⎜ ⎟ v0 ⎝ z0 ⎠

α

7.5


0

Experimental cumulative distribution

5 10 15 Wind speed (m s–1)

0.5

Ln (V)

1

1.5

2

2.5

y = 1.4201x – 1.936 R 2 = 0.9998

Least-squares fit method

3 2 1 0 –1 –0.5 0 –1 –2 –3 20 –4 Ln {–Ln [1–F(v)]}

Frequency

7.7 Method to calculate the Weibull distribution.

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 3

Frequency A = 3.91 m s–1

k = 1.42

0.02

0.04

0.06

0.08

0.10

0.12

0.00 6.5 8.5 10.5 12.5 14.5 16.5

Measured F (v) Measured f(v) Calculated F (v) Calculated f(v)

Wind speed m s–1 Experimental verification

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0 0.5 2.5 4.5

Frequency



223

where α depends on the topography and climatic conditions, v and v0 are the wind speeds at heights z and z0 (z0 being the reference height).

7.2.3 Complementarity of renewable resources

Wind energy Wind speed (W h m–2) (m s–1)

Solar and wind energy are non-depletable, site-dependent, non-polluting, and potential sources of alternative energy. As specified by Ozdamar et al. (2005), the biggest problem for a separate use of wind energy and solar energy is their discontinuity: a solar energy system by itself cannot provide a continuous source of energy due to its low availability during each no-sun period and during winter, while a wind energy system cannot itself satisfy constant load demand due to the different magnitudes of wind speed from one hour to another. In general, the variations/fluctuations of solar and/or wind energy generation do not match the time distribution of load demand on a continuous basis. The complementary nature of wind and solar resources in the US was examined as early as 1981 by Aspliden (1981), and a more recent study was realized by Reichling & Kulacki (2008). Special attention was paid to the energy sources’ complementarity in studies of wind/solar hybrid systems (Aspliden, 1981; Katti & Khedkar, 2007; Gilau & Small, 2008; Mahmoudi et al., 2008; Reichling & Kulacki, 2008). The yearly variability of these two sources is illustrated in Fig. 7.8 (for Ersa, France). Combining wind and solar energy together in a hybrid power plant helps to smooth this variation. The decision whether or not to use both resources also depends on the load to be supplied, because the objective is to make production equal consumption. Actually, in most cases, it is desirable that the two sources are complementary. In this study, we quantify this 30 20 10 0

0

50

100

150

200

250

300

350

200

250

300

350

200

250

300

350

15000

Solar irradiation (W h m–2)

10000 5000 0 0

50

100

150

1000 800 600 400 200 0 0

50

100

150

7.8 Illustration of the variability of the renewable energy resources (wind speed, wind energy, solar irradiation) in Ersa (France).


224


complementarity firstly on a monthly basis, and secondly on an hourly basis in a monthly and yearly analysis using hourly data for solar radiation per unit of horizontal plane, and with wind velocity measured at 10 m above ground. We cannot quantitatively compare wind energy per unit of cross-sectional area (Eq. [7.2]) and solar energy per unit area of horizontal plane because the two unit areas do not refer to the same type of surface. Thus, at first, we observed the variation of the two renewable resources on a monthly basis: what we wanted to see was that, during a no-sunny month (in winter), wind energy was higher than during sunny periods. Two parameters were used: •

the correlation coefficient (CC) which quantifies the temporal simultaneity of solar and wind power: CC =

{⎡⎣∑

∑ ( yi i =1 N

N i =1

( yi − y ) ( xi − x )

}

N 2 2 − y ) ⎤ ⎡ ∑ i =1 ( xi − x ) ⎤ ⎦⎣ ⎦

12

7.6

•

where yi and xi are the ith monthly mean _ values _ of daily wind energy and daily solar irradiation values, and y and x are the respective wind energy and solar irradiation mean values; • the ratio between the two annual renewable energies: R=

Ewind Esolar

7.7

_ _ where E solar and E wind are the annual average daily solar irradiation per m2 and daily wind energy per m2. When CC is near 1, the two renewable sources vary in the same way. For a good complementarity, CC must be near −1. The spatial variability of the wind resource is much greater than the solar resource’s, thus: the higher R is, the more appreciable the renewable potential becomes. For example, the results for two Corsican sites are shown in Fig. 7.9: Ersa has a good renewable potential and the two resources are complementary; in Ajaccio, the solar resource is about the same but the wind potential is low and its monthly distribution is close to that of solar energy. It is important to see the behaviour of these two resources on a daily scale because it plays an important role in sizing storage. For each month, we computed the two previous parameters: CC where yi and xi are the ith monthly mean values of hourly wind energy and solar irradiation at hour i, and R is the ratio of the daily wind and solar energies. If R is used to demonstrate how wind energy can complete solar energy in terms of quantity, a new parameter is necessary to quantify the available renewable


8

225 Monthly mean value of daily wind energy per m2 (kW h m–2)

Monthly mean value of daily solar irradiation (kW h m–2)


25 Ajaccio CC = 0.693 R = 0.433 Ersa CC = –0.903 R = 2.992

7

20

Solar

6 5

15

4 Wind

3

10

2 5 1 0

0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

7.9 Monthly variation of solar and wind energy for two Corsican sites: Ajaccio and Ersa. 1200

9

WE Sliven (March) SE Sliven (March) WE Ersa (November) SE Ersa (November) Wind Speed Sliven (March) Wind Speed Ersa (November)

800

Sliven March CC = –0.612 R = 0.307 P = 289 984 W h2 m–4 Ersa November CC = +0.934 R = 9.127 P = 33 245 750 W h2 m–4

8 7 6 5

600 4 400

Wind speed (m s–1)

Renewable energy (W h m–2)

1000

3 2

200 1 0

0 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Hours

7.10 Two examples of wind and solar energy daily repartition (Ersa, France and Sliven, Bulgaria).

energy: the product of the two renewable energies (P). Figure 7.10 shows the monthly distribution of the two renewable resources for Sliven, Bulgaria (with good complementarity but low renewable potential), and Ersa, France (with bad complementarity but good potential). More specifically, in Fig.


226


7.10, R, P and CC values are demonstrated for two representative months for each of the two sites. It is impossible to make a general conclusion about the complementarity of the two renewable resources, because any conclusion depends on the site and on the period considered. Moreover, for a more complete assessment, the load curve (i.e. load distribution versus time) needs to be taken into account, and a positive correlation between electricity demand and availability of electricity generated from solar and wind power needs to be looked for. We need to know which energy system among a PV system alone, a wind energy system, or a combined PV–wind system, is the most suitable to supply the demand (from the national grid and/or from a remote load). The influence of the complementarity of these sources on the sizing of a wind–PV hybrid system is important and, sometimes, installing a hybrid PV–wind system will entail no benefit (see Section 7.6 below).

7.3

Design and configuration of a wind–photovoltaic (PV) hybrid energy system

The concept of a wind–PV hybrid energy system is shown in Fig. 7.11. In this general configuration, an engine generator has been added. The wind energy conversion system (WECS) and the PV system are operated in parallel in order to supply electrical power to the load, and the excess energy generated is supplied to batteries. For emergency cases, where wind/ solar generation and stored energy are not sufficient to supply the load, the

DC Load Control system PV array

Inverter

AC Load

Electrical interface

AC/DC battery charger Wind turbine

Engine generator

Battery bank

7.11 Diagram of PV–wind–engine generator hybrid system.



227

back-up engine generator operates and is used to charge the battery and/ or to supply directly to the load.

7.4

Modelling and simulation of a wind–photovoltaic (PV) hybrid energy system

7.4.1 PV system The following description is restricted to crystalline silicon solar modules which account for about 90% of the world’s PV production. Other technologies, such as amorphous silicon, CdTe, copper-indium-diselenide (CIS) and tandem cells have been developed, but their use in power systems is still limited. PV module performance is highly influenced by weather conditions, especially solar radiation and PV module temperature. The shortcircuit current Isc and the open-circuit voltage Voc are the two main parameters of the I–V curve (Fig. 7.12). Isc is almost proportional to solar irradiance and Voc increases slowly when solar irradiance increases. When the PV temperature increases, Voc leads to a decrease of the available maximum electrical power, in spite of a small increase of the short-circuit current Isc. The working point of a PV panel depends on the load characteristic; when the battery is connected, its voltage imposes PV voltage. Sometimes, a maximum power point tracker (MPPT) is used and is connected to the PV array to extract the maximum available power, whatever the solar irradiance is.

4.0

PMPP

Isc 3.5

IMPP

50

3.0

25 °C, G = 1000 W m Power

–2

40 Maximum power point MPP

2.0

30

1.5 20 1.0 10

0.5 VMPP

0 0

2

4

6

8

10

12

14

16

Voltage (V)

7.12 I–V and P–V curves of a PV module.


18

Voc 20

0 22

Power (W)

2.5 Current (A)

60

228


A PV array is constituted by Ns modules in series and Np modules in parallel; the total PV power is: PPV = Np × NS × Pmodule × ηMPPT × ηoth

7.8

where ηMPPT is the MPPT efficiency (generally around 95%) and ηoth represents other existing losses (cable resistance, imbalance, dust, etc.). Note that if no MPPT is used, i.e.ηMPPT = 1, another coefficient must be introduced in the equation to take into account that the PV module does not operate in MPP conditions. The PV power is estimated using both energy and electrical models. The simplest model expresses a direct relationship between the output power Pmodule and in-plane solar irradiance, using the PV module efficiency, and also being dependent on irradiance Gβ and cell temperature θcell: ηPV = ηPV,ref[1 − β1(θcell − θcell,ref) + γ1logGβ]

7.9

where ηPV,ref is the reference module efficiency at θcell,ref = 25 °C and at Gβ = 1000 W m−2, γ1 and β1 are the solar irradiance and temperature coefficients of the PV module, respectively, both depending on the material used. The parameters (θcell,ref; ηPV,ref; β1; γ1) are given by the manufacturer for the silicon (β1 = 0.0048 °C−1; γ1 = 0.12) (Evans, 1981) but often γ is taken as equal to 0 because the solar irradiance does not have a high influence on the PV efficiency (Habib et al., 1999; Deshmukh & Deshmukh, 2008). In Fig. 7.13, the influence of the cell temperature on the experimental efficiency of a 700 Wp BP585F monocrystalline silicon PV array is shown. 0.16 0.14

PV array efficiency

0.12 Cell temperatures

0.10

under 11 °C 13–15 °C 17–19 °C 21–23 °C 25–27 °C 29–31 °C 33–35 °C 37–39 °C 41–43 °C 45–47 °C over 49 °C

0.08 0.06 0.04

11–13 °C 15–17 °C 19–21 °C 23–25 °C 27–29 °C 31–33 °C 35–37 °C 39–41 °C 43–45 °C 47–49 °C

0.02 0 0

100

200

300

400

500

600

700

800

900

Solar irradiance (W m–2)

7.13 Influence of the cell temperature on m-Si PV array efficiency.


1000


Id1

Iph

Id2

Rs

Rsh

229

I

U

7.14 Equivalent circuit of a PV module (with one or two diodes).

The PV module can also be modelled using an electrical equivalent circuit (Fig. 7.14) consisting of a current source in parallel with one diode (Hecktheuer et al., 2002; Rosell & Ibanez, 2006; Karatepe et al., 2007), or with two diodes (Eicker, 2003; Priyanka et al., 2008). Although the second model is more accurate, the first is more often used in wind–PV systems modelling (Underwood et al., 2007; Soltani & Debbache, 2008) and is briefly presented here. The voltage–current equation is: q ⎡ (V + RS I )⎤⎥ ⎫⎪ V + RS I ⎪⎧ ⎢ ⎦ I = I L − I 0 ⎨exp ⎣ A0 KTcell − 1⎬ − Rsh ⎩⎪ ⎭⎪

7.10

where IL is the photocurrent, I0 the diode-saturation current, q the standard electron charge, A0 the diode ideality factor and K the Boltzmann constant. Rs and Rsh are the series and shunt resistances, respectively. Then, Pmodule = V × I and the maximum power point is found from (∂P/∂V) = 0. The characteristic equation of the PV module is a transcend equation which has no analytical solution. Assuming that an MPPT is used, the formulas for calculating the optimal operating point current and voltage under arbitrary conditions, and using easily available parameters, have the following forms (Lasnier et al., 1988; Borowy & Salameh, 1996; Ai et al., 2003): ⎧ ⎡ ⎛V ⎞ ⎤⎫ I MPP = I sc,ref ⎨1 − C1 ⎢exp ⎜ MPP,ref ⎟ − 1⎥ ⎬ + ΔII ⎝ C2Voc,ref ⎠ ⎦ ⎭ ⎩ ⎣

7.11

⎡ ⎛ Gβ ⎞ ⎤ VMPP = VMPP,ref ⎢1 + 0.0539 log 10 ⎜ + β0 ΔT ⎝ Gβ ,ref ⎟⎠ ⎥⎦ ⎣

7.12

VMPP,ref −1 I Voc,ref ⎛ ⎞ ⎡ V ⎤ C1 = ⎜ 1 − MPP,ref ⎟ exp ⎢ − MPP,ref ⎥ and C2 = ⎝ I I sc,ref ⎠ ⎛ ⎞ ⎣ C2Voc,ref ⎦ ln ⎜ 1 − MPP,ref ⎟ ⎝ I sc,ref ⎠

7.13


230

Stand-alone and hybrid wind energy systems ⎛ Gβ ⎞ ⎛ Gβ ⎞ ΔI = α 0 ⎜ ΔT + ⎜ − 1⎟ I sc,ref ⎟ ⎝ Gβ ,ref ⎠ ⎝ Gβ ,ref ⎠

7.14

ΔT = Tcell − Tcell,ref

7.15

with α0 and β0 being the module current and voltage temperature coefficients, respectively. The PV module power is calculated by multiplying the voltage by the current in MPP conditions. In all these models, the cell temperature θcell appears, influencing the I–V characteristics and the electrical efficiency of the PV module. The most common method to determine θcell consists of using the normal operating cell temperature (NOCT) (calculated for a wind speed v = 1 m s−1, an ambient temperature θa = 20 °C and a hemispherical irradiance Gβ = 800 W m−2) given by PV module manufacturers:

θ cell = θ a + ( NOCT − 20 °C )

Gβ 800

7.16

Other methods to determine the cell temperature are given by Jones & Underwood (2001) and Mattei et al. (2006). If a PV module is shaded, very dramatic effects occur on its power– voltage curve and, even if only a very small fraction of the module or cell is shaded, a very significant power reduction takes place. This partial shading can occur as a result of chimneys, trees, parts of other buildings, etc., and special attention should be given to this problem so as to avoid a high reduction of the PV system’s performance (Hecktheuer et al., 2002; Eicker, 2003; Karatepe et al., 2007). In Fig. 7.15, the influence of the partial shading of one cell on the I–V and P–V curves is shown. With only 50% of one cell shaded (out of the 36 serial cells), power is reduced by 25%, and with 100% of one cell shaded, the power falls by 57%. Special attention should be paid to this problem when designing a system.

7.4.2 Small and medium wind turbines Three items of data are essential to calculate the output power of a WECS: • • •

the power curve (joining aerodynamic, mechanical transmission and converting efficiencies) given by the manufacturer; the hourly data of wind speed for the installation site; the hub height.

Different WECS with the same rated power can generate, at the same site, very different amount of electrical energy because of the difference of the power curve (Notton et al., 2008). This influence is even more important when storage is present because it introduces a lag between production and


Hybrid wind–photovoltaic energy systems 5.0 Open symbols: power (right y-axis) Solid symbols: current (left y-axis)

4.5

25% one shaded cell 50% one shaded cell 75% one shaded cell One shaded cell Without shaded cell

Maximum power point

4.0

60

50

40

3.0 30

2.5 2.0

Power (W)

Current (A)

3.5

231

20

1.5 1.0

10

G = 810 W m–2

0.5

qcell = 40 °C

0

0 5

7

9

11

13

15

17

19

Voltage (V)

7.15 Some experimental I–V curves for BP585F PV module with various percentages of one shaded cell.

consumption. Thus, the sizing of a wind system is strongly influenced by the wind turbine’s power profile (Notton et al., 2001). An inventory of WECS from 0.2 to 20 kW available on the European market was undertaken. The form of the power curves vary greatly and the WECS output power can reach a value of 40% greater than PWECS,rated. Among the 59 power curves studied, eight types of WECS power profiles were selected (see Fig. 7.16; the electrical power P has been divided by the rated power PWECS,rated for a better comparison). Three models usually used in hybrid system sizing are also shown: the linear model, Pallabazzer model (Pallabazzer, 1995; Underwood et al., 2007) and Chang model (Chang & Tu, 2007). These models are not representative of small and medium WECS and, considering the importance of the power curves on system sizing, it is preferable to use real WECS power curves. To obtain the WECS output power, wind speed measured at 10 m must be first calculated at hub height, using Eq. [7.5] and then coupled to the WECS power curves.

7.4.3 Battery storage Lead–acid batteries are usually used for energy storage in hybrid systems to store surplus energy, to regulate system voltage and to supply load in case of insufficient solar radiation and/or wind. Only 2 or 3 days of autonomy is required for batteries in wind–PV hybrid systems, while 5 to 6 days


232


1.0

p = (P/Pnom)

0.8

0.6

0.4

0.2

0.0

Linear model Pallabazzer model Chang model 0

5

10 15 Wind speed (m s–1)

20

25

7.16 Eight types of WECS power curve (0.2–20 kW) and three wellknown models.

of autonomy are necessary in separate PV or wind systems (Muselli et al., 1999; Deshmukh & Deshmukh, 2008). Other storage means can be used but lead–acid batteries are a low-cost, maintenance-free and highly efficient technology. Battery behaviour modelling is very complex and various models are available (Zhou et al., 2008). Battery capacity depends on maximum depth of discharge (DOD), temperature and age. A battery’s state of charge (SOC) is generally expressed as a percentage, according to the following: during the charging process: SOC(t + 1) = SOC(t) · [1 − σ(t)] + [Ibat(t) · Δt · ηc(t)/Cbat]

7.17

during the discharge process: SOC(t + 1) = SOC(t) · [1 − σ(t)] − [Ibat(t) · Δt/ηdis(t)]/Cbat

7.18

with (1 − DOD) ≤ SOC(t) ≤ 1 where σ(t) is the hourly self-discharge rate depending on the battery state but often taken constant at about 0.02% (Yang et al., 2007). Cbat is the nominal capacity of the battery (A h). The charge efficiency ηc depends on the SOC and the charging current and has a value between 0.65 and 0.85 (Yang et al., 2007; Diaf et al., 2008a) and the discharge efficiency ηdis is generally taken equal to 1 (Ai et al., 2003; Diaf et al., 2008a). For high DOD, phenomena such as sulfatation, freezing or stratification occur in the battery and reduce the battery lifetime, thus generally DOD is taken between 50



233

and 80%. Ibat = P/Vbat(t) is calculated from an energy balance between the input power (wind + PV) and the output power (load) and depends on the hybrid system configuration. Vbat(t) can be calculated by simple or complex models (Ai et al., 2003; Yang et al., 2007; Zhou et al., 2008) and using parameters depending on SOC. In a first approximation, Vbat(t) can be taken as constant. The influence of the temperature on the rated capacity of the battery and the floating voltage is sometimes taken into account (Ai et al., 2003; Zhou et al., 2008; Diaf et al., 2008a).

7.4.4 DC/AC and AC/DC converters In a PV–wind hybrid system, several electrical converters can be used: • •

DC/AC converters or inverters to supply AC load (between charge regulator and load); AC/DC converters or rectifiers, after the wind turbine or engine generator.

Using inverters has three major disadvantages: a high cost depending on the quality of the output signal (square, pseudo-sinus or sinus); a decrease in the overall system performance (inverter efficiency depending on the load ratio and self-consumption); and a risk of failure. Modern electronic inverters are efficient over a wide range of outputs. If a stand-alone inverter performance reaches 87–95% at two-thirds of its rated capacity, its efficiency decreases sharply when the power supply falls below this value and can reach values under 50% at a very small load. An inverter requires some power just to run itself, so inverter efficiency will be low when running very low loads. In a typical home, there are many hours of the day when electrical load is very low. One solution consists of using as many inverters as AC loads to supply; thus, each converter has a higher performance, increasing the overall system reliability but with a significant increase of the system cost. The best solution in the opinion of many authors is the mixed system, in which the hybrid system is divided into two subsystems, a DC one for lighting, radio and television, for instance, and an AC one for other equipment; in this way any inverter will be turned on only when an AC load requires it. Consequently, it can be seen that sizing an inverter correctly/well for its required purpose is important (Tsagas, 2002): if it is undersized, there will not be enough power; demanding more than its limit will shut it off; if it is oversized, it will be much less efficient (due to standing losses) and more costly to buy and run. Moreover, some inverters operate without interruption even if no electrical charge is supplied, and thus have a significant self-consumption.


234


The choice of electrical signal (square, pseudo-sinus or sinus) produced by an inverter depends on the type of connected appliances, but the inverter price increases with the quality of the signal and its performance, and so can be increased up to four times for the same nominal power. In most publications, inverter efficiency is taken as a constant and equal to 90–95%, which is sometimes high compared with commercial data but, actually, varies with the load. When the load is fluctuating, it is desirable to use the inverter efficiency curve versus load power in any modelling. If the wind turbine has AC output, the use of a rectifier is necessary to charge the battery. If an auxiliary engine generator is used, a rectifier must be connected. The rectifier efficiency depends on the type of AC power, type of rectifying elements, type of rectifier and the percentage of load of the unit, as for the inverter. The AC/DC converter efficiency is generally taken to be some percentage points lower than the equivalent inverter efficiency. Wind turbine rectifier peak power is calculated from the WECS nominal power. For an auxiliary generator rectifier, its peak power is computed according to the maximum battery charge current rate, at around 20% of the battery’s nominal capacity (Sandia National Laboratories, 1995; Yang et al., 2003). Sometimes, when an engine generator is used, the inverter is replaced by an inverter-charger capable of converting DC from batteries to AC for the load, as well as converting AC from the generator to DC to charge batteries. Switching from one mode to another can be done manually or with an automatic transfer switch.

7.4.5 Auxiliary engine generator An auxiliary generator is used in case of a long lack of wind or sun. It can just charge the batteries, which is the usual case, or simultaneously charge the batteries and the AC load directly. The choice of a generator depends on the nature of the load. Generally, diesel generators are used because they are more economical but, on the other hand, it is difficult to find diesel generators with very low power and, in that case, gasoline generators can be used. A diesel generator’s running speed is a function of the expected running time; if it is used occasionally, to charge a battery for example (in the case of a hybrid system), a 3000 rpm unit may be sufficient; for more frequent use, a 1500 rpm is recommended. To determine the rated capacity of the engine generator, two cases are considered:


Hybrid wind–photovoltaic energy systems • •

235

if the generator is directly connected to the load, then the rated capacity must be at least equal to the maximum power demand; if it is used only as a battery charger, then (as stated in Section 7.4.4) the maximum battery charge current rate is around 20% of the battery nominal capacity, and the rated capacity of the generator is Cbat · Vbat/5ηrect.

In the first configuration, the generator has a rated capacity greater than in the second case and often runs at partial load even if it charges the battery simultaneously; moreover, it consumes more fuel. In the second configuration, the generator is chosen in such a way that it always runs at full load, i.e. with a higher efficiency. A linear relationship links the fuel consumption Qv to the produced power PEG (Fig. 7.17). The no-load fuel consumption for a small engine generator may be a high fraction of the full-load fuel consumption Q0v (25–40%) (Notton et al., 1996; Kaldellis, 2007). Thus, generator efficiency is higher at high load than at partial and low loads. ⎛ Qv ⎞ ⎛ PEG ⎞ ⎜⎝ 0 ⎟⎠ = a ⎜⎝ 0 ⎟⎠ + (1 − a) Qv PEG

7.19

1.3 1.2 1.1 1.0

Gasoline Diesel 3000 rpm Diesel 1500 rpm Gasoline engine generator Qv° (l/kW h) = 0.7368PGE−0.2954

0.9 0.8 0.7

Musgrove, 1988

0.6 0.5 0.4 0.3 0.2 0.1 0

Calloway, 1986

2

4

6

8

10

12

14

16

18

20

1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0

Qv/Q°v

Specific consumption at nominal power (l/kW h)

The specific fuel consumption Q0v depends on several parameters defining the quality of the engine. It varies greatly for low rated power ( (1 − DOD), and the SOC is calculated using Eq. [7.18]. If the energy stored in batteries is insufficient to satisfy the energy load requirements for hour t, there are two possibilities: 1

If no auxiliary fuel generator is connected, an energy deficit LPS(t) (load energy not provided during the time interval [t − 1,t]) is created: LPS(t) = EL(t) − [EPV(t) + ηrectEWT(t) + SOC(t − 1)Cbat − (1 − DOD)Cbat] · ηinv

2

7.23

If an auxiliary generator is connected, it is started and is stopped when the battery reaches a given SOC threshold (often 100%).

Two reliability concepts are used in sizing a hybrid system (Ai et al., 2003; Yang et al., 2007): •

The loss of load probability (LLP or LOLP): a temporal concept defined as the power failure time period Tf divided by the total working time of the hybrid system T; LLP = Tf/T

•

7.24

the loss of power supply probability (LPSP): an energy concept defined as the ratio of power deficit LPS(t) to the sum of the load demand EL(t) during the same period: T

LPSP = ∑ LPS ( t ) t =1

T

∑E

L

(t )

7.25

i =1

An LLPS or LLP equal to zero means that the load is always satisfied; an LLPS or LLP equal to 1 means that the load is never supplied. In case 2 above, LLPS is always zero because, in case of system failure, the auxiliary generator is started; only the running time and fuel consumption increase. A flowchart synthesising the methodology and various input and output parameters is shown in Fig. 7.20.


Hybrid wind–photovoltaic energy systems Load power profile AC and/or DC

PV array efficiency

Hourly meteorological data solar irradiation, wind speed (10 m) temperature

Components characteristics

Wind turbine Power curve

Battery type

241

Auxiliary generator specific fuel consumption

Electrical converter efficiency curve

Wind speed at hub height

AC/DC rectifier size (current limitation)

PV array

Wind turbine

Storage

Engine

peak power

peak power

capacity

nominal power

AC/DC

DC/AC

nominal power

nominal power

DC/AC inverter size Hub height

Wind/PV hybrid system behaviour simulation PV energy

Engine generator start/stop threshold

Wind turbine energy Generator energy

Battery DOD

Renewable fraction Fuel consumption Running time

Excess energy LLP/LLPS if no engine generator

Start/stop number

7.20 Synthesis of the methodology and input and output data.

7.5.2 Excess energy, solar fraction and gross production Wasted or excess energy EEXC is energy not produced by a renewable energy converter (PV modules and wind turbine) because battery capacity is at its highest level (SOC = 1) and the load does not require all power produced. This excess energy can also have been produced and dissipated in a resistance or sent towards another load but not used for the main load. Often, a dimensionless parameter is used by dividing the excess energy by the total produced energy, and thus allowing that part of the energy produced but not used by the system to be known (as a fraction or percentage). The performance of renewable energy systems can be characterized by a renewable energy sources fraction (RESF), often called solar fraction because wind energy comes from the sun, which is the fraction of energy that is produced orderived from a renewable source (RESF = 1 if no fuel engine is used). Generally, excess energy is not taken into account when calculating RESF, defined by: RESF =

EPV + EWT − EEXC EPV + EWT − EEXC + EAUX


7.26

242


with EAUX being auxiliary generator energy (this parameter only being used when an auxiliary generator exists). Another parameter characterizing system performance is gross production, PRG, originating from a solar source (PV + wind) in units of load energy (EL), and defined by: PRG =

EPV + EWT EL

7.27

7.5.3 Sizing optimization Optimum sizing is generally calculated on an hourly basis, to take the temporal distribution of the load and the energy sources into account. To meet an energy load with a given LLPS value, several PV–wind hybrid system configurations are possible. Some configurations are not technically feasible (storage too large, incompatibility between generator power and storage size, etc.). The optimized wind–PV system under investigation must meet the energy demand with minimum costs, with minimum fuel use or green gas emissions, or with the minimum energy pay-back period (a comparison of produced energy with the energy content of the entire system). Nema et al. (2009) write that various optimization techniques are used in the literature to compute the optimal design of hybrid systems, such as linear programming, a probabilistic approach, iterative techniques, dynamic programming and multi-objective genetic algorithms. The assessment of wind/PV system costs using annualized life-cycle costing methods is an important step in system sizing; indeed, it is often the main criterion of optimization used in the literature. Using well-costed optimized systems increases the economic attractiveness of such systems and their acceptance by users.

7.6

Wind–photovoltaic (PV) hybrid energy system: case studies

Figure 7.21 presents a household with an electrical AC load. The PV–wind hybrid systems (without an auxiliary generator) are located at distant sites about 130 km away: in Ajaccio (a non-windy site with an annual average wind speed of 3.5 m s−1), and in Ersa (a windy site, with an annual average wind speed of 7.11 m s−1). The renewable energy potential of these two sites is shown in Fig. 7.9; the solar potential is almost equal but the wind potential is very different. The PV–wind hybrid system was sized to satisfy the load with an LLPS equal to zero, i.e. all the load was satisfied without interruption. For each configuration (PV array peak power, wind turbine rated power, battery



243

800

Hourly load energy (W h)

700

Spring–autumn (3.844 kW h per day) Summer (3.436 kW h per day) Winter (4.230 kW h per day)

600 500 400 300 200 100 0

0

4

8

12 Time (hour)

16

20

24

7.21 House electrical load.

capacity) the electricity generation cost CkW h (C/kW h) was computed using the assumptions presented in Section 7.4.6. Figure 7.22 shows the PV subsystem and wind turbine subsystem sizes (on the left axis) and corresponding levelized kW h costs (on the right axis), for various batteries with different storage sizes (from 2 to 5 days of storage). For Ajaccio, a non-windy site, the ‘best’ configuration found is for 3 days’ storage, while for Ersa, the ‘best’ configuration is for only 2 days’ storage. The optimal configurations for a PV–wind hybrid system, for PV alone and for wind alone, are given in Table 7.1. Battery size decreases when a hybrid system is used, wherever the site is, and it is one of the main advantages of using hybrid systems because the battery bank represents a high percentage of total levelized costs. Kaldellis et al. (2006) obtained the same conclusions showing that the introduction of PV panels into a wind energy system considerably reduces the complete installation dimensions, and decreases the corresponding operational costs owing to the significant battery capacity reduction imposed. A PV–wind hybrid system is very suitable for Ersa compared with the two other systems, and the kW h cost is reduced by 35%. For Ajaccio, a PV system alone is more suitable because the wind potential at that site is not sufficient for the addition of a wind turbine, which would not provide any benefit to the profitability of the production system but would result in an increase in the system’s complexity.


244


4000

2 days’ storage

3 days’ storage

4 days’ storage

5 days’ storage

2.3

3500 Ajaccio

2.1

1.7

2000 1500

1.5

1000

1.3

500 0

4500

500

1000

2 days’ storage

4000

1500 2000 2500 Wind turbine power (W) 3 days’ storage

3000

4 days’ storage

3500

5 days’ storage

Open symbols: kW h cost (right y-axis) Solid symbols: PV peak power (left y-axis) Ersa

3500 PV peak power (Wp)

1.1

Open symbols : kW h cost (right y-axis) Solid symbols : PV peak power (left y-axis) 0

kW h cost (€/kW h)

1.9

2500

0.9 4000

2.3 2.1

3000

1.9

2500

1.7

2000

1.5

1500

kW h cost (€/kW h)

PV peak power (Wp)

3000

1.3 1000 1.1

500 0

0

50

1000

1500 2000 2500 3000 Wind turbine power (W)

3500

0.9 4000

7.22 System configurations and energy levelized cost for LLPS = 0.

For the optimal configurations of the hybrid system, the SOC evolution over a year is plotted in Fig. 7.23. Even though the minimum SOC reached by the two systems is the same, the SOC variation differs from one site to another. As Ai et al. (2003) have said, using a PV–wind hybrid system increases the batteries’ lifetime remarkably compared with the utilization of either a wind system or a PV system alone, because the charge regime is more appropriate; prolonging the battery lifetime reduces the electricity production costs.



245

Table 7.1 Optimal sizing for PV–wind hybrid system, PV alone and wind alone systems in Ajaccio and Ersa Site

Battery storage size (days)

PV–wind hybrid system Ajaccio 3 Ersa 2

PV peak power (W)


Levelized kW h cost (C kW h−1)

2350 850

200 600

1.54 0.92

Wind alone system Ajaccio Ersa

6 5

0 0

4600 1200

2.54 1.40

PV alone system Ajaccio Ersa

3 3

2450 2050

0 0

1.52 1.38

The monthly values of PV energy, wind turbine energy, excess energy and load energy are plotted in Fig. 7.24. If the annual energy produced at each site with the corresponding optimized PV–wind hybrid system are quite similar, the monthly distribution of these energies differs greatly. The monthly variation is more pronounced for Ajaccio than for Ersa due to a better complementarity of the two renewable resources in Ersa, as seen in Section 7.2.3. Thus, the profitability of such a hybrid system is linked to a great extent to the characteristics of the solar and wind resources. The annual excess energy represents about 55% of total electrical renewable energy for the two cases studies but its monthly distribution changes: for Ajaccio, between 23% in December to 70% in July; for Ersa, between 44% in December to 61% in June. Gross production (PRG) varies between 1.48 to 3.98 for Ajaccio and between 2.15 to 3.02 for Ersa depending on the month, with a yearly average around 2.5–2.7. Thus, on a yearly basis, an optimized PV–wind hybrid system produces 2.5 times more energy than the load energy and about 55% of the energy produced by both the wind turbine and the PV modules is lost, owing to the necessity to reach an LLPS equal to zero (giving total autonomy). This important element of excess energy is linked to the utilization of two random power sources and the necessity to satisfy the load at all times. It has been shown that increasing the LLPS greatly reduces the excess energy (Notton et al., 1996, 2001), improving the system performances. Adding an auxiliary source as an engine generator allows total autonomy to be reached without increasing the energy excess and with a reduction in size of the other components, which in turn induces a reduction of the kW h cost.


246


1.0

Hourly state of charge

0.9 0.8 Ajaccio

0.7 0.6 0.5 0.4 0.3

DOD 0

876

1752

2628

3504

4380 Hours

5256

6132

7008

7884

8760

5256

6132

7008

7884

8760

1.0

Hourly state of charge

0.9 0.8 Ersa

0.7 0.6 0.5 0.4 0.3

0

876

1752

2628

3504

4380 Hours

7.23 Hourly variation of battery SOC for optimal hybrid system configuration.

These results demonstrate the importance of a thorough preliminary study of the potential for solar and wind and their complementarity; the characteristics of renewable resources (both their energies and temporal distribution) strongly influence the sizing, the SOC and the distribution of energy.



247

450 Ajaccio

400

Ersa

Monthly energy (kW h)

350

Epv Ewt Etot Eexc Eload

300 250 200 150 100 50 0 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 Month

7.24 Monthly energy balance for the optimal PV–wind hybrid system in Ajaccio and Ersa.

7.7

Future trends

Various research projects and improvements concerning PV–wind hybrid systems are already under way or must be realized soon, including the following: •

New controlling methods using artificial intelligence and other expert systems to manage energy flux control. These advanced methods will improve the performances of such systems and the quality of the electricity provided. • New battery technologies, which are more reliable and have reduced maintenance needs, have been developed for other applications and are being implemented in hybrid energy systems. Other storage means are also in development, such as the promising fuel cells, flywheels or hydropumps for large energy systems. • The reduction of the kW h cost, which can be achieved by decreasing the manufacturing cost of various components (particularly of PV cells), or by the development of improved optimization procedures in the design of wind–PV systems. • The design and development of software tools for pre-feasibility, sizing, simulation and open architecture research, specifically for application to hybrid wind/PV systems.


248


7.8

Conclusions

Using a PV–wind energy system substantially reduces the size of the production subsystem (wind turbine or PV modules) and of the storage required, compared with a single source only system, provided that the wind and solar potential are suitable. Consequently, a hybrid energy system can significantly reduce the total life-cycle costs and make the utilization of renewable sources more profitable compared with conventional electrical systems. The wind resource is more site-dependent than the solar resource, while solar potential can be considered identical over a larger area. Moreover, wind is more unpredictable than direct sunshine and its variation more important. If demand increases at a particular site, it will be more practical to add new PV modules than another wind turbine because installing a new wind generator needs a new mast and often a new converter while adding new PV modules can be realized without important modifications to the system design.

7.9

References

Ai, B, Yang, H, Shen, H, Liao, X (2003), ‘Computer-aided design of PV/wind hybrid system’, Renew Energy, 28, 1491–1512. Aspliden, C (1981), ‘Hybrid solar-wind energy conversion systems meteorological aspects’, Report no. PNL-SA-10063. Richland: Pacific Northwest Laboratory. Bagiorgas, HS, Assimakopoulos, MN, Theoharopoulos, D, Matthopoulos, D, Mihalakakou, GK (2007), ‘Electricity generation using wind energy conversion systems in the area of Western Greece’, Energy Convers Mngt, 48, 1640– 1655. Borowy, BS, Salameh, ZM (1996), ‘Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system’, IEEE Transactions on Energy Conversion, 11–2, 367–375. Calloway, TM (1986), ‘Design of an intermediate-sized autonomous photovoltaicdiesel power plant’, Sandia Report, SAND85-2136. Celik, AN (2003), ‘Energy output estimation for small-scale wind power generators using Weibull-representative wind data’, J Wind Eng Ind Aerodynamics, 91, 693–707. Chang, TJ, Tu, YL (2007), ‘Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: a case study of Taiwan’, Renew Energy, 32, 1999–2010. De L.Musgrove, AR (1988), ‘The optimization of hybrid energy conversion systems using the dynamic programming model RAPSODY’, Int J Energy Res, 12, 447–457. Deshmukh, MK, Deshmukh, SS (2008), ‘Modeling of hybrid renewable energy systems’, Renew Sustain Energy Rev, 12, 235–249.



249

Diaf, S, Notton, G, Belhamel, M, Haddadi, M, Louche, A (2008a), ‘Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions’, Appl Energy, 85, 968–987. Diaf, S, Belhamel, M, Haddadi, M, Louche, A (2008b), ‘Technical and economic assessment of hybrid photovoltaic/wind system with battery storage in Corsica island’, Energy Policy, 36, 743–754. Duffie, JA, Beckman, WA (2006), Solar Engineering of Thermal Processes, Wiley, 3th edition. Eicker, U (2003), Solar Technologies for Buildings, Wiley. Elamouri, M, Ben Amar, F (2008), ‘Wind energy potential in Tunisia’, Renew Energy, 33, 758–768. Evans, DL (1981), ‘Simplified method for predicting photovoltaic array output’, Solar Energy, 27(6), 555–560. Gilau, AM, Small, MJ (2008), ‘Designing cost-effective seawater reverse osmosis system under optimal energy options’, Renew Energy, 33, 617–630. Habib, MA, Said, SAM, El-Hadidy, MA, Al-Zaharna, I (1999), ‘Optimization procedure of a hybrid photovoltaic wind energy system’, Energy, 24, 919– 929. Hecktheuer, LA, Krenzinger, A, Prieb, CWM (2002), ‘Methodology for photovoltaic modules characterization and shading effects analysis’, J Braz Soc Mech Sci, 24–1, 126–132. Iqbal, M (1983), An Introduction to Solar Radiation, Academic Press Inc. INES (Institut National de l’Energie Solaire), 2009, http://www.ines-solaire.com [accessed January 2009]. Jebaraj, S, Iniyan, S (2006), ‘A review of energy models’, Renew Sustain Energy Rev, 10, 281–311. Jones, AD, Underwood, CP (2001), ‘A thermal model for photovoltaic systems’, Solar Energy, 70, 349–359. Justus, CG, Hargraves, WR, Yalcin, A (1976), ‘Nationwide assessments of potential output from wind powered generators’, J Appl Meteorol, 15, 673–678. Kaldellis, JK (2007), ‘An integrated model for performance simulation of hybrid wind–diesel systems’, Renew Energy, 32, 1544–1564. Kaldellis, JK (2008), ‘The wind potential on the maximum wind energy penetration in autonomous electrical grids’, Renew Energy, 33–7, 1665–1677. Kaldellis, JK, Kavadias, KA (2007), ‘Cost–benefit analysis of remote hybrid winddiesel power stations: case study Aegean Sea islands’, Energy Policy, 35, 1525–1538. Kaldellis, JK, Kostas, P, Filios, A (2006), ‘Minimization of the energy storage requirements of a stand-alone wind power installation by means of photovoltaic panels’, Wind Energy, 9, 383–397. Kaldellis, JK, Spyropoulos, G, Kavadias, K (2007), Computational Applications of Soft Energy Resources: Solar potential-photovoltaic applications–Solar heat systems, 1st ed. a Stamoulis. Karatepe, E, Boztepe, M, Colak, M (2007), ‘Development of a suitable model for characterizing photovoltaic arrays with shaded solar cells’, Solar Energy, 81, 977–992. Katti, PK, Khedkar, MK (2007), ‘Alternative energy facilities based on site matching and generation unit sizing for remote area power supply’, Renew Energy, 32, 1346–1362.


250


Lasnier, F, Ang, TG, Lwin, KS (1988), Solar Photovoltaic Handbook, Energy Technology Division, Asian Institute of Technology. Mahmoudi, H, Abdul-Wahab, SA, Goosen, MFA, Sablani, SS, Perret, J, Ouagued, A, Spahis, N (2008), ‘Weather data and analysis of hybrid photovoltaic–wind power generation systems adapted to a seawater greenhouse desalination unit designed for arid coastal countries’, Desalination, 222, 119–127. Mattei, M, Notton, G, Cristofari, C, Muselli, M, Poggi, P (2006), ‘Calculation of the polycrystalline PV module temperature using a simple method of energy balance’, Renew Energy, 31, 553–567. Muselli, M, Notton, G, Louche, A (1999), Design of hybrid-photovoltaic power generator with optimization of energy management. Solar Energy, 65–3, 143–157. NASA, 2009, Atmospheric Science Data Center, http://eosweb.larc.nasa.gov/ [accessed January 2009]. Nema, P, Nema, RK, Rangnekar, S (2009), ‘A current and future state of art development of hybrid energy system using wind and PV solar: a review’, Renew Sustain Energy Rev, 13–8, 2096–2103. Ngala, GM, Alkali, B, Aji, MA (2007), ‘Viability of wind energy as a power generation source in Maiduguri, Borno state, Nigeria’, Renew Energy, 32, 2242– 2246. Notton, G, Muselli, M, Louche, A (1996), ‘Autonomous hybrid photovoltaic power plant using a back-up generator: a case study in a Mediterranean island’, Renew Energy, 7–4, 371–391. Notton, G, Muselli, M, Poggi, P (1998), ‘Costing of a stand-alone photovoltaic system’, Energy, 23–4, 289–308. Notton, G, Muselli, M, Poggi, P, Louche, A (2001), ‘Decentralized wind energy systems providing small electrical loads in remote area’, Int J Energy Res, 25, 141–164. Notton, G, Poggi, P, Cristofari, C (2006), ‘Predicting hourly solar irradiations on inclined surfaces based on the horizontal measurements: performances of the association of well-known mathematical models’, Energy Convers Mgmt, 47, 13–14, 1816–1829. Notton, G, Lazarov, V, Stoyanov, L (2008), ‘Study of small scale wind turbine productivity according to wind speed distributions and power curves’, Proceeding of World Renewable Energy Congress X, 19–25 July 2008, Glasgow, Scotland, 2259–2264. NREL (National Renewable Energy Laboratory) (2009), Renewable resource data center, http://www.nrel.gov/rredc/ [accessed January 2009]. Ozdamar, A, Ozbalta, N, Akin, A, Yildirim, ED (2005), ‘An application of a combined wind and solar energy system in Izmir’, Renew Sustain Energy Rev, 9, 624–637. Pallabazzer, R (1995), ‘Evaluation of wind-generator potentiality’, Solar Energy, 55, 49–59. Priyanka, S, Singh, SN, Lal, M, Husain, M (2008), ‘Temperature dependence of I–V characteristics and performance parameters of silicon solar cell’, Sol Energy Mat Sol Cells, 92, 1611–1616. Reichling, JP, Kulacki, FA (2008), ‘Utility scale hybrid wind–solar thermal electrical generation: a case study for Minnesota’, Energy, 33, 626–638.



251

Rosell, JI, Ibanez, M (2006), ‘Modelling power output in photovoltaic modules for outdoor operating conditions’, Energy Conv Mngt, 47, 2424–2430. Sandia National Laboratories (1995), Stand-alone Photovoltaic Systems: a Handbook of Recommended Design Practices, Sandia National Laboratories Report SAND87–7023, Albuquerque, New Mexico. Soltani, F, Debbache, N (2008), ‘Integration of converter losses in the modelling of hybrid photovoltaic–wind generating system’, Europ J Sci Res, 21–4, 707–718. Tsagas, I (2002), ‘Laboratory evaluation of DC/AC inverters for stand-alone and grid-connected photovoltaic systems’. MSc’s Dissertation, University of Strathclyde, Energy Systems Research Unit. UMass Lowell (2009), Solar Energy Engineering, Solar Irradiation Database, http:// energy.caeds.eng.uml.edu/fpdb/Irrdata.asp [accessed January 2009]. Underwood, CP, Ramachandran, J, Giddings, RD, Alwan, Z (2007), ‘Renewableenergy clusters for remote communities’, Appl Energy, 84, 579–598. Yang, HX, Lu, L, Burnett, J (2003), ‘Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong’, Renew Energy, 28, 1813–1824. Yang, H, Liu, L, Zhou, W (2007), ‘A novel optimization model for hybrid solarwind power generation system’, Solar Energy, 81, 76–84. Zhou, W, Yang, H, Fang, Z (2008), ‘Battery behavior prediction and battery working states analysis of a hybrid solar-wind power generation system’, Renew Energy, 33, 1413–1423. Zoumakis, NM (1993), ‘The dependence of the power-law exponent on surface roughness and stability in a neutrally and stably stratified surface boundary layer’, Atmosphera, 6, 79–83.

7.10 A A0 CC Cbat C1, C2 CkWh CO&M Ctot,syst EAUX EEXC Eprod E_wind E_wind Esolar EPV EWT

Nomenclature scale parameter in the Weibull distribution diode ideality factor correlation coefficient battery capacity coefficient used in the MPP PV power model electricity generation cost operation and maintenance cost total cost of the hybrid system hourly auxiliary generator energy hourly energy surplus total electrical production wind energy per unit of cross-sectional area annual wind energy annual solar energy hourly photovoltaic energy hourly wind turbine energy


m s−1

Ah

C kW h−1 C C Wh Wh kW h W h m−2 W h m−2 W h m−2 Wh Wh

252


EL Gβ Gb,β Gr,β

hourly load energy total solar radiation on a β tilted plane beam solar radiation on a β tilted plane ground reflected solar radiation on a β tilted plane sky diffuse solar radiation on a β tilted plane current battery current photocurrent diode-saturation current current at maximum power point short-circuit current Boltzmann constant hybrid system lifetime loss of load probability (also called LOLP) (no dimension) power deficit loss of power supply probability number of analysed data number of PV modules in series number of PV modules in parallel power of the engine generator rated power of the engine generator PV module power total PV power engine generator fuel consumption engine generator fuel consumption at rated power serial resistance shunt resistance battery state of charge PV cell temperature power failure time period total working time of the hybrid system voltage battery voltage open-circuit voltage present value of i value of an expenditure i the year j annual increase of the cost electricity price escalation rate interest rate

Gd,β I Ibat IL I0 IMPP Isc K L LLP LPS LPSP N Ns Np PEG P0EG Pmodule PPV Qv Q0v Rs Rsh SOC Tcell Tf T V Vbat Voc Xi,0 Xi,j gi gelec i


Wh W m−2 W m−2 W m−2 W m−2 A A A A A A 1.38 × 10−23 J K−1 year

Wh

W W W W l kW h−1 l kW h−1 Ω Ω % Κ h h V V V C C

Hybrid wind–photovoltaic energy systems k q v v0 xi, yi _ _ x, y z z0 α α0 β β0 β1 Δt ηc ηdis ηinv ηPV ηMPPT ηoth ηrect φ γ1 ρa σ θa θcell ref

shape parameter in the Weibull distribution standard electron charge wind speed reference wind speed at the height z ith value mean values of x and y hub height reference height exponent depending on topography PV module current coefficient inclination angle PV module voltage coefficient PV module temperature coefficient time interval battery charge efficiency battery discharge efficiency inverter efficiency PV module efficiency maximum power point tracker efficiency other losses efficiency rectifier efficiency latitude PV module solar irradiance coefficient air density hourly battery self-discharge rate ambient temperature PV cell temperature in reference conditions


1.6 × 10−19 C m s−1 m s−1

m m A K−1 ° V K−1 °C−1 s

° kg m−3 °C °C

253

8 Hybrid wind–hydrogen energy systems T. TSOUTSOS, Technical University of Crete, Greece

Abstract: Hydrogen can be produced from wind-generated electricity by various methods including both grid-independent and grid-assisted wind–hydrogen generation, wind power for grid-electricity and hydrogen generation, an integrated wind–hydrogen utility energy system, and a grid-independent integrated wind–hydrogen energy system. The future environmental implications of a potential large-scale wind–hydrogen economy will depend on how much hydrogen we use, how fast our use increases, the amount of fossil fuel emissions that can be saved, and the steps we take to control hydrogen emissions. Key words: wind–hydrogen, hybrid systems, fuel cells, environmental impact assessment.

8.1

Introduction

There have been several studies on the cost of using renewable energy for electrolysis. However, there is a potential to generate relatively inexpensive hydrogen from wind energy. Wind power may be produced at a very low cost in the regions with enough wind resources. It can be used to generate hydrogen on both small and large scales. This chapter presents the most common electrolysis technologies (alkaline, proton exchange membrane, solid oxide electrolysis cells) and the process of electrochemical electricity generation (using fuel cells). Critical issues concerning hybrid wind–hydrogen systems and their typical applications are presented, and different designs for hydrogen storage systems are discussed (including liquid hydrogen systems, metal hydride storage, novel hydrogen storage methods). The main steps in the design of isolated wind–hydrogen systems are outlined before moving on to discuss a case study on the Greek island of Karpathos. A general environmental impact assessment follows (including discussion of future emissions, European levels and potential overall environmental impacts), together with a discussion of hydrogen safety. An assessment of market potential and general barriers for wind–hydrogen systems is presented before, finally, a discussion of technology developments and future trends. 254 © Woodhead Publishing Limited, 2010

Hybrid wind–hydrogen energy systems

8.2

255

Design of wind electrolysis production systems

8.2.1 Introduction Water splitting in its simplest form uses an electrical current passing through two electrodes to break water into H2 and O2. Commercial low temperature electrolysers have system efficiencies of 56–73% (70.1–53.4 kW h/kg H2 at 1 atm and 25 °C) (Holladay et al., 2009). The most common electrolysis technology is alkaline based, but proton exchange membrane (PEM) electrolysis and solid oxide electrolysis cells (SOEC) units are now under development. Although the least developed, SOEC electrolysers are the most electrically efficient but still face challenges with corrosion, seals, thermal cycling and chrome migration. PEM electrolysers have none of the corrosion and seal issues that SOEC face, and are more efficient (though more costly) than alkaline systems. Alkaline systems are the most developed, lowest in procurement cost, but also the least efficient. Wind power may be produced at a very low cost in those regions with enough wind resources. It can be used to generate hydrogen on a small or a large scale (Sherif et al., 2005). Alkaline electrolysers Alkaline electrolysers are typically composed of electrodes, a microporous separator and an aqueous alkaline electrolyte of approximately 30 wt% KOH or NaOH. In alkaline electrolysers, the most common cathode material is Ni, with a catalytic coating such as Pt. For the anode, Ni or Cu, coated with metal oxides such as Mn, W or Ru, are used. In an alkaline cell, water is introduced to the cathode where it decomposes into hydrogen and OH− (NAS, 2004). PEM electrolyser PEM-based electrolysers typically use Pt black, Ir, Ru and Rh for electrode catalysts and a Nafion membrane which not only separates the electrodes, but also acts as a gas separator (NAS, 2004). SOEC SOECs partially replace the electrical energy required to split water with thermal energy (Holladay et al., 2009). Higher temperatures increase the electrolyser efficiency by decreasing the anode and cathode overpotentials, which cause power loss during electrolysis.


256


8.2.2 Electrochemical electricity generation (fuel cells) Fuel cells (FCs) are amongst the most promising hydrogen technologies. In an FC, H2 combines with O2 without combustion in an electrochemical reaction (the reverse of electrolysis) and produces direct current (DC) electricity. There are several types of FCs, depending on the type of electrolyte used (Larminie and Dicks, 2003); •

Alkaline fuel cells (AFC) use 85 wt% KOH as the electrolyte for high temperature operation (250 °C) and 35–50 wt% for lower temperature operation (30 000 m3, 12–16 bars). This range of applications and storage systems could similarly be used for hydrogen storage. Pressurized hydrogen tanks, made of ultra-light but strong new composite materials that allow pressures in excess of 200 bars, have been developed and used in prototype automobiles and buses. A storage density of more than 0.05 kg of H2 per 1 kg of total weight is easily achievable (Mitlitsky, 1996).

8.3.2 Liquid hydrogen storage Hydrogen liquefaction is an energy-intensive process. It requires amounts of energy equal to about one-third of the energy in the liquefied hydrogen. Hydrogen liquefaction and use of liquid hydrogen are usually practised only when achieving high storage density is absolutely essential, such as in aerospace applications. Some prototype hydrogen-powered automobiles, as well as commercially available automobiles, also use specially developed liquid hydrogen tanks (Braess and Strobl, 1996).

8.3.3 Metal hydride storage Hydrogen can form metal hydrides with some metals and alloys. During the formation of the metal hydride, hydrogen molecules are split and hydrogen atoms are inserted in spaces inside the lattice of suitable metals and/or alloys. In this way, effective storage comparable to the density of liquid hydrogen is created. However, when the mass of the metal or alloy is taken into account, the metal hydride gravimetric storage density is comparable to the storage of pressurized hydrogen. The best achievable gravimetric storage density is about 0.07 kg of H2/kg of metal for a high temperature hydride such as MgH2. During the storage process, heat is released, which must be removed in order to allow the continuity of the reaction. During the hydrogen release process, heat must be supplied to the storage tank. One advantage of storing hydrogen in hydriding substances is the safety aspect: serious damage to a hydride tank (e.g. a collision) would not pose a fire hazard, since hydrogen would remain in the metal structure. Table 8.2 lists some hydriding substances suitable for use as hydrogen storage media, while Table 8.3 provides a list of hydrogen storage types and densities.


262


Table 8.2 Hydriding substances as hydrogen storage media Medium

Hydrogen content (kg/kg)

Hydrogen storage capacity (kg/l)

Energy density (kJ/kg)

Energy density (kJ/l)

MgH2 Mg2NiH4 VH2 FeTiH1.95 TiFe0.7Mn0.2H1.9 LaNi5H7.0 RENi5H6.5 Liquid H2 Gaseous H2 (100 bar) Gasoline

0.070 0.0316 0.0207 0.0175 0.0172 0.0137 0.0135 1.00 1.00

0.101 0.081

9 933 4 484 3 831 2 483 2 440 1 944 1 915 141 900 141 900 47 300

14 330 11 494

0.096 0.090 0.089 0.090 0.071 0.0083

13 620 12 770 12 630 12 770 10 075 1 170 35 500

Table 8.3 Hydrogen storage types and densities

Large volume storage (102–104 m3) Underground storage Pressure gas storage (above ground) Metal hydride Liquid H2 Stationary small storage ( Δt

Q* = Q

No

No

No ≥ NFIN

Yes (No−Q*) curve

No

Mf ≥ MfFIN Yes End

14.11 Wind–diesel algorithm.


498

Stand-alone and hybrid wind energy systems Start Nin, Qin, Npvin, δN, δQ, δNpv, Δt, δt, NFIN, QFIN, NpvFIN Npv = Npvin

Npv = Npv + δNpv

No = Nin No = No + δN Yes Q = Q + δQ

Q = Qin No

Q ≥ QFIN

t= 0 t = t + δt Remote consumer energy demand ND(t)

Meteorological data W/T and PV power curves NW = NW(t) and NSOl = NSOl(t) ∑NRES

ND

∑NRES > ND

Energy storage

No

Yes

Yes

Yes

No

ΔN = ∑NRES-ND To low priority loads

∑NRES = 0

ΔN = ND-∑NRES Yes

Battery full? Yes No

No

No ND is covered by ΔN is covered by battery via charge battery via charge controller and inverter controller and inverter

Energy is stored to the battery via rectifier and charge controller

t > Δt

Battery empty?

Battery empty?

Yes Q* = Q

No No

No ≥ NFIN Yes (No-Q*) curve

No

Npv ≥ NpvFIN Yes End

14.12 Wind-PV algorithm.


Integration of stand-alone and hybrid WESs into buildings 4000

Winter consumption Summer consumption

3500 Load demand (W)

499

3000 2500 2000 1500 1000 500 0

0

24

48

72 96 Time (hours)

120

144

168

14.13 Typical electricity profile of the autonomous building analyzed.

Energy production (W h) Energy consumption (W h) Battery capacity (A h)

20 000 18 000

20 200

16 000

Energy (W h)

19 800

12 000 19 600 10 000 19 400 8000 19 200 6000


20 000

14 000

19 000

4000

18 800

2000

18 600

0 1

8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141 148 155 162

Hours (h)

14.14 Wind hybrid stand-alone system main parameters evolution, low wind potential area, winter time.

battery capacity diminution during the long-lasting calm spells, although large in absolute terms, does not lead to a significant depth-of-discharge increase of the storage system. Figure 14.15 presents the corresponding wind turbine rated power and battery bank capacity combinations that guarantee 1-year energy autonomy without any external energy input for various wind potential classes. The mean annual wind speeds for the specific areas are: for Andros 9.5 m/s; for


500


100 000 Andros Island Naxos Island Skiros Island Kithnos Island Kea Island


90 000 80 000 70 000 60 000 50 000 40 000 30 000 20 000 10 000 0 0

2000

4000

6000

8000 10 000 12 000 14 000 16 000 18 000 20 000


14.15 Comparison of wind stand-alone system configurations for selected areas.

Naxos 7 m/s; for Skiros 6.5 m/s; for Kithnos 6 m/s; and for Kea 5 m/s (Kaldellis, 2004). According to the results obtained, there is a significant battery capacity reduction as the wind turbine rated power increases. This increase is more abrupt for the high wind potential areas, while the medium wind potential areas present milder distribution. Additionally, for all regions examined, the battery size tends to an asymptotic value as the wind turbine size surpasses a specific value, which depends on the wind potential quality. Finally, it is important to note that for the relatively low wind potential areas the battery size is significantly larger than for the medium or high wind potential cases. Figure 14.16 presents the corresponding autonomous energy distributions (for a wind turbine–battery storage system) for various annual dieseloil consumption levels, for a high wind speed area. More precisely, each curve drawn corresponds to a given diesel–oil rate (e.g. Mf = 100 kg/y); the x-axis describes the wind-turbine’s rated power and the y-axis, the corresponding battery capacity. In the same figure, a zero-diesel solution is also included. According to the graph, there is a considerable battery capacity diminution by accepting a minimum (25 kg/y) diesel-oil consumption, representing approximately 1% of the annual diesel-only system fuel consumption. A significant battery capacity decrease is also encountered by accepting 100 kg/y diesel-oil consumption. For bigger diesel-oil quantities, the battery capacity is fairly reduced, excluding configurations based on very small wind-turbines, i.e. rated powers below 3 kW. Figure 14.17 presents similar calculations for a relatively low wind potential area. Using the information of Fig. 14.17, it can be easily concluded that, even by using significant diesel-oil quantities, the system’s dimensions


Integration of stand-alone and hybrid WESs into buildings 25 000

Mf = 0 (kg/year) Mf = 25 (kg/year) Mf = 50 (kg/year) Mf = 100 (kg/year) Mf = 250 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year)

20 000 Battery capacity (A h)

501

15 000

10 000

5000

0 0

2000

4000

6000

8000

10 000

12 000

14 000

16 000


14.16 Wind–diesel hybrid system optimization for a high wind speed area.

Mf = 0 (kg/year) Mf = 25 (kg/year) Mf = 50 (kg/year) Mf = 100(kg/year) Mf = 250 (kg/year) Mf = 500 (kg/year) Mf = 1000 (kg/year) Mf = 1250 (kg/year) Mf = 1500 (kg/year)

50 000 45 000 Battery capacity (A h)

40 000 35 000 30 000 25 000 20 000 15 000 10 000 5000 0 0

2000

4000

6000

8000 10 000 12 000 14 000 16 000 18 000 20 000


14.17 Wind–diesel hybrid system optimization for a low wind speed area.

(mainly battery capacity) are much larger than the high wind speed area ones. On top of this, only by using significant annual diesel-oil quantities (e.g. Mf = 250 kg/y) is it possible to obtain a considerable battery capacity reduction. Finally, it can be seen that almost 1000 kg/y of diesel-oil should


502

Stand-alone and hybrid wind energy systems z = 0 PV panels z = 10 PV panels z = 20 PV panels z = 50 PV panels z = 75 PV panels z = 100 PV panels z = 300 PV panels


25 000 20 000 15 000 10 000 5000 0 0

2500

5000

7500

10 000

12 500

15 000


14.18 Stand-alone wind–photovoltaic configuration for a low wind and medium solar potential area.

be used to guarantee the system energy autonomy, exploiting a relatively small wind-turbine (i.e. below 3 kW). In Fig. 14.18 the combined wind–photovoltaic solution is applied to an area with a relatively low wind potential (slightly above 5.5 m/s at 10 m height). According to the results calculated, a wind turbine of rated power equal to 7 kW minimum should be used in order for the required battery capacity not to exceed 25 000 A h. Considering, however, the high solar potential of the area, a significant battery size reduction is encountered by introducing a small number of photovoltaic panels. At the same time, the wind turbine rated power requirement is also decreased. Specifically, using 20 photovoltaic panels of 51 Wp (or 1020 W), the required wind turbine rated power is less than 5 kW, while the corresponding battery capacity drops to 20 000 A h. In addition, a larger number of photovoltaic panels (e.g. z = 100 or 5.1 kW) can practically establish a viable energyautonomous solution by using only 8000 A h of battery capacity and a wind converter of 5 kW. Accordingly, Fig. 14.19 shows the resulting battery size reduction for a higher (i.e. higher than that corresponding to Fig. 14.18) solar potential area. In that case, however, owing to the higher solar radiation available, the ensuing battery size reduction is much greater since, by using 20 pho-


Integration of stand-alone and hybrid WESs into buildings

z = 0 PV panels z = 10 PV panels z = 20 PV panels z = 50 PV panels z = 75 PV panels z = 100 PV panels z = 300 PV panels

25 000


503

20 000

15 000

10 000

5000

0 0

2500

5000 7500 10 000 Wind turbine rated power (W)

12 500

15 000

14.19 Stand-alone wind–photovoltaic configuration for a low wind and high solar potential area.

tovoltaic panels, the corresponding battery reduction approaches 25% in comparison with the zero-PV panel (or wind-only) solution, while the corresponding wind turbine size decreases to 3 kW. Subsequently, installation energy autonomy can be achieved by using only 50 panels, instead of 100 (see also Fig. 14.18, where the corresponding battery capacity is less than 10 000 A h). Finally, by increasing the number of photovoltaic panels, not only is the battery capacity decreased but a considerable reduction is also encountered in the wind converter rated power requirement. The possibility of using a stand-alone wind hybrid system to meet the electricity demand of remote consumers is environmental friendly and can be a viable solution. By applying an analytical sizing procedure, such systems are able to guarantee the energy autonomy of the building for the entire period analyzed. For estimation of the optimum size, long-term wind speed and solar irradiance measurements, as well as other meteorological data are needed. According to the international literature and to experience, wind hybrid systems have the ability to cover the corresponding load demand, though in case of zero load rejection (i.e. where the systems must satisfy 100% load demand), the utilization of a back-up electricity generator system is proposed.


504


14.9

References and further reading

Ameku K., Nagai B.M. and Roy J.N. (2008), ‘Design of a 3 kW wind turbine generator with thin airfoil blades’, Experimental Thermal and Fluid Science, 32/8, 1723–1730. Argiropoulos G. and Kaldellis J. (2000), ‘Are the fuel cells the solution of future energy demand problem?’, S-350, Lab. of Soft Energy Application & Envi. Pro., TEI of Piraeus. Bahaj A.S., Myers L. and James P.A.B. (2007), ‘Urban energy generation: Influence of micro-wind turbine output on electricity consumption in buildings’, Energy and Buildings, 39/2, 154–165. Balaras C., Gaglia A., Georgopoulou E., Mirasgedis S., Sarafidis Y. and Lalas D. (2007), ‘European residential buildings and empirical assessment of the Hellenic building stock, energy consumption, emissions and potential energy savings’, Building and Environment, 42, 1298–1314. Eggleston D. and Stoddard F. (1987), Wind turbine engineering design, Van Nostrand Reinhold. Energy Information Administration (2006), ‘International energy statistics’. EU Press Release (2009), ‘All new buildings to be zero energy from 2019 say MEPs’. European Union (2004), ‘European Union energy and transport in figures – 2004 edition, Part 2: Energy’. Brussels: DG for Energy and Transport. Freris L.L. (1990), Wind Energy Conversion Systems, Prentice Hall. International Energy Agency (2008), World Energy Outlook 2008. International Energy Agency (2009), World Energy Outlook 2009. Kaldellis J.K. (2004), ‘Parametric investigation concerning dimensions of a standalone wind-power system’, Applied Energy, 77, 35–50. Kaldellis J.K. (2005), Wind Energy Management 2nd ed., Stamoulis. Kaldellis J. and Kavadias K. (2000), Laboratory Applications of Renewable Energy Sources, Stamoulis. Kaldellis J.K. and Kavadias K. (2006), Computational Applications of Soft Energy Resources: Wind energy-hydro power, Stamoulis. Kaldellis J.K., Arrianas L., Konstantinou P. and Vlachou D. (1996), ‘Validation of aerodynamic behaviour of commercial wind-turbines’, presented at the 5th National Congress on Soft Energy Resources, B, 11–20, Democritos, Athens. Kaldellis J., Thiakoulis Tr. and Vlachou D. (1999), ‘Autonomous energy systems for remote islands based on renewable energy sources’, presented at 1999 European Wind Energy Conference and Exhibition, 968–971, Nice, France. Kaldellis J.K., Kavadias K. and Christinakis E. (2001), ‘Evaluation of the windhydro energy solution for remote islands’, Journal of Energy Conversion and Management, 42/9, 1105–1120. Kaldellis J.K., Kostas P. and Filios A. (2006), ‘Minimization of the energy storage requirements of a stand-alone wind pv’, Wind Energy, 9, 383–397. Kaldellis J. K., Spyropoulos G. and Kavadias K. (2007), Computational Applications of Soft Energy Resources: Solar potential–photovoltaic applications–solar heat systems, Stamoulis. Kavadias K.A. and Kaldellis J.K. (2000), ‘Storage system evaluation for wind-power installations’, presented at the International Conference Wind Power for the 21st Century, Kassel, Germany.


Integration of stand-alone and hybrid WESs into buildings

505

Köppen W. (1918), ‘Klassifikation der Klimate nach Temperatur, Niederschlag und Jahreslauf’, Petermans Mitt., 64, 193–203. Lai C.-M. (2006), ‘Prototype development of the rooftop turbine ventilator powered by hybrid wind and photovoltaic energy’, Energy and Buildings, 38/3, 174–180 Lemstrom B., Rakkolainen J. and Peltola E. (1999), ‘A wind farm’s impact on the quality of electricity in weak network’, presented at 1999 European Wind Energy Conference and Exhibition, 747–749, Nice, France. Lu L. and Ip K.Y. (2009), ‘Investigation on the feasibility and enhancement methods of wind power utilization in high-rise buildings of Hong Kong’, Renewable and Sustainable Energy Reviews, 13/2, 450–461. Mithraratne N. (2009), ‘Roof-top wind turbines for microgeneration in urban houses in New Zealand’, Energy and Buildings, 41/10, 1013–1018. Muller G., Jentsch M.F. and Stoddart E. (2009), ‘Vertical axis resistance type wind turbines for use in buildings’, Renewable Energy, 34/5, 1407–1412. Nagai B.M., Ameku K. and Roy J.N. (2009), ‘Performance of a 3 kW wind turbine generator with variable pitch control system’, Applied Energy, 86/9, 1774–1782. Ozgener O. (2006), ‘A small wind turbine system (SWTS) application and its performance analysis’, Energy Conversion and Management, 47/11–12, 1326–1337 Peacock A.D., Jenkins D., Ahadzi M., Berry A. and Turan S. (2008), ‘Micro wind turbines in the UK domestic sector’, Energy and Buildings, 40/7, 1324–1333. Torcellini P., Pless S., Deru M. and Crawley D. (2006), ‘Zero energy buildings: a critical look at the definition’, NREL, ACEEE Summer Study, Pacific Grove California. United Nations Development Programme (2006), ‘Human development report’. van der Linde C. (2004), ‘Study on energy supply security and geopolitics’, ETAP program final report, Directorate General Energy & Transport, European Commission, TREN/C1–06–2002. The Hague: Reprovan de Kamp BV. Vlachou D., Christinakis E., Kavadias K. and Kaldellis J. (1999a), ‘Optimum windhydro energy station operation, using an advanced fluid flow analysis code’, presented at 3rd National Congress on Computational Mechanics, 811–820, Volos, Greece. Vlachou D., Messaritakis G. and Kaldellis J. (1999b), ‘Presentation and energy production analysis of commercial wind turbines’, presented at 1999 European Wind Energy Conference and Exhibition, 476–480, Nice, France. World Business Council for Sustainable Development (2008), ‘Energy efficiency in buildings’. www.allcountries.org (2006), ‘Floor space completed and housing conditions of urban and rural residents’, China Statistics 2006: Data from the National Bureau of Statistics of the Peoples Republic of China (accessed June 2009).


15 Hybrid wind energy systems for desalination E. KONDILI, TEI of Piraeus, Greece

Abstract: In addition to power generation, the exploitation of renewable energy sources and hybrid energy systems can prove valuable in the production of fresh or even potable water. The objective of the present chapter is to describe the technology and implementation of water desalination systems with renewable energy sources (RESs) for freshwater supply. In this context, this chapter will: (a) review the current status, practices, advances, R&D activities and future prospects of the state of the art desalination technologies; (b) focus on the energy aspects of desalination and identify the critical parameters for the successful design and operation of hybrid energy desalination systems; and (c) give an insight into the future prospects of hybrid systems’ implementation in desalination processes. Key words: RES-based desalination, reverse osmosis, energy and water storage, desalination economics.

15.1

Introduction: the water scarcity problem

In addition to power generation, the exploitation of renewable energy sources and hybrid energy systems can prove valuable in the production of fresh or even potable water, especially in remote areas with difficult access to water and energy networks. In this context, the objective of the present chapter is to describe the technology and implementation of water desalination systems with renewable energy sources (RES) for fresh-water supply and highlight yet another valuable contribution that hybrid energy systems have made in the search for potential solutions to imperative social problems. Water is a valuable natural resource and access to fresh water is considered to be a basic human right. Water shortage is expected to be one of the most serious social and environmental problems to be faced in the coming years in many parts of the world. In fact, almost a quarter of the human population is suffering from an inadequate or poor quality fresh-water supply. Water scarcity does not only denote a lack of water in arid regions, but also a mismatch between water supply and demand; a problem with very strong spatial and temporal characteristics. Even in cases of positive total water balance, there may be periods of time in which water is not available. 506 © Woodhead Publishing Limited, 2010

Hybrid wind energy systems for desalination

507

Since almost 97% of water on Earth is seawater, the desalination (i.e. the removal of salt from the virtually unlimited supply of seawater or brackish water) is considered to be a very promising method to meet water demand, and today it is widely applied in areas with limited water resources. As in any type of separation, the critical issue to address in water desalination is the process’s particularly high energy demand. The forms of energy that are used in desalination processes are mainly thermal and electrical, which will be analyzed later in this chapter. The use of thermal or electrical energy sources in desalination technologies can play a very significant role in reducing the operational costs of the units, improving their environmental impacts and extending their implementation in remote areas with difficult access to other energy sources. With this in mind, this chapter’s specific objective is to analyse and describe the use of renewable energy sources in desalination processes. This chapter will: •

•

• • •

review the current status, practices, advances, research and development (R&D) activities and future prospects of the state of the art desalination technologies; focus on the energy aspects of desalination and place emphasis on its use, technical issues and environmental and financial implications of various energy forms in desalination processes; demonstrate the basic desalination system design principles and the main difficulties in the implementation of these systems; identify the critical parameters and provide guidelines for the successful design and operation of hybrid energy desalination systems; give an insight into the future prospects of hybrid systems’ implementation in desalination processes.

15.2

Desalination processes and plants

15.2.1 General considerations Desalination is the process of removing salt from saline water and producing fresh, even potable water. Seawater desalination separates saline water into two streams: a fresh-water stream containing a low concentration of dissolved salts and a concentrated brine stream. A large number of desalination plants have been installed throughout the world, the majority of which can be found in the Middle East and the Caribbean islands, with very good prospects for development in China in the coming years. Desalination is still considered to be more expensive than other methods of water production, mainly due to its intensive use of energy. However, this picture is changing because new R&D efforts and technological advancements have begun to reduce the cost of production.


508


In various cases, desalination has now proved a more reliable and economic means of water production when compared with methods such as dam construction or transportation of water by marine vessels. Even if desalination technology is used solely for the production of non-drinking quality water, it helps preserve existing water resources from over-exploitation and mismanagement. Desalination technology has evolved considerably over the past 50 years and has demonstrated its technical feasibility. Today, the average price of desalinated seawater is estimated to be only one-tenth of what it was 20 years ago, making it an attractive solution for the supply of water to islands. The total worldwide desalination capacity in 1971 was reported to be around 1.5 × 106 m3/day. In 1996 this had risen to 20.3 × 106 m3/day, with approximately 11 000 installations spread in 120 countries all over the world. Today, it is estimated that over 75 million people worldwide obtain fresh water by desalinating salt or brackish water. The International Desalination Association’s (IDA) Desalting Inventory 2004 Report shows that at the end of 2002 the installed and contracted brackish and seawater desalination plants worldwide totaled almost 13 600 desalination units in 10 350 plants, with a total capacity of 37.75 × 10 m3/day of fresh water [1]. The market volume increased from US$2.5 billion in 2002 to US$3.8 billion in 2005, with a growth rate in total desalination capacity of 12%, with over 15% per annum increase in plant and equipment investment. It is expected that the market will reach nearly US$30 billion by 2015. A dramatic increase is expected in new technologies and in small system applications in Asia, particularly in China. Currently, regions of the Middle East dominate demand with over 50% of the market share, followed by Asia-Pacific, America and Europe with almost 10% market share each [2]. The two main driving forces for this market development are the increasing number of water shortages and technology-driven cost reductions. Although desalination was previously considered a very expensive means of supplying water, the technological advancements (mainly focused in improved energy utilization) have allowed it to become a competitive method against other water supply approaches. However, with these production capacities worldwide, the large amounts of energy consumed in desalination plants must be considered, and as fossil fuels are used in certain desalination technologies, the negative environmental impacts of these processes must also be addressed. All desalination processes require energy to operate, depending on the separation technologies. The two most common desalination processes in use today are distillation processing and membrane processing (Fig. 15.1), together accounting for almost the total installed global desalination capacity.



509

Seawater desalination processes

Thermal processes (phase change)

Membrane processes (single phase)

Multistage flash evaporation (MSF)

Reverse osmosis (RO)

Multi-effect distillation (MED)

Electrodialysis (ED)

Vapor compression (VC) mechanical (MVC)& thermal (TVC)

15.1 Main classification of desalination processes [3].

Thermal desalination (distillation) involves a phase change, while membrane processes are single phase. Today, most R&D efforts and technological innovations are oriented towards membrane processes and, more specifically, towards reverse osmosis (RO) processes, as will be discussed later in the chapter.

15.2.2 Distillation desalination processes In distillation desalination processes, saline water is heated to produce water vapor which in turn is condensed to form fresh water. These processes include multi-stage flash (MSF) distillation, multi-effect distillation (MED) and vapor compression distillation (VC). Each of these processes require thermal or mechanical energy to cause water evaporation. Almost 40% of the world’s desalination capacity is based on the MSF desalination principle. In the MSF desalination process, seawater feed is pressurized, heated and discharged to a chamber maintained slightly below the saturation vapor pressure of the water. Then a fraction of this water flashes into steam and condenses on the exterior surface of heat-transfer tubing [3]. Historically, distillation technologies have had the lion’s share in the seawater desalination market, partly because energy subsidies favored these more energy-intensive technologies, and partly because of the poor


510


reliability of earlier membrane technologies. In particular, distillation is still the dominant process used in desalination plants in Middle Eastern countries where fossil fuels remain abundant and where areas suffer from serious fresh-water shortages.

15.2.3 Membrane/RO desalination processes It is worth noting that all of the major plants constructed or under construction by the private sector in certain non-oil-rich Mediterranean countries (Malta, Cyprus, Tunisia and Israel) have used membrane technologies which require electrical power as the only source of energy. Where fossil fuel prices are low, it is likely that distillation technology will continue to be used. However, even in the Middle East, where distillation technologies dominate, RO is gradually entering the market. Overall, given the current interest in global warming and sustainable development, the future development of desalination technologies may well shift towards low-energy desalination processes, i.e. favoring membrane technologies. Indeed, most new desalination plants now use membrane technologies. Membrane processes have considerable advantages in desalting water, and are now being widely applied in this market. The most widely applied membrane process, RO, represents more than 88% of membrane processes [4]. The RO process involves the forced passage of water through a membrane against the natural osmotic pressure to accomplish separation of water and ions. Under such high pressure the water molecules can pass through the membranes, and the salts are left behind as a briny concentrate. A typical RO system consists of four major subsystems (Fig. 15.2): • • • •

pre-treatment system; high pressure pump; membrane modules; post-treatment system.

Feed water pre-treatment is a critical factor in the operation of an RO system due to the membrane’s sensitivity to fouling. Pre-treatment commonly includes feed water sterilization, filtration and the addition of chemicals in order to prevent scaling and biofouling. The post-treatment system consists of sterilization, stabilization and mineral enrichment of the fresh water produced. The pre-treated feed water is forced by a high-pressure pump to flow across the membrane surface. RO operating pressure varies from 17 to 27 bar for brackish water and from 55 to 82 bar for seawater. Part of the feed water passes through the membranes, removing from it the majority of the dissolved solids, which is



511

Pre-chlorination Chemicals for pH control Seawater pumping

Filtered water pumping

Sand filters

Recycled water Dechlorination

RO unit

High-pressure pumps

pH control

Pre-treatment

Chlorination Post-treatment

15.2 Typical reserve osmosis unit flow sheet.

termed the permeate water. The remaining water together with the rejected salts emerges from the membrane modules at high pressure as a concentrated-brine discharge stream. In large plants reject brine pressure is used by a turbine to partially recover energy, recovering between 20% and 40% of the overall consumed process energy. The critical magnitudes for RO processes are the energy saving (i.e. the percentage of the mechanical energy that can be recovered during the process) and the water recovery ratio (i.e. the ratio of the seawater input volume to fresh-water output volume). A large number of RO plants have been installed for both seawater (SWRO) and brackish water (BWRO) applications. The RO process is also widely used in manufacturing, agriculture, food processing and pharmaceutical industries. Almost 32% of the total RO unit installed capacity is found in the United States, 21% in Saudi Arabia, 8% in Japan and 8.9% in Europe. RO units are available in a wide range of capacities due to their modular design. Large plants are made up of hundreds or thousands of modules which are accommodated in racks. Very small units (down to 0.1 m3/day) are also available for marine purposes, houses or hotels. The emerging dominance of RO in recent years is mainly due to the improvements that have been made in the RO process, reflected in the reduction of both capital and operating costs, with significantly reduced energy consumption. Most of this progress has been achieved through improvements to the membranes themselves, with improved resistance to


512


compression, longer life, higher possible water recovery, improved flux and improved salt passage. Apart from the need for an elaborate pre-treatment plant, RO processes have many advantages: • • • •

• •

The modular structure of the process makes it flexible enough to handle different plant capacities. The process is conducted at ambient temperature, which minimizes corrosion hazard. There is an embedded potential for water-power cogeneration when coupled with energy recovery systems. The rate of development in RO technology is high compared with other desalination processes, indicating that there may be further cost reductions in RO production of desalted water in the near future. Desalination by RO results in high salt rejection (up to 99%) and high water recovery ratios (up to 40%). Seawater reverse osmosis can produce potable water with salt content of about 500 ppm.

The energy issues of desalination processes and plants are discussed in the following sections of the chapter.

15.3

Energy requirements of desalination processes

15.3.1 General issues All desalination processes use energy, which is the largest cost component in the operation of a desalination plant. The greatest potential for further efficiency improvement and cost reduction in the desalination process lies in improvements to energy consumption. In fact, energy consumption is considered to be the main reason that desalination has not yet been as widely applied as expected, and only 1–2% of the fresh water consumed worldwide comes from desalination. The share of energy in overall cost varies according to the plant, its operational parameters and location, as is shown in Figs 15.3 and 15.4 for thermal and membrane processes respectively. The energy consumption of a desalination process depends on a variety of factors, including: • • • • •

seawater salinity; technology being used; ability of the system for energy recovery; temperature of operation for membrane processes; performance ratio;


Hybrid wind energy systems for desalination Electrical energy, 9%

Capital, 32%

513

Thermal energy, 50%

Chemicals, 3% Personnel, 6%

15.3 Typical cost structure of thermal seawater desalination [4]. Electrical energy, 44%

Capital, 37%

Consumables, 3%

Maintenance and parts, 7%

Membrane Labor, 4% replacement, 5%

15.4 Typical cost structure of RO seawater desalination [4].

Table 15.1 Power requirements of various desalination processes [4,5] Processa

Gain output ratio

Electrical energy consumption (kW h/m3)

Thermal energy consumption (kW h/m3)

MSF MED MED-TVC MVC BWRO SWRO

8–12 8–12 8–14 N/A N/A N/A

3.25–3.75 2.5–2.9 2.0–2.5 9.5–17 1.0–2.5 4.5–8.5

6.75–9.75 4.5–6.5 6.5–12 – – –

a

MSF multi-stage flash, MED multi-effect distillation, MED-TVC multi-effect distillation – thermal vapor compression, MVC mechanical vapor compression, BWRO brackish water reverse osmosis, SWRO seawater reverse osmosis.

• •

heat losses; temperature differences, etc., for thermal processes.

Table 15.1 shows the major power requirements of desalination processes [4,5]. Theoretically the absolute minimum amount of energy required for desalination is about 0.8 kW h/m3 of water produced, depending on the salt


514


content and regardless of the process used. In reality, the energy use is much higher than the theoretical minimum, and at its very highest it can be in the range of 3–15 kW h/m3 for seawater desalination, along with the older distillation plants. The development of reverse osmosis and improvements in energy recovery devices have changed this situation. With energy consumption in Mediterranean seawater RO plants lowered to 3 kW h/m3, seawater desalination is now within the reach of many communities. In RO desalination operations, the majority of the energy required is used for pressurizing the feed water, and on average a small RO plant’s energy consumption is approximately 6–8 kW h/m3 without energy recovery. Installing an energy recovery device reduces the energy consumption quite dramatically to 3–4 kW h/m3. Further unit energy consumption reductions have been noted, down to as low as 2 kW h/m3 [6,7]. For medium and large RO systems an energy recovery system can be used, recovering about 40% of the input energy. In countries making significant desalination investments, energy policies and energy investment planning should be revised to provide incentives for appropriate desalination processes, and to decide whether cogeneration of water and power is a suitable option according to the particular circumstances. This has become more significant for reasons ranging from integration of policies, water demand and power demand growing at a different rates and seasonal variations between water and power demands [8,9]. Thermal processes (MSF, MED) that operate with steam supplied by exhaust or bleeding steam (from back pressure or extraction steam turbines) are, however, economically attractive and comparable with RO energy cost [10].

15.3.2 RES-based desalination The use of RES in the operation of desalination plants is a feasible and environmentally compatible solution in areas with significant RES potential. The main driving forces for applying RES in desalination plants are: • • • • • •

continuing technological advancements in RES systems and their cost reduction; seasonal variability in water (and energy) demand, usually occurring in areas with high renewable energy availability; limited availability of conventional energy supply in remote areas; technological advancements in desalination systems; limitations presented by the environmental impacts of conventional desalination systems; and improved plant operation and maintenance of RES as compared with conventional power plants.



515

To that end, a lot of research and development work has been carried out, and the problem of the optimal configuration/combination of a RES energy source with a desalination plant has attracted the attention of many researchers and construction and engineering companies. The problem of how best to couple RES with desalination systems is a complicated and interesting one, and its solution is not always obvious. In fact, this is a major decision-making issue, and forms part of the wider problem of infrastructure planning. Various criteria should be taken into account, including among others: • • • • • •

the renewable energy availability; the investment and operational cost, and the availability of financial resources; the system’s efficiency; the availability of operational personnel; the suitability of the system to the characteristics of the location; the possibility for future increase of the system capacity [11].

RES suitable for use in desalination processes include wind, solar thermal, solar photovoltaic (PV) and geothermal. As mentioned above, RES driven desalination systems fall into two categories. The first one includes distillation processes driven by heat produced by RES systems, and the second includes membrane and distillation processes driven by electricity or mechanical energy produced by RES. Various potential combinations between RES and desalination systems are shown in Fig. 15.5. Matching renewable energies with desalination units, however, requires a number of important factors to be considered, as the various combinations of RES-driven desalination systems are not always practicable or viable in certain circumstances. The optimum combination of technology Renewable energy sources Geothermal

Electricity

Heat

Solar

Solar thermal

PV

RO ED MVC TVC MED MSF Electricity

Wind

Heat

Shaft

Shaft

Electricity

Electricity MVC RO RO ED MVC

RO ED MVC TVC MED MSF MVC RO ED RO MVC

15.5 Combinations of RES and desalination methods (PV photovoltaic, RO reverse osmosis, ED electrodialysis, MVC mechanical vapor compression, MED multi-effect distillation, MSF multi-stage flash, TVC thermal vapor compression) [4].


516


must be studied in connection to various local parameters such as geographical conditions, topography of the site, capacity and type of low-cost energy available, availability of local infrastructure (including electricity grid), plant size and feed water salinity. More specifically, the factors to be considered for selecting desalination processes suitable for a particular site include: •

• •

• • • • • • •

the amount of fresh water required in a particular application (i.e. the plant’s capacity) combined with the applicability of the various desalination processes; the seawater treatment requirements, i.e. the feed’s water salinity; the technical infrastructure of the area (e.g. road access, network), the local regulations concerning the land use, and the land area required or available, for the installation of the integrated energy and desalination unit; the remoteness of the area and the availability of grid electricity; the suitability and effectiveness of the process with respect to energy consumption; the capital cost of the equipment; robustness/low-maintenance criteria and simplicity of operation; compact size and ease of transportation to site; acceptance and support by the local community; operational organization at local level with relatively simple operator training.

Table 15.2 evaluates the combinations of desalination and RES according to certain energy-related criteria.

15.4

Integrated systems of renewable energy sources (RES) with desalination plants

15.4.1 General description Desalination using renewable energy is still at an early stage of development and implementation. One of the most likely markets for coupling RES with desalination is in small communities in remote locations, where there is no power grid connection or where energy is expensive. In the context of the utilization of more widely established RES (e.g. solar thermal–PV– wind), stand-alone desalination systems have been widely discussed. However, where only one source of renewable energy is available, the final system may still be configured in many ways. Much research continues to be done in this area, with many research teams focusing on specific technical issues, ways of integrating RES and desalination processes and how to optimize those processes (see also: the



Well suited for desalination plants requiring thermal power (3) Typically good match with need for desalination (3) Output is intermittent (energy storage required) (1) Output is relatively unpredictable (2)

Suitability for powering desalination plants Well suited for desalination plants requiring electrical power (3) Typically good match with need for desalination (3) Output is intermittent (energy storage required) (1) Output is relatively unpredictable (2)

Photovoltaic

Output is intermittent (energy storage required) (1) Output is very stochastic/ fluctuates (1)

Well suited for desalination plants requiring electrical power (3) Resources is locationdependent (2)

Wind energy

Output is predictable (3)

Well suited for desalination plants requiring thermal power (3) Resources are limited to certain location (1) Continuous power output (3)

Geothermal energy

Note: 3 excellent compliance with criterion, 2 good compliance with criterion, 1 poor compliance with criterion.

Predictability of power output

Continuity of power output

Site requirements and resources availability

Solar thermal energy

Criterion

Table 15.2 Evaluation of various RES in desalination applications [12]

518


reference list at the end of this chapter). Many small-scale and experimental projects have been installed but as yet there have been no serious industrial-scale projects. The Red–Dead project, aiming at linking the Red Sea with the Dead Sea, might be the first very large renewable energy-driven desalination scheme. It would have the potential to produce up to 850 millions of m3/year of potable water.

15.4.2 RO: wind desalination Wind power can now be regarded as a reliable and cost-effective power source for many areas of the world. Desalination systems driven by wind power are the most frequent renewable energy desalination plants (Fig. 15.6). More specifically, wind energy can be used efficiently providing that the average wind velocity is above 5 m/s. This makes wind-powered desalination a particularly interesting option for windy locations (such as islands), both for the solution of their energy supply problem and for the operation of seawater desalination plants. This option is becoming even more interesting given the new generation of small- and medium-sized wind turbines that have been and are being developed that offer reliabile service and low investment costs. Wind turbines may be classified depending on their nominal power: No as very small (No < 10 kW), small (No < 100 kW), medium sized (No < 0.5 MW) and large (No > 0.5 MW). All are based on mature technologies

Wind energy unit Grid

Desalination plant

Pre-treatment Wind

Generator Energy management

Pumps

Battery bank

Seawater

RO unit

Energy recovery

Posttreatment

Freshwater storage

Water Energy

15.6 Structures of a wind-based RO desalination plant [13].



519

and are commercially available, except for very large power systems, which still require advancement. Wind energy systems and desalination couplings may either be connected indirectly via a small autonomous electricity grid, or via the direct coupling of the wind generator and desalination plant for the sole purpose of producing water. Wind power is considered to be the most suitable for application to small (1–50 m3/day) and medium (50–250 m3/day) scale RO desalination plants [8]. The main design variables that affect the design of a wind–RO system are: • • • • • • • • • • •

water demand and RO plant’s capacity; location of wind turbine and desalination plant (required site development, altitude, etc.); feed water salinity; wind speed distribution; configuration of energy system; water storage capacity; distribution of available power; desalination unit energy consumption; salt rejection; operating pressure; permeate flux, both in terms of overall product rate and specific rate (per unit membrane area).

Design issues The basic assumptions for calculating the energy efficiency of the wind turbines, with or without an energy storage system, may be considered as follows. For a wind turbine with a nominal power of No kW, we expect an energy production E in the order of magnitude of E = CF * No*8760 kW h/year. Note that the installation capacity factor CF usually varies between 20% and 30%. Depending on the type of desalination plant, the required amount of energy per m3 of potable water will also be given. Therefore, we may have a series of alternatives concerning the installed power of the wind turbine and the combined capacity of the desalination plant [14,15]. To smooth operational performance, surplus energy can be stored using batteries [9] or water pumping systems, and so storage sizing should be considered in the design stage. Many other parameters should also be taken into account, including the possible losses incurred through use of energy storage systems (i.e. for low-wind/no-wind operation) and the availability of a water storage systems. In addition, capital and maintenance costs should be carefully assessed.


520


The variable nature of wind power does not affect water availability as water can be stored inexpensively for long periods of time without deterioration. If a plant is constructed with dimensions according to local wind conditions, then with appropriate energy storage facilities, fresh water could be made available at any time. The major problem with this type of installation, however, is that variable wind power may cause operational problems in the system. This is one of the most critical issues to be resolved in the design and implementation of a RES wind-based desalination project. Operational issues: technical difficulties RES are characterized by intermittent and variable intensity, whereas desalination processes are designed for continuous steady state operation. The variable nature of wind power means it can be problematic in process applications such as in a desalination plant. Although relatively predictable, wind is seldom constant, sometimes stopping altogether. The storage of wind energy in the form of electrical power is practical only when small amounts are involved. Storage batteries increase the total investment cost, so running a process of any magnitude on stored electrical energy is economically impracticable at present. If the product of the process can be stored inexpensively, however, then it may be practical to use large water storage equipment, which is relatively cheap, thereby allowing for downtime. To avoid the fluctuations inherent in RES, different energy storage systems may be used. The relative sizes of the wind turbine and the RO plant and the cut-in and cut-out criteria (for the RO plant to avoid excessive start-up and shut-down cycles) require careful design, but these are normally the only major obstacles at the design stage. If an intermediate energy storage system is necessary, however, it will mean a decrease in available energy, as well as increasing the cost of the plant, and would therefore impact on the wind turbine–RO plant configuration. For the operation of a wind-powered desalination plant, it is most important to have a plant that is insensitive to repeated start-up and shut-down cycles caused by changing wind conditions. At present, reverse osmosis is sensitive to stopping and starting due to time-consuming pre- and posttreatment processes, membrane fouling, and sensitive high-pressure pumps, which cannot be subject to erratic usage. Other drawbacks facing RO in remote areas include the requirement of skilled workers, the complex pre-treatment process, process chemicals requirements, and membrane maintenance and replacement.



521

15.4.3 Wind–RO configuration possibilities A classification of the different wind-powered RO systems found in the literature has been made [16]. This was based on some of the points previously discussed: • • •

the existence of an alternative electrical supply (weak grid or diesel generator); the matching of the available wind energy to the load; and the operational characteristic of RO membranes.

Systems with back-up (diesel/grid) In these systems, an additional energy source is provided (a diesel-powered generator or even the local grid) so that the power supplied to the RO is constant. The back-up generation complements the power generated from the wind turbine to match the RO unit power consumption. The main benefit of these systems as in any hybrid wind–diesel configuration is the achievement of fuel savings, which may increase the generator availability and reduce overall energy costs. On the other hand, problems such as fuel shortages, diesel generator maintenance, interruptions or power cuts in the supply may lead to unavailability of the RO system since it may not be possible to power it using the wind turbine alone. Systems without back-up RO systems without an external energy source can be divided into two categories, those which run under approximately constant operating conditions, and those that experience variable operational conditions. Near constant operating conditions Near constant operation can be implemented by three different means: usage of storage devices, on/off switching of the RO units, and de-rating the wind turbine. In all three cases, an attempt is made to supply the individual RO modules with approximately constant power. Storage devices In this strategy, storage devices are employed to accumulate energy surplus during periods when the power generated by the wind turbine is greater than the load demand from the desalination unit. This surplus would then


522


be used later when the generated power is insufficient to meet the load demand. One common way of storing the surplus energy is by using batteries. In this case, the relation between operational pressure, storage sizing and average wind speed should be considered at the design stage. Capital and maintenance costs must also be assessed carefully. A disadvantage of this approach to the system design is the rating of the energy storage system, which can make it economically unattractive at higher power levels due to the sizing of the battery bank. RO unit switching This strategy is based on the use of a higher-power wind turbine connected to multiple smaller RO units. Power control is achieved by switching the units on and off so as to match the power generated by the turbine to the demand for process energy. There is no limitation concerning system power rating, and this approach is feasible up to power levels of hundreds of kilowatts. Although frequent cycling of RO units is not usually recommended, problems can potentially be overcome by implementing different types of configuration. Higher power wind turbines operating at near constant speed could be connected to many smaller RO units switching on/off (load management), with power fluctuations smoothed out using short-term energy storage (for instance a flywheel). Wind turbine de-rating This approach consists of making use of the flat end of a pitch-controlled wind turbine power curve to operate the RO unit at approximately constant power. An implication of this configuration is that, since the turbine rated power is only achieved at high wind speeds, it would have to be de-rated by changing the settings of the pitching mechanism. This will cause the generated power to be flattened at lower wind speeds and consequently to have lower values. Therefore, the original rating of the turbine rotor would need to be considerably higher than the RO unit rated power, making the system more expensive. Variable operating conditions In contrast to systems that operate under constant conditions, another operational strategy is based on the establishment and imposition of certain operational limits. This means that, based on the input power to the RO unit (flow multiplied by pressure), a control strategy is determined which



523

imposes a fixed operating point on the system that lies within the allowed region (i.e. the operational window of the RO unit). By doing this, an attempt is made to operate the system autonomously over a wider power range, without the need to use a back-up unit or storage devices. The overall effect is to reduce capital and operating costs. It must be emphasized however, that very little is known about the consequences of variable operation of RO membranes. It is recognized that mechanical fatigue can occur and that the lifetime of the RO elements may be shortened and performance impaired by operating in these conditions. Extensive laboratory testing was carried out so as to develop suitable mathematical models for individual components over a wide operating range.

15.4.4 Implementation projects A number of units have been designed and tested; however, most of them are in demonstration and experimental scale [17,18,19]. To date, the only practical experiments that have taken place have used small capacity wind-powered RO systems, though few conclusions have been drawn from such small-scale experimentation, as it is still difficult to control the usage of wind in a cost-effective way. Coupling of a variable energy supply system to a desalination unit requires either power or demand management. The prospects of this combination remain high, however, owing in a large part to the low cost of wind energy. The operational experience from early demonstration units is expected to contribute to improved designs and a large number of commercial systems are expected to be implemented in the near future. •

•

•

As previously stated, experimentation with RO–RES coupling has been going on for some time, albeit on a small scale. As early as 1982, a small system was set up at Ile du Planier, France; a 4 kW turbine coupled to a 0.5 m3/h RO desalination unit. The system was designed to operate via either a direct coupling or in combination with energy storage using batteries. Another case of RO–RES coupling has been developed on the island of Drenec in France, in 1990. The wind turbine in this case was rated at 10 kW and used to drive a seawater RO unit. More recently some R&D projects have been carried out, such as the wind desalination system built on a cement plant at Drepanon, near Patras, Greece. The project was initiated in 1992 and completed in 1995. The project called for the full design and construction of a 35 kW wind turbine (blades, generator, etc.), plus the installation of two RO units with a production capacity of 5 m3/day and 22 m3/day respectively.


524

•


Unfortunately, since 1995 operational results have been poor due to the low wind regime. A very interesting experiment has been carried out at a test facility in Lastours, France, where a 5 kW wind turbine provides energy to a number of batteries (1500 A h, 24 V) and via an inverter to an RO unit with a nominal power of 1.8 kW. Furthermore a great amount of work on wind RO systems has been done by the Instituto Tecnologico de Canarias, ITC within several projects such as AERODESA, SDAWES and AEROGEDESA.

15.4.5 Implementation projects with hybrid energy systems Autonomous hybrid systems are independent and incorporate more than one power source. Diesel generators are mainly used as back-up systems. However, fuel transportation to remote areas poses the same difficulties as water transportation. RES penetration depends only on the economic feasibility and the proper sizing of the components to ensure quality and continuity of supply. One important application is the use of photovoltaics and wind generators to drive RO desalination units. Each desalination system has specific problems when it is connected to a variable power system. RO has to deal with the sensitivity of the membranes regarding fouling and scaling, as well as any unpredictable phenomena, such as start–stop cycles and partial load operation during periods of oscillating power supply. Several RO units with intermittent or infrequent operation have to replace their membranes regularly. On the other hand, units with storage back-up systems (e.g. batteries) increase the system’s initial costs and also increase the maintenance requirements. Most of the plants constructed to date have been as either research or demonstration projects forced to conclude by budget limitations or staff availability issues. GECOL and a consulting consortium of experts from ZSW, DEWI and LI are managing the implementation of an experimental research facility for seawater reverse osmosis desalination powered from renewable energy sources (SWRO-RES) on the Mediterranean coast of Libya. The nominal production of the plant will be 300 m3/day of drinking water to supply a local village with. Both wind energy conversion (WEC) and PV power generation will be integrated into a grid-connected power supply for a RO desalination plant, with power recovery by pressure exchange. The facility design is flexible, allowing for the integration of a diesel generator and electrochemical storage as power supply alternatives, as well as BWRO. The wide range of feasible plant configurations will mean that research can be extended to off-grid/stand-alone performance analysis of such hybrid systems.



525

While the expected nominal power load for the operation of the RO desalination system is 70 kW (net power after recovery), the solar PV system is designed for 50 kW, and the WEC for 200 kW nominal output. The design aims at a reduction of the annual non-renewable energy consumption to about 40% of total annual energy consumption. The economic analysis of the integrated renewable energy systems predicts standardized water costs for the integration of Grid+WEC with RO at c1.8/m3, and for Grid+PV with RO at c1.9/m3 as compared with c1.3/m3 for operation using grid power (i.e. conventional plant) alone [20,21].

15.5

Environmental impacts of renewable energy sources (RES)-based desalination plants

15.5.1 Environmental aspects Desalination plants provide water to meet the supply needs of remote areas. They are usually implemented as the result of analyzing various solutions to the need for a water supply. For example, in several Greek islands, fresh-water requirements are normally met through the construction of large dams, ground reservoirs or desalination plants. On smaller islands, the only available solution is the transport of fresh water by ship, which can incur high costs and may not provide adequately hygienic conditions [10]. All these water supply methods have environmental ramifications, varying in severity according to the type of project, its location and its scale. The main environmental impacts of an RO desalination plant are: • • • • • •

noise disturbance; optical disturbance; land use; interference with public access to the coast; discharge of brine to coastal or marine eco-systems or, in the case of inland brackish water desalination, to rivers and aquifers; the emission of greenhouse gases from electricity and steam production using fossil fuel powered plant – this is eliminated/reduced where the energy source is renewable.

The area required for installation of RO desalination units is minimal, though it can be much larger for thermal desalination units, and in most cases these plants will be positioned close to the sea. However, as the areas requiring desalination plants are often small islands, land availability often merits strong opposition to the choice of desalination as a means of water production. On the other hand, installation of dams and ground reservoirs can have much worse environmental impacts and present much greater land


526


use restrictions, which in turn provides a strong argument in favor of properly sized and located desalination plants. The floating desalination unit [22] is an innovative project that is hoped will resolve land use problems.

15.5.2 Floating desalination plant The first floating wind turbine/desalination plant in the world has been developed by a number of scientists and engineers, led by the University of the Aegean. Two of the most pressing environmental challenges of today – energy production and water supply – have been addressed by this innovative and practical solution to the water needs of Greek islands. The floating autonomous environmentally friendly and efficient desalination unit (FAEFEDU) is designed to produce potable water from seawater by generating power through its on-board wind turbines (Fig. 15.7). The unit sits on a special floating 20 × 20 m2 platform with an 8 m high water cylinder and a 22 m high wind turbine tower. The unit can adapt to any weather conditions. Water production is more than 70 m3/day – enough for the needs of about 300 people. In order to achieve the largest possible energy and desalination production, scientists focused on minimizing the scale and polluting effects of the central desalination unit, increasing the overall energy efficiency of the cycle. In addition, because the unit is autonomous, it is not required to be connected to the national electrical grid. Since the unit is portable, it can be

15.7 Floating desalination unit.



527

stationed away from populated centers wherever needed, on a seasonal basis for instance, to service the needs of islands that have an enlarged population during summer months. In addition, the unit can be repositioned to take advantage of changing weather conditions. The unit has been co-financed by the European Fund for Regional Development and domestic national funds.

15.6

Economic considerations in renewable energy sources (RES)-based desalination

Various efforts have been made to develop tools for the design, the economic evaluation and the determination of the main parameters for basic RES-based desalination plants. Many articles and R&D projects have analyzed the design and financial aspects of these units [22–33], which reach various conclusions concerning the optimal decision under specific circumstances. A number of parameters affect the design of such a plant, and they also impact on the financial evaluation of the units. There is no specific and generally applicable tool for determining the cost of such a unit, as all the technical, environmental and social variables are site-specific. As a general rule, a seawater RO unit has low capital cost and significant maintenance cost due to the high cost of the membrane replacement. The cost of the energy used to drive the plant is also high. This chapter is intended to inform researchers and professionals working on RES-based desalination plants in two ways. Firstly, it attempts to exhaustively enumerate all the factors that should be taken into account with such an endeavor. Secondly, it provides real case studies, including design and cost specifications.

15.6.1 Parameters affecting the economics of desalination A detailed financial analysis leading to precise estimates should always be carried out before private investments are made. The investor may undertake the cost of the project expecting to benefit from the future sale of fresh water either on the free market or to the municipality it belongs to, like, for example, the Milos desalination plant [13]. Many such private investments are expected to be made in the coming years, particularly in areas with water shortages and a reliance on the tourist industry. Table 15.3 presents a synthesis of the most critical limitations and choices that affect the feasibility and financial attractiveness of a RES-based desalination project. More specifically, for the case of wind–RO desalination, the factors that are taken into account in water production costs are shown in Table 15.4.


528


Table 15.3 Parameters affecting economics of RES-based desalination plants Parameters affecting economics of RES-based desalination plants

Comments

The desalination technology (thermal, RO)

In general, RO units have lower investment cost but high operation and maintenance costs Large capacity units are more expensive but the water unit cost is lower They define the size of the wind farm required for a given annual production of fresh water

Plant’s capacity The climatic conditions, the characteristic of wind turbines and the energy requirement of the RO plant The energy requirement of the desalination plant

The feed water salinity The location where the wind turbine and the desalination plant will be installed The configuration of the energy system The water storage capacity The available power distribution (e.g. the wind speed distribution, solar radiation)

This is determined by: (1) the water supply salt concentration and (2) the coupling of the energy and the desalination system BWRO is generally cheaper than SWRO Required siting, altitude, infrastructure preparation costs Main design decision determining the operation and the cost of the unit Design parameter determining the operation of the unit It affects the size, the configuration and, therefore, the investment cost

Table 15.4 Cost items of a wind-based desalination plant Investment cost Cost Cost Cost Cost

of of of of

land wind turbine energy storage systems the RO plant components

Annual operating cost Personnel cost Chemicals cost Electricity cost Maintenance and spares cost Membrane replacement



529

15.6.2 Examples of RES-based desalination cost estimation In the following section, some case studies from real plants are presented. They are all different types of plants installed in different areas and each one has its own technical characteristics. Libya [27] A demonstration RES-desalination plant has been designed in Libya (Integrated Power and Water Point) that will supply up to 300 m3/day of water and 240 kW electricity to a village. For the 60 kW RO power demand a 275 kW wind turbine is integrated with a 300 kW diesel plant. The process simulation for the desalination of seawater with 4.3% salinity under nominal operation conditions yields 57% recovery rate at a specific energy consumption of 4.8 kW h/m3 (pumping included). The power demand at a nominal fresh-water output of 300 m3/day is 60 kW, based on the calculation of 3000 m3/day*4.8 kW h/m3/24 h/day = 60 kW. The resulting cost of water is c2.24/m3. In this specific plant, detailed measurements have been taken in order to make reliable calculations of the costs. Spain [11] As every wind farm with a particular type of turbine and a given wind regime has a nominal optimum production capacity for each desalination plant, this must be specified in every case under consideration. In this context, a wind farm proposed for installation in Gran Canaria with a nominal power of 460 kW and a wind regime (in the area of Pozo Izquierdo) with an average annual speed of 7.9 m/s and sited 10 m above ground level, was projected to employ an optimum of 11 RO plants each with a capacity of 100 m3/day. However, for technical and economical reasons the decision was made to use 8 RO plants, each with a capacity of 25 m3/day. The water cost of a large, wind-powered brackish water reverse osmosis unit with a capacity of 250 m3/day is of the order of c2/m3. A project in Tenerife, Spain, included a 200 kW wind turbine, operating at an average wind velocity of 7.5 m/s, with an expected yearly energy yield of around 600 MW h. This amount of energy was projected to be capable of producing over 200 m3/day water. Morocco [30] Morocco is characterized by a semi-arid climate, where the obligation to use other nonconventional water resources such as desalinated water or


530

Stand-alone and hybrid wind energy systems Table 15.5 Seawater desalination Seawater desalination

RO

MVC

Number of desalination units Nominal unit water production (m3/day) Specific energy consumption (kW h/m3) Total nominal power (kW) Annual energy consumption (MW h/year) Lifetime (years)

5 1200 5 250 2190 20

2 1200 8 400 3500 20

waste water reuse is a necessity. In addition, Morocco has a large potential for wind and solar energy sources that could be used for seawater desalination. Here follows an estimation of the cost of desalinated water for three towns in the south of Morocco, using the method of levelized water cost (LWC). The cost was estimated for two seawater desalination processes: reverse osmosis and mechanical vapour compression (MVC) powered by wind turbines. Electric connection with the grid is available, so that the grid can be used to power the plant when RES are not available. This alternative is then compared with the baseline which consists of the grid-only configuration. The desalination processes studied were designed to produce 1200 m3/ day of fresh water, equivalent to the daily consumption of almost 10 000 inhabitants. Table 15.5 gives technical characteristics of the two desalination processes studied. The baseline water cost was evaluated at c0.91/m3 for RO. The cost breakdown structure for a wind-based RO-desalination unit, as shown for this plant in Morocco, is: • • • •

37.5% desalination investment cost; 31.6% wind turbine cost; 24.2% operation and maintenance of desalination unit cost; 6.7% operation and maintenance of wind turbine cost.

This adds up to an LWC of almost c0.85/m3. Milos island, Greece [13] A wind-based desalination unit on the Greek island of Milos, located in the Cyclades complex of islands, has been in operation since summer 2007. The unit has a capacity of 3000 m3/day. At the moment it operates at 2000 m3/day production of potable water. This is a private investment that has been subsidized by the state. The water is sold to the municipality of



531

Milos, in a continuous effort to solve the urgent water shortage problem, especially during the summer months. The contract that has been signed between the private company and Milos Municipality refers to a selling price of water of almost c1.8/m3. The entire plant includes: • the desalination plant; • a wind turbine of 600 kW; • the storage tanks (capacity 3000 m3); • the remote monitoring and control system. Before the installation of the unit, water was transported from Athens at a very high cost and of very poor quality [33]. The implementation of this novel project has improved the quality of life of the island in many respects. The siting of the unit on such a tourist island as Milos could have been a major problem, mainly because of the optical and noise disturbance. Therefore, the unit was located on a hill that is not apparent from most of the island’s villages.

15.7

Future trends

Although present desalination technologies and various forms of RES are well developed, there is wide scope for improvements in efficiency, reliability, simplicity and investment costs in each one of these technologies. Therefore, further research efforts should be directed towards improving and enhancing the presently utilized technologies. It is also important that new technologies be investigated. There is a particular need for research and development in the coupling between desalination and RES. Serious progress in the field will take place if industrial-scale projects are implemented. Analysis of current trends in R&D activities shows that energy consumption in all desalination processes is much higher than the thermodynamic minimum requirement, and this energy consumption is the major component of the operating cost of a desalination plant. Research in this area is therefore focused on both reducing energy consumption, and the introduction of alternative, clean and sustainable energy sources. Development of high-flux membranes and the integration of energy recovery devices have been combined to greatly reduce overall energy consumption, resulting in a potential energy demand of below 2 kW h/m3. Coupling desalination processes with non-conventional energy sources would present further advantages in improving the environmental impacts of desalination and the long-term economic costs of RES-based desalination systems.


532


15.8

Sources of further information and advice

In addition to all the references already cited, the contributions below will facilitate further reading and provide a rather more complete list of works in the field. Alghoul, M.A., Poovanaesvaran, P., Sopian, K., Sulaiman, M.Y., 2009. Review of brackish water reverse osmosis (BWRO) system designs. Renewable and Sustainable Energy Reviews, Vol. 13(9), pp. 2661–2667. Almulla, A., Hamad, A., Gadalla, M., 2005. Integrating hybrid systems with existing thermal desalination plants. Desalination, Vol. 174(2), pp. 171–192. Altener Programme, 2002. Renewable energy driven desalination systems – REDDES. Technical analysis of existing RES desalination schemes. Stylianos Loupasis. http://www.nad.gr/readsa/files/ TechnodatabaseREDDES.PDF Calì, G., Fois, E., Lallai, A., Mura, G., 2008. Optimal design of a hybrid RO/MSF desalination system in a non-OPEC country. Desalination, Vol. 228(1–3), pp. 114–127. Ekren, B.Y., Ekren, O., 2009. Simulation based size optimization of a PV/ wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions. Applied Energy, Vol. 86(9), pp. 1387–1394. Fadigas, E.A.F.A., Dias, J.R., 2009. Desalination of water by reverse osmosis using gravitational potential energy and wind energy. Desalination, Vol. 237(1–3), pp. 140–146. Fritzmann, C., Löwenberg, J., Wintgens, T., Melin, T., 2007. State-of-the-art of reverse osmosis desalination. Desalination, Vol. 216(1–3), pp. 1–76. Greenlee, L.F., Lawler, D.F., Freeman, B.D., Marrot, B., Moulin, P., 2009. Reverse osmosis desalination: water sources, technology, and today’s challenges. Water Research, Vol. 43(9), pp. 2317–2348. Hamed, O.A., 2005. Overview of hybrid desalination systems – current status and future prospects. Desalination, Vol. 186(1–3), pp. 207–214. Helal, A.M., El-Nashar, A.M., Al-Katheeri, E.S., Al-Malek, S.A., 2004. Optimal design of hybrid RO/MSF desalination plants Part II: Results and discussion. Desalination, Vol. 160(1), pp. 13–27. Helal, A.M., El-Nashar, A.M., Al-Katheeri, E.S., Al-Malek, S.A., 2004. Optimal design of hybrid RO/MSF desalination plants. Part I: Modelling and algorithms. Desalination, Vol. 154(1), pp. 43–66. Kamal, I., 2008. Myth and reality of the hybrid desalination process. Desalination, Vol. 230(1–3), pp. 269–280. Khawajia, A.D., Kutubkhanaha, I.K. Wieb, J-M, 2008. Advances in seawater desalination technologies. Desalination, Vol. 221(1–3), pp. 47–69.



533

Kim, Y.M., Kim, S.J., Kim, Y.S., Lee, S., Kim, I.S. , Kim, J.H., 2009. Overview of systems engineering approaches for a large-scale seawater desalination plant with a reverse osmosis network. Desalination, Vol. 238(1–3), pp. 312–332. Kiranoudis, C.T., Voros, N.G., Maroulis, Z.B., 1997. Wind energy exploitation for reverse osmosis desalination plants. Desalination, Vol. 109(2), pp. 195–209. Moreno, F., Pinilla, A., 2005. Preliminary experimental study of a small reverse osmosis wind-powered desalination plant. Desalination, Vol. 171(3), pp. 257–265. Pestana, I., Latorre, F.J.G., Espinoza, C.A., Gotor, A.G., 2004. Optimization of RO desalination systems powered by renewable energies. Part I: Wind energy. Desalination, Vol. 160(3), pp. 293–299. Schiffier, M., 2004. Perspectives and challenges for desalination in the 21st century. Desalination, Vol. 165, 1–9. Tzen, E., Theofilloyianakos, D., Kologios, Z., 2008. Autonomous reverse osmosis units driven by RE sources, experiences and lessons learned. Desalination, Vol. 221(1–3), pp. 29–36. Voivontas, D., Yannopoulos, K.A., Rados, K., Zervos, A., Assimacopoulos, D., 1999. Market potential of renewable energy powered desalination systems in Greece, Desalination, Vol. 121, pp. 159–172. Warfel, C.G., Manwell, J.F., McGowan, J.G., 1988. Techno-economic study of autonomous wind driven reverse osmosis desalination systems. Solar & Wind Technology, Vol. 5(5), pp. 549–561.

15.9

References

1. Wangnick/GWI, 2005. 2004 Worldwide desalting plants inventory. Global Intelligence, Oxford, UK. (Data provided to the Pacific Institute and used with permission.) 2. http://ec.europa.eu/environment/etap/pdfs/waterdesalination.pdf 3. Al-Karaghouli, A., Renne, D., Kazmerski, L.L., 2009. Solar and wind opportunities for water desalination in the Arab regions. Renewable and Sustainable Energy Reviews, Vol. 13(9), pp. 2397–2407. 4. Eltawil, M.A., Yhengming, Y., Zuan, L.-Q., 2009. A review of renewable energy technologies integrated with desalination systems, Renewable and Sustainable Energy Reviews, Vol. 13(9), pp. 2245–2262. 5. Miller, J.E., 2004. Review of water resources and desalination technologies. Albuquerque, NM: Sandia National Laboratories; http://www. sandia.gov/ water/docs/MillerSAND2003_0800.pdf. 6. Forstmeier, M., Mannerheim, F., D’Amato, Shah, M., Liu, Y., Baldea, M., Stella, M., 2007. Feasibility study on wind-powered desalination. Desalination, Vol. 203(1–3), pp. 463–470.


534


7. Habali, S.M., Saleh, I.A., 1994. Design of a stand – alone brackish water desalination wind energy system for Jordan. Solar Energy, Vol. 52(6), pp. 525–532. 8. Kaldellis, J.K., Kavadias, K.A., Kondili, E., 2004. Renewable energy desalination plants for the Greek islands – technical and economic considerations. Desalination, Vol. 170(2), pp. 187–203. 9. Kaldellis J.K., Kondili E., Kavadias K.A., 2005. Energy and clean water coproduction in remote islands to face the intermittent character of wind energy. Int. J. of Global Energy Issues, Vol. 25/3,4, pp. 298–312. 10. Vlachos G., Kaldellis J.K., 2004. Application of a gas-turbine exhausted gases to brackish water desalination. A techno-economic evaluation. Applied Thermal Engineering, Vol. 24(17–18), pp. 2487–2500. 11. Carta, J.A., Gonzhlezb, J., Subiela, V., 2004. The SDAWES project: an ambitious R&D prototype for wind powered desalination. Desalination, Vol. 161(1), pp. 33–48. 12. Delyannis, E., Belessiotis, V., 1996. A historical overview of renewable energies. In: Proc. Mediterranean Conference on Renewable Energy Sources for Water Production, EURORED network, CRES, EDS; p. 13–7. 13. Kondili E., Kaldellis J.K., 2008. Proceedings, World Renewable Energy Congress (WRECX) Editor A. Sayigh, July, Glasgow, Scotland, UK, pp. 2120–2128. 14. Koklas, P.A., Papathanassiou, S.A., 2007. Component sizing for an autonomous wind-driven desalination plant. Renewable Energy, Vol. 31(13), pp. 2122–2139. 15. Mathioulakis, E., Belessiotis, V., Delyannis, E., 2007. Desalination by using alternative energy: Review and state-of-the-art. Desalination, Vol. 203(1–3), pp. 346–365. 16. Miranda, M.S., Infield, D., 2003. A wind-powered seawater reverse-osmosis system without batteries. Desalination, Vol. 153(1–3), pp. 9–16. 17. Papapetrou, M., Epp, C., 2007. Autonomous desalination units based on Renewable energy systems – a review of representative installations worldwide. Solar Desalination for the 21st Century, Springer, Netherlands. 18. Tzen, E., Theofilloyianakos, D., Kologios, Z., 2008. Autonomous reverse osmosis units driven by RE sources, experiences and lessons learned. Desalination, Vol. 221(1–3), pp. 29–36. 19. Tzen, E., Christian Epp., Papapetrou M., 2006. Co-ordination Action for Autonomous Desalination Units Based on RE Systems, ADU-RES. http://www. evvec2006proceedings.info/allfiles2/628_Ewec2006fullpaper.pdf. 20. Kershman, S.A., Rheinlander, J., Neumann, T., Goebeld, O., 2005. Hybrid wind/PV and conventional power for desalination in Libya – GECOL’s facility for medium and small scale research at Ras Ejder. Desalination, Vol. 183(1–3), pp. 1–12. 21. Kershman, S.A., Rheinländer, J., Gabler, H., 2003. Seawater reverse osmosis powered from renewable energy sources – hybrid wind/photovoltaic/grid power supply for small-scale desalination in Libya. Desalination, Vol. 153(1–3), pp. 17–23. 22. Agashichev, S.P., 2004. Analysis of integrated co-generative schemes including MSF, RO and power generating systems (present value of expenses and ‘levelised’ cost of water). Desalination, Vol. 164(3), pp. 281–302.



535

23. Atikol U., Hikmet S.A. 2005. Estimation of water production cost in the feasibility analysis of RO systems, Desalination, Vol. 184, pp. 253–258. 24. Ekren, O., Ekren, B.Y., Ozerdem, B., 2009. Break-even analysis and size optimization of a PV/wind hybrid energy conversion system with battery storage – a case study. Applied Energy, Vol. 86(7–8), pp. 1043–1054. 25. Fiorenza G, Sharma VK, Braccio G. 2003. Techno-economic evaluation of a solar powered water desalination plant. Energy Convers Manage, Vol. 44, pp. 2217–2240. 26. Garcia-Rodriguez, L., Romero-Ternero, V., Gomez-Camacho, C., 2001. Economic analysis of wind-powered desalination. Desalination, Vol. 137, pp. 259–265. 27. Rheinländer, J., 2007. De-central water and power supply integrating renewable energy – technical and economic performance prediction. In Solar Desalination for the 21st Century, pp. 111–126, Springer, Netherlands. Available online at: http://www.springerlink.com. 28. Saheb-Koussa, D., Haddadi, M., Belhamel, M., 2009. Economic and technical study of a hybrid system (wind–photovoltaic–diesel) for rural electrification in Algeria. Applied Energy, Vol. 86(7–8), pp. 1024–1030. 29. Warfel, C.G., Manwell, J. F., McGowan, J.G., 1988. Techno-economic study of autonomous wind driven reverse osmosis desalination systems. Solar & Wind Technology, Vol. 5(5), pp. 549–561. 30. Zejli, D., Elmidaoui, A., 2007. Moroccan potentialities of renewable energy sources for water desalination. In Solar Desalination for the 21st Century, pp. 127–138, Springer, Netherlands. Available online at: http://www.springerlink. com. 31. Zervos, A., Assimacopoulos, D., 2000. Estimating the cost of water produced by RES powered desalination systems. Mediterranean Conference on Renewable Energy Sources for Water Production, Santorini, Greece, June 2000. 32. Kaldellis J.K., Kavadias K., Vlachou D., 2000. Improving the economic viability of desalination plants. Mediterranean Conference on Policies and Strategies for Desalination and Renewable Energies, Santorini Island, Greece. 33. Kaldellis, J.K., Kondili, E., 2007. The water shortage problem in Aegean Archipelago islands. Cost-effective desalination prospects. Desalination Journal, Vol. 216, pp. 123–128.


Index

ABB, 349 absorbent glass mat, 342 AC generators, 172 AC/DC converters, 233–4 Active Power flywheel, 370, 372–3, 378 cutaway/exploded view, 371 AERODESA, 524 AEROGEDESA, 524 Aerospace Flywheel Development Program, 381 ageing mechanism, 39 air lift pump, 288 air saturator, 400 alkaline fuel cells, 256 Altairno lithium–titanate material battery cells, 346 aluminium, 346 anemometer, 446 annual escalation rate, 114 APS see autonomous power systems arbitrage, 44 autonomous power systems, 274, 278 auxiliary electronic equipment, 489, 494 auxiliary generators, 234–6 balance of system, 33 battery, 485–6 modelling, 454–5 see also specific battery battery bank, 119–20, 153, 194 battery energy storage system, 53–6, 192, 199 control, 205–6 control scheme, 206 lead–acid batteries, 54–5 Li ion, 56 metal-air batteries, 56 Na–S, 55 Ni–Cd, 55

WECS–BESS feeding a load, 209–10 WECS–DG–BESS feeding a load, 210–11 battery sizing, 188 battery storage, 231–3 Beacon flywheel system, 383 beryllium oxide, 377 BESS see battery energy storage system black start, 403 brackish water reverse osmosis, 511, 524 bromine, 354 buildings characteristics, 477–8 energy consumption overview, 478–80 European Union facts about hybrid energy systems, 481–2 gross domestic product vs primary consumption per capita, 476 hybrid energy systems description, 482–9 auxiliary electronic equipment, 489 complementary electric generator unit, 488–9 storage system unit, 485–8 wind turbine generator, 482–5 operational modes of hybrid energy systems, 495–6 sizing procedure for hybrid energy systems, 489–94 5 kW inverter efficiency evolution, 495 auxiliary electronic equipment, 494 diesel–electric generator, 493–4 diesel–electric generator SFC distribution, 493 energy storage system’s main parameters, 490–2 non-dimensionalised wind turbine power curve, 491

536 © Woodhead Publishing Limited, 2010

Index photovoltaic power station, 494 pump–hydro, 492–3 wind turbine rated power, 489–90 stand-alone and hybrid wind energy systems integration, 475–503 stand-alone wind–PV configuration low wind and high solar potential area, 503 low wind and medium solar potential area, 502 system performance and optimisation of hybrid energy, 496–503 electricity profile of autonomous building analysed, 499 PV–diesel algorithm, 498 wind hybrid stand-alone system main parameters, 499 wind stand-alone system configurations, 500 wind–diesel algorithm, 497 wind–diesel hybrid system optimisation for high wind speed area, 501 wind–diesel hybrid system optimisation for low wind speed area, 501 bulb turbine, 292 BWRO see brackish water reverse osmosis CAES see compressed air energy storage; compressed air energy storage technologies CAES with humidification, 400 CAES with steam injection, 400 CAESSI see CAES with steam injection CAN-bus see controller area network-bus capacity factor, 490 capital cost, 41 carbon dioxide emissions, 270–1 Carnot engine, 433 CASH see CAES with humidification cell temperature, 230 centralised generation, 425 chemical energy storage, 53–9, 73 choppers, 435 climate change, 419 cogging torque, 172–3 commercial sector, 479–80 commodity storage, 44 Composite Manufacturing Technology Centre, 380 compressed air energy storage technologies, 51–2 abandoned limestone mine to be used by Norton, Ohio CAES plant, 401

537

capacity factor as function of autocorrelation time for storage capacities, 417 combustion turbine, expander and motor/generator, 399 compressor train, clutch and motor/ generator, 398 current status and future progress, 396–403 advanced concepts, 397–402 advantages and research issues, 402–3 current systems, 397–402 first generation CAES plant schematic, 396 second generation CAES plant schematic, 402 proposed CAES plant in Norton, Ohio, 400 Ridge Energy wind CAES study, 403–4 commercial energy storage costs, 404 Ridge Storage wind/CAES plant vs other generation options, 409 storage wind/CAES simulation parameters, 405 seasonal storage, 416 wind energy systems, 393–419 wind integration issues, 404–18 cost of electricity delivered to demand centre, 411 electricity cost and wind turbine capacity factor, 408 wind energy baseload plant parameters, 413 wind speed autocorrelation time and storage capacity for baseload wind systems, 416–18 wind turbine arrays and transmission systems, 408–12 wind turbine arrays, transmission, and CAES, 412–16 wind turbine capacity factor, 406–8 wind/transmission/CAES plant costs, 415 constant economic growth, 480 contingency reserve, 43 continuously variable transmission, 385 control circuit, 434 controlled rectifiers, 434 controller, 178, 182 controller area network-bus, 442 conventional generator modelling, 455 Coral Bay wind–diesel system, 386 cost calculation, 237–9 cost-benefit analysis, 155


538

Index

coulombic efficiency, 333–4 cross-flow turbine, 292 customer damage function, 458 CVT see continuously variable transmission cycle efficiency, 37, 38 D&WS see Diesel and Wind Systems Daimler Benz, 352 DC/AC converters, 233–4 DC/DC buck–boost converters, 435 DC/DC step-down converters, 435 DC/DC step-up converters, 435 decentralised generation, 425 deferrable loads, 439 depth of discharge, 486, 491 desalination cost estimation examples, 529–31 Libya, 529 Milos island, Greece, 530–1 Morocco, 529–30 Spain, 529 economic considerations in RES-based desalination plants, 527–31 parameters affecting desalination economics, 527 wind-based desalination plant cost items, 528 energy requirements, 512–16 general issues, 512–14 power requirements of various seawater desalination, 513 RES-based desalination, 514–16 RO seawater desalination cost structure, 513 thermal seawater desalination cost structure, 513 evaluation of various RES applications, 517 future trends, 531–2 hybrid wind energy systems, 506–31 process main classification, 509 processes and plants, 507–12 distillation desalination processes, 509–10 general consideration, 507–9 membrane/RO desalination processes, 510–12 RES and desalination methods, 515 RES integrated systems, 516, 518–25 general description, 516, 518 implementation projects, 523–4 implementation projects with hybrid energy systems, 524–5 RO–wind desalination, 518–20

wind–RO configuration possibilities, 521–3 RES-based desalination plants environmental impacts, 525–7 environmental aspects, 525–6 floating desalination plant, 526–7 seawater desalination, 530 water scarcity problem, 506–7 desalination plants, 17–18 desalination systems, 15–18 DFIG see doubly-fed induction generator Diesel and Wind Systems, 387 diesel engines, 197 comparison of available solutions for a stand-alone system, 125–6 wind–diesel hybrid system installed in Canary Islands, 431 diesel-oil price, 152 diesel-oil price annual escalation rate, 152 diesel–electric generator, 120–1, 493–4 SFC distribution, 493 discharging time, 38 distillation, 509 distributed generation, 194, 425 Distributed Utility Integration Test, 355 distribution facility deferral, 45 doubly excited induction generators, 172 doubly-fed induction generator, 193, 198, 203–4, 212 current-linked converter-based controller, 204 voltage-linked converters, 204–5 Dresser-Rand CAES modules, 397, 398 DUIT see Distributed Utility Integration Test dynamic models, 303 EC project JOR3-CT95-00700, 385 economic efficiency, 111 economic optimisation, 93 economiser, 400 Ecostar van, 349 EDL see electric double layer EDR see electrodialysis reversal effective cost coefficient, 110–11 elastomeric matrix composite, 370 electric double layer, 335 Electric Power Research Institute, 383 electric water pumps, 289 electrical energy storage, 73–4 electrical energy system, 426 electrical pump, 288 electrochemical capacitors, 335–6 electrochemical cells, 329, 332–4 actual cell potential, 332–3 cell capacity, 333


Index cell efficiencies, 333–4 coulombic efficiency, 333–4 energy efficiency, 334 voltage efficiency, 334 theoretical cell potential, 329, 332 electrochemical energy storage technologies diesel vs electrochemical power conversion, 335 energy storage, 329 application comparisons, 330 technology comparisons, 331 types, 335–62 all-vanadium redox battery, 357–61 electrochemical capacitors, 335–6 flow batteries, 352–3 fuel cells, 336–9 lead–acid battery, 339–43 Li ion battery, 344–6 metal–air battery, 346–7 nickel–metal hydride batteries, 343–4 sodium–sulphur battery, 347–50 vanadium bromide redox battery, 361–2 ZEBRA battery, 350–2 Zn/Br battery, 354–7 wind energy systems, 323–63 fundamentals of electrochemical cells, 329, 332–4 large grid-connected wind farms, 328–9 off-grid or remote power systems, 324–5 wind–diesel grids, 326–8 electrodialysis reversal, 440 electrolysis, 255–60, 487–8 electronic converter, 455 electronic devices, 6 electronic shock absorber, 336 energy efficiency, 334 energy payback, 38–9 energy ratio, 38–9 energy saving, 511 energy storage, 3–4, 5–6 energy storage capacity, 38 energy storage system characteristics, 37–42 discharging time, reaction time, storage duration, 38 efficiency, energy ratio and energy payback, 38–9 energy and power density, 40 energy storage capacity, 38 influence on the environment, 40 lifetime, number of cycles, duty cycle requirements, 41

539

other features, 41–2 rated or available power, energy to power ratio, 37 self-discharge, parasitic losses, ageing mechanisms, maximum depth of discharge, 39–40 system costs, 41 chemical energy storage, 53–9, 60 battery energy storage, 53–6 battery energy storage configuration, 54 FC-HS configuration, 59 flow batteries, 56–8 flow battery energy storage configuration, 57 fuel cells and hydrogen storage, 58–9 fuel cells types and characteristics, 60 comparison, 63–72 cycle efficiency, 71 energy and power costs, 68–9 energy and power costs of each system, 69 energy and power densities, 66 energy storage capacity vs discharge time, 64–5 environmental and safety concerns, 70–1 mass and volume energy density, 67 mass and volume power density, 67 maturity, 71–2 maturity levels, novel concepts and cost distribution, 72 power and discharge time ratings for cumulative installed capacity, 64 power, discharge time and energy capacity ratings, 63 self-discharge plotted against recommended storage period, 65 self-discharge vs recommended storage duration, 65–6 service period and number of cycles, 66–8 service period vs number of cycles, 68 useful energy and power extraction response, 70 useful energy, power extraction response and cycle efficiency, 69–70 customer service, 46–7 energy management or peak shaving or demand charge reduction, 46 power quality and reliability, 46 renewable energy, 46–7


540

Index

description, 32–42 energy flows, 36 energy generation management and frequency-voltage control, 35 main components, 32–5 operation principle and energy flows, 35–7 typical energy storage configuration, 33 electricity applications requirements, 47–9 alternatives, 49 duty cycle, portability and space requirements, 48 power output and discharge period requirements, 47 future trends, 72–4 chemical energy storage, 73 electrical energy storage, 73–4 mechanical energy storage, 73 generation, 42–4 application areas, 43 area control and frequency responsive reserve, 44 commodity storage or load levelling or arbitrage, 44 rapid, spinning or contingency reserve, 43 mechanical energy storage, 50–3 CAES configuration, 51 compressed air energy storage, 51–2 flywheel energy storage configuration, 53 flywheels, 52–3 PHS configuration, 50 pumped hydro storage, 50–1 renewable energy systems, 29–32 benefits from energy storage adoption, 31 contemporary ESSs, 49–50 critical role, 32 supercapacitors, 62–3 configuration, 62 superconducting magnetic energy storage, 61–2 configuration, 61 transmission and distribution, 44–6 distribution facility deferral, 45–6 transmission facility deferral, 45 transmission system stability, 44–5 transmission voltage regulation, 45 energy systems models, 82 EPRI see Electric Power Research Institute ESA see electronic shock absorber ESS see energy storage system

European Commission DirectorateGeneral for Energy and Transport, 481 European Fund for Regional Development, 527 ex-works price, 105, 106, 107 excess energy, 241–2 Exxon Research and Engineering Corporation, 353 FAEFEDU see floating autonomous environmentally friendly and efficient desalination unit FESS see flywheel energy storage systems fibre optic cable, 442 filament-winding process, 370 floating autonomous environmentally friendly and efficient desalination unit, 526 floating desalination plant, 526–7 flooded-type lead–acid battery, 341 schematic, 340 flow battery, 56–8, 352–3 see also specific flow battery flow cell stack configuration, 353 fluid flow machines, 289 Flysafe project, 377 flywheel, 166, 194 design and construction, 368–74 bearing types, 372–3 containment technology, 374 power interface, 374 rotor configurations and construction, 370–2 rotor materials specific strength, 369 theory of energy storage using flywheels, 368–9 flywheel energy storage systems, 486 application, 383–90 autonomous wind power systems, 384–6 commercial developments and tools, 386–7 installations, 387–8 wind power in weak grid applications, 383–4 configuration, 487 cutaway/exploded view Active Power steel flywheel, 371 Beacon Power composite flywheel, 373 PowerStore containerised building, 387 UPT composite flywheel, 372 EC projects including flywheel R&D, 390


Index engineers inspect PowerStore flywheel at Ross Island, 388 experimental wind–diesel–flywheel system, 385 features and limitations, 375–7 cycling service and lifetime, 376 energy and power density, 375 energy losses, 376 environmental and safety considerations, 376–7 key features, 375 system performance, 375 flywheel design and construction, 368–74 bearing types, 372–3 containment technology, 374 power interface, 374 rotor configurations and construction, 370–2 rotor materials specific strength, 369 theory of energy storage using flywheels, 368–9 key advantages, 383 sources of further information, 389 technology status, 377–82 commercial products, 377–9 research and development, 380–2 steel vs composite rotors, 379 storage technology cost, 379–80 wind energy systems, 366–90 Flywheel Safety and Containment Consortium, 377 flywheels, 52–3 Ford Research Laboratories, 349 Francis turbine, 292 fuel cells, 271, 336–9, 487–8 energy storage system, 488 hydrogen fuel cell schematic, 336 operating principles, 257 reactions, 256 technologies comparison, 338 theoretical efficiency, 257–8 types, 256 types and characteristics, 60 fuel cells and hydrogen storage, 58–9 fuel consumption, 235 fuel mass rate, 493 furling, 177 G2 V/Br see vanadium bromide redox battery gas microturbines, 429, 431–2 gear ratio, 196 GECOL, 524 gel electrolyte, 339 General Electric LMS100, 402

541

generation 2 vanadium bromide redox cell, 361 generators, 170–3, 197–8 friction and cogging torque, 172–3 heat issues, 178–9 overspeeding, 181 speed, 171 types, 171–2 Geographic Information System, 304 Gibbs free energy, 329 Gibbs free energy equation, 257 GIS see Geographic Information System glass microfibre separator, 342 global radiation, 454 greenhouse gas emissions, 40 grid extension, 125 grid power, 525 grid-connected WHPS, 283–4 gross production, 242 guyed towers, 174, 485 H-APS see hydrogen autonomous power systems H-SAPS see hydrogen stand-alone power systems Hellenic Statistical Agency, 496 HEV see hybrid electric vehicle HEW station income, 110 HEW systems see hybrid electricity generation wind-based systems high-pressure air turbine, 398 high-speed flywheels, 367 high-temperature superconductor, 373, 382 high-voltage direct current, 408 HOGA see Hybrid Optimisation by Genetic Algorithms HOMER see Hybrid Optimisation Model for Electric Renewables horizontal axis wind turbine, 294 HTS see high-temperature superconductor HTS bearing, 380 HVDC see high-voltage direct current hybrid electric vehicle, 381 hybrid electricity generation wind-based systems, 102 case studies analysis, 121–45 Aegean sea wind potential map, 121 annual wind potential characteristics of analysed remote areas, 124 electricity generation cost, 141–5 electricity production cost values, 143, 144 impact of PV panels on total cost, 133–6


542

Index

impact of system reliability, 132 Kithnos stand-alone system main parameters, 134 life cycle hybrid system minimum electricity production cost vs annual diesel-oil consumption, 144 life-cycle cost analysis, 134 maximum battery size reduction, 136 operational years’ impact on total cost, 127–9, 137–41 optimum stand-alone wind power system dimensions, 129, 138 reliability impact on total cost, 129–32 comparison of available solutions for a stand-alone system, 125–7 diesel engine use, 125–6 grid extension solution, 125 total cost comparison of electrification solutions, 126 wind-energy based stand-alone solution, 126–7 cost benefit analysis, 109–12 electricity generation cost, 114–15 first installation cost, 104–7 proposed autonomous HEW system, 104 specific price of existing PV installations, 106 impact on electricity production cost diesel-oil current price impact, 152 diesel-oil price annual escalation rate, 152, 153 investment turnkey price, 154 return on investment index, 151 wind potential, 150 maintenance and operation cost, 107–9 no-load rejection configuration basis of minimum 10 year cost, Andros, 146 basis of minimum 10 year cost, Kea, 146 basis of minimum 10 year cost, Kithnos, 128 basis of minimum 20 year cost, Kithnos, 129 basis of minimum initial cost, Kithnos, 127 battery technology improvement incorporated, 148 zero initial cost subsidisation, 147 reliability impact-loss of load cost, 112–14 sensitivity analysis of financial behaviour, 145–54

central values of main parameters, 149 impact of wind potential, 149–51 installation turnkey cost, 153–5 monthly average wind speed values, 150 return on investment index, 151 stand-alone system, 149–54 wind-only stand-alone system, 145–8 zero load configuration, 149 socio-environmental impacts, 115–21 battery bank, 119–20 diesel-electric generator, 120–1 electric and electronic equipment, 120 photovoltaic generator, 118–19 wind turbine, 115–18 stand-alone system configuration and 10 years total cost relation Andros, 130 Kithnos, 131 wind potential data Andros island, 122 Kea island, 123 Kithnos island, 123 wind-diesel hybrid system energy autonomous configuration, Andros, 137 mean annual cost comparison, 140 ten-year cost analysis, Andros, 138 ten-year electricity production cost, Andros, 142 total 10 year cost analysis, 141 twenty-year cost analysis, Andros, 139 twenty-year electricity production cost, Andros, 143 hybrid micro-grids advantages and limitations, 461–2 control and monitoring, 437–42 load control, 437–40 remote micro-grid control and monitoring system diagram, 441 strategies, 440–2 design and construction, 442–9 analysis of demand, 443–4 construction process diagram, 450 factors which condition micro-grid design, 448 hybrid energy system designing and installation, 448–9 mean daily power consumption curves, 445 mean monthly wind speeds and solar irradiation, 447


Index power consumption seasonal variation, 444 resources estimation, 444–7 solar irradiation and air temperature mean daily evolution, 447 wind speed mean daily evolution, 446 electrical energy system configuration and structure, 426 future trends, 462–3 isolated micro-grids technological components, 429–35 gas microturbines, 431–2 gas turbine conceptual outline, 432 power electronic converters, 433–5 reciprocating internal combustion engines, 431 Stirling engines, 432–3 modelling and simulation, 450–7 diesel generator set fuel consumption, 456 dynamic models, 456–7 probabilistic models, 457 PV panel I–V curves, 453 wind turbines characteristic power– speed curves, 452 optimising integration, 457–61 considerations on the emission of pollutants, 459–60 economic criteria for selection between alternatives, 458 software packages, 461 system control strategies, 460–1 system reliability evaluation, 458–9 options, 427–9 micro-grid architecture interconnected with centralised control, 428 normal interconnected hybrid microgrids, 427–8 stand-alone hybrid micro-grids, 428–9 quasi-dynamic models, 451–5 battery modelling, 454–5 conventional generator modelling, 455 electronic converter modelling, 455 hydraulic turbine modelling, 455 PV panel modelling, 452–4 wind turbine modelling, 451–2 renewable energy systems integration into remote micro-grids, 425–63 stand-alone hybrid micro-grids architectures, 435–7 modular centralised AC bus architecture, 438

543

modular centralised DC bus architecture, 437 modular distributed AC bus architecture, 439 remote micro-grid architecture various configurations, 436 technologies for stand-alone microgrids, 430 Hybrid Optimisation by Genetic Algorithms, 96, 451, 461 Hybrid Optimisation Model for Electric Renewables, 90, 94–5, 263, 264, 265, 309, 386–7, 451, 461 hybrid PV–wind–battery systems, 93 hybrid RAPS system, 325 hybrid wind energy systems applications, 13–24 covering electricity needs in remote villages in Chile, 22 domestic to community level electrification, 20–4 electrification of remote fishing community in Mexico, 23 electrification of remote fishing community in Mongolia, 23 experimental unit, 20 remote cell phone base station, 15 small desalination systems, 15–18 small wind turbine adjusted on relay mast, 14 telecommunications stations, 13–15 water pumping, 18–20 water pumping unit with PV panels, 19 wind turbine for water pumping, 19 wind-driven desalination plants, 17 wind-solar hybrid street lamps, 24 desalination, 506–31 economic considerations in RESbased desalination plants, 527–31 energy requirements, 512–16 future trends, 531 processes and plants, 507–12 RES integrated systems with desalination plants, 515, 518–25 RES-based desalination plants environmental impacts, 525–7 water scarcity problem, 506–7 description, 7–11 typical hybrid wind-based stand alone system, 9 design and performance optimisation, 81–98 future trends, 97–8 scope and objectives, 81–2


544

Index

energy storage opportunities, 11–13 energy balance for a high wind potential area, 12 energy systems modelling, 82–7 optimisation models, 86–7 scope and type of energy models, 82–4 specific problem types, 84 synthesis, design and operation energy models, 84–6 types and uses of energy models, 83 future trends, 24–6 small wind turbine for electrification in Kansas, 25 with PV panels for electrification in Netherlands, 25 hybrid electricity generation windbased system feasibility assessment, 102–56 case studies analysis, 121–45 cost benefit analysis, 109–12 electricity generation cost, 114–15 first installation cost, 104–7 maintenance and operation cost, 107–9 reliability impact-loss of load cost, 112–14 sensitivity analysis of financial behaviour, 145–54 socio-environmental impacts, 115–21 hybrid power station of S.E.A. and ENVI.PRO Lab with battery storage system installation, 486 integration into buildings, 475–503 building sector characteristics, 477–8 description of hybrid energy systems in buildings, 482–9 energy consumption in buildings, 478–80 European Union facts about hybrid energy systems in buildings, 481–2 optimisation techniques, 91–4 criteria, 91–2 economic and techno-economic optimisation, 92–4 optimisation methodologies relative merits and demerits, 97 overview, 3–26 powered T/C station wind-based system at Osmussaar, Estonia, 15 wind-PV based installation at Cesme-Izmir, Turkey, 16 wind-PV based installation in Turkey, 16

software tools for simulation and optimisation, 94–6 HOGA software system, 96 HOMER software system, 94–5 hybrid simulation and optimisation tools characteristics, 94 HYBRID2 software system, 95 synthesis, design and operation, 87–91 local conditions and system selection, 88–90 optimal design, 90–1 steps for energy system development, 89 with wind-PV-hydro energy storage system, 88 with wind–PV–diesel energy storage components, 89 wind-only stand-alone system sensitivity analysis of financial behaviour, 145–8 impact of subsidy, 146–7 impact of wind potential, 145 improvement of battery technology, 147–8 local economy impact, 148 wind–diesel hybrid system for standalone electrical systems, 483 wind–photovoltaic hybrid system for stand-alone electrical systems, 484 see also specific system hybrid wind-diesel energy systems, 191–214 components, 197–9 BESS, 199 diesel engine, 197 generators, 197–8 loads, 199 wind turbine, 197 control strategies, 199–206 BESS control scheme, 206 chopper and inverter control scheme, 201 control scheme for PMSG with chopper and inverter, 200 current-linked converter-based controller for DFIG, 204 energy storage units, 205–6 induction generators, 203–5 machine-side and load-side converter, 202 PMSG control with diode rectifier and chopper, 200–1 PMSG control with voltage source inverters, 201–3 PMSG with voltage linked converters, 202


Index SCIG control scheme, 206 voltage linked converter for DFIG control, 205 design considerations, 195–6 load assessment, 196 resource assessment, 196 storage requirements, 196 future trends, 213–14 modelling and simulation, 207–11 complete SIMULINK model, 208 PMSG response for wind speed variations, 211 power flow in watts from WECS, DG and BESS, 212 power variations, 210 WECS-BESS feeding a load, 209–10 WECS-DG-BESS feeding a load, 210–11 wind turbine sizing, 194–5 wind-diesel generation system, 192–4 block schematic, 193 hybrid wind-hydrogen energy systems, 254–79 abbreviations, 281 environmental assessment, 267–72 annual emissions savings estimates, 268 estimates of future emissions, 267 hydrogen safety, 271–2 environmental impacts, 268–9 carbon dioxide emissions, 270–1 changes in atmospheric water vapour, 270 changes in oxidising capacity, 269 European level, 268 soil uptake, 270 future trends, 274, 278–9 hydrogen storage systems, 260–3 hydriding substances as hydrogen storage media, 262 hydrogen storage types and densities, 262 liquid hydrogen storage, 261 metal hydride storage, 261 novel methods, 262–3 isolated systems step by step design, 264–5 emission estimates calculation, 265 hydrogen electricity in SAPS, 265 hydrogen-fuelled vehicles, 265 load profile, 264 sustainable energy technologies integration, 264 wind energy calculations, 264

545

market potential and barriers, 272–4 H-SAPS project results, 273 hydrogen in APS, 274 main strengths, weaknesses, opportunities and threats, 275–7 SWOT analysis, 274 optimisation, 263–7 annual electrical energy production, 266 case study: Karpathos, 265–7 electrical energy production and consumption, 267 Karpathos’s monthly average grid energy demands, 264 wind electrolysis production systems, 255–60 alkaline electrolytes, 255 applications, 259–60 critical issues, 258–9 electrochemical electricity generation, 256–8 fuel cell reactions, 256 fuel cells operating principles, 257 PEM electrolyser, 255 SOEC, 255 hybrid wind-photovoltaic energy systems, 216–48 case studies, 242–6 battery SOC hourly variation, 246 house electrical load, 243 monthly energy balance, 247 optimal sizing, 245 system configurations and energy levelised cost for LLPS, 244 design and configuration, 226–7 diagram of PV-wind-engine generator hybrid system, 226 PV-wind-engine generator hybrid system, 226–7 future trends, 247 modelling and simulation, 227–39 auxiliary engine generator, 234–6 battery storage, 231–3 cell temperature on m-Si PV array deficiency, 228 cost calculation, 237–9 cost calculation methodology, 237 DC/AC and AC/DC converters, 233–4 energy management and control unit, 236 I-V and P-V curves of PV module, 227 I-V curves for BP585F PV module, 231


546

Index

methodology and input and output data, 241 PV module equivalent circuit, 229 PV system, 227–30 small and medium wind turbines, 230–1 small wind turbine, battery, engine generator and inverter specific cost, 238 specific consumption of engine generator and fuel consumption variation, 235 WECS power curve, 232 nomenclature, 251–3 renewable energy resources and their potential, 216–26 direct normal spectral irradiance, 217 ground reflected radiation, 218 panel inclination on monthly mean values of solar irradiations, 220 position of the sun and solar diagram, 220 renewable resources complementarity, 223–6 solar and wind energy monthly variation, 225 solar energy resource, 217–19 tilted solar irradiances, 219 variability, 223 Weibull distribution, 222 wind and solar energy daily repartition, 225 wind distribution and wind turbine power curve, 221 wind energy resource, 219–23 sizing and optimisation, 239–42 excess energy, solar fraction and gross production, 241–2 methodology, 239–40 sizing optimisation, 242 hybrid wind–hydropower energy systems, 282–319 benefits and limitations, 310–14 combined wind and hydropower, 313–14 hydro energy applications, 312–13 wind energy applications, 310–11 different operational policies and techniques for isolated grids, 314–15 different types, 284–302 efficiency of Francis, Pelton and Kaplan turbines, 294 medium and large wind/hydropower generation systems, 298–300

micro, mini and small hydropower systems integration, 290–8 mini-hydro power station, 293 power coefficients of wind rotors, 295 pumped storage systems, 300–2 small wind turbine vs piston pump and centrifugal pump, 289 water pumping systems, 285–90 wind pumping system and storage tank, 287 wind–hydro pumped storage system, 302 economics, 316–18 models and tools used to evaluate different renewable energy technologies, 317 environmental impacts, 315–16 electric power generation technologies, 305 generation, investment and external costs for various power generation technologies, 315 models classification dynamic models, 303 logistical models, 303 research and development, 302–10 computational tools and software, 303–4 literature review, 304–10 the need to couple wind–hydropower systems, 283–4 HYBRID2, 90, 95, 451, 461 HYBRIDS, 451 hydraulic turbine modelling, 455 hydro energy advantages, 312 disadvantages, 312–13 Hydro Tasmania, 327, 359 hydro turbines, 291–3, 486 impulse turbine, 291–2 cross-flow turbine, 292 Pelton wheel, 291–2 Turgo wheel, 292 reaction turbine, 292–3 Francis turbine, 292 kinetic energy turbine, 293 propeller turbine, 292 hydrogen liquefaction, 261 production methods, 337, 339 safety, 271–2 hydrogen autonomous power systems, 268, 273, 274 hydrogen energy density, 337


Index hydrogen stand-alone power systems, 265, 273 hydrogen storage systems, 260–3 hydriding substances as storage media, 262 liquid hydrogen storage, 261 metal hydride storage, 261 novel methods, 262–3 storage types and densities, 262 hydrostatic transmission system, 385 ICE see internal combustion engine IGBT technology see insulated-gate bipolar transistor technology Indianapolis Power & Light, 346 induction generators control schemes, 203–5 DFIG control using current linked converters, 203–4 DFIG control using voltage-linked converters, 204–5 SCIG, 205 initial investment cost, 105 INSEL, 451 institutional sector, 479–80 Instituto Tecnologico de Canarias, 524 insulated-gate bipolar transistor devices, 380 insulated-gate bipolar transistor technology, 374 Integrated Power and Water Point 9, 529 integrated synchronous generator, 374 internal combustion engine, 337 internal rate of return, 112 inverter, 178, 434, 489 IPL see Indianapolis Power & Light IRR see internal rate of return ISG see integrated synchronous generator ITC see Instituto Tecnologico de Canarias J-Power, 360 Job and Economic Development Impact, 304 Kalbarri, 388 Kaplan turbine, 292 kinetic energy turbine, 293 Koeppen climate classification, 478 Kyushu Electric Power company, 355 Laplace transform method, 456 large wind–hydropower systems, 283 lattice towers, 174 Lawrence Livermore National Laboratory, 367, 380

547

LCC see life-cycle costing LCE see levelised cost of energy lead–acid battery, 5, 54–5, 135, 185, 231, 339–43 electrode reactions, 340–1 energy storage demonstrations, 342–3 flooded-type lead–acid batteries limitations, 341 storage systems, 343 UltraBattery, 342 valve regulated lead–acid batteries, 341–2 levelised cost of energy, 92, 458 levelised production cost, 316–18 levelised water cost, 530 Li ion battery, 56, 344–6 applications, 345–6 cell reactions, 345 schematic, 345 life-cycle costing, 458 lightning, 182–3 linear programming, 91 liquid hydrogen storage, 261 LLNL see Lawrence Livermore National Laboratory LLP see loss of load probability load levelling, 44 load operating network-bus, 442 loads, 199 logistical models, 303 LOLP see loss of load probability LON-bus see load operating network-bus loss of load probability, 91, 240, 459 loss of power supply probability, 91, 92, 240, 459 low-pressure air turbine, 398 low-speed flywheels, 367 low-voltage network, 426 LPSP see loss of power supply probability LWC see levelised water cost Macintosh plant, 397 combustion turbine, expander and motor/generator, 399 compressor train, clutch and motor/ generator, 398 MATLAB, 307, 309, 457 MATLAB/SIMULINK, 207 maximum depth of discharge, 39 maximum power point tracking, 171, 181–2, 192, 227 MCFC see molten carbonate fuel cells McMurdo Station, 388 mechanical energy storage, 50–3, 73 mechanical vapour compression, 530


548

Index

MED see multi-effect distillation medium-power systems, 283 metal hydride storage, 261 metal–air battery, 56, 346–7 cell reactions, 347 Zn-air battery charge and discharge operation, 347 MG4520 200 W wind turbine, 170 MGCC see micro-grid central controller micro-grid central controller, 428, 441 micro-grids, 426 construction process diagram, 450 factors which condition the micro-grid design, 448 micro-wind turbines, 180 micropower optimisation model, 263 micropower system, 284 Milos desalination plant, 527 MILP see mixed-integer linear programming mini power system, 284 MINLP see mixed-integer non-linear programming mixed-integer linear programming, 85 mixed-integer non-linear programming, 85 molten carbonate fuel cells, 256 Monte Carlo simulation, 310, 459 MPPT see maximum power point tracking multi-effect distillation, 509 multistage flash distillation, 509 MVC see mechanical vapour compression Na–NiCl2 battery, 351 National Renewable Energy Laboratory, 386, 481 NEDO see New Energy Development Organisation Nernst equation, 332 net present cost, 92, 458 New Energy Development Organisation, 360 NGK Insulators Ltd, 349 nickel, 351 nickel–cadmium battery, 55, 343 nickel–metal hydride battery, 343–4 electrode reactions, 343–4 schematic, 344 no-energy fulfilment cost, 113 NOCT see normal operating cell temperature noise emission, 116 non-controlled rectifiers, 434 normal operating cell temperature, 230, 454

NPC see net present cost NREL see National Renewable Energy Laboratory Nuclear Energy Agency, 316 open-circuit voltage, 227 Otto engines, 431 oxygen recombination cycle, 340 PAFC see phosphoric acid fuel cells parasitic losses, 39 PCC see point of common coupling PCS see power conditioning system Pelton wheel, 291–2 PEM see proton exchange membrane PEM electrolyser, 255 PEMFC see proton exchange membrane fuel cells permanent magnet generators, 171, 173 permanent magnet synchronous generator, 193, 198, 212 control scheme, 200 control with diode rectifier and chopper, 200–1 control with voltage source inverters, 201–3 with voltage linked converters, 202 permeate water, 511 petroleum, 394 phosphoric acid fuel cells, 256 photovoltaic generator, 135 hybrid electricity generation windbased systems socio-environmental impacts, 118–19 photovoltaic panel I–V curves, 453 modelling, 452–4 photovoltaic power station, 494 photovoltaic system, 217, 227–30 PHS see pumped hydro storage Piller flywheel, 370 piston pump, 288 pitching, 176, 177 PMSG see permanent magnet synchronous generator point of common coupling, 427 polyethylene microporous separators, 339–40 polysulphide bromide battery, 58 power circuit, 433–4 power conditioning system, 355 power curve, 5 power demand, 7, 10 power electronic converter, 432, 433–5 basic functions, 434


Index power electronic equipment block diagram, 433 power electronics technology, 380 power output, 186 power quality systems, 367 power reliability analysis, 91–2 Powerbridge, 378, 386 Powercorp, 386, 387 PowerStore Flywheel System, 166, 388 cutaway view of PowerStore containerised building, 387 energy storage system block diagram, 386 Premium Power Corporation, 355 probability density function, 457 propeller turbine, 292 proton exchange membrane, 255 proton exchange membrane fuel cells, 256 pulse width modulated bi-directional converter, 374 pulse width modulation, 178 pump-hydro systems, 486, 492–3 pump-hydro solution for isolated consumers, 487 pumped hydro storage, 50–1 PV system see photovoltaic system pyranometer, 446 pyroheliometer, 446 rapid reserve, 43 RAPS see remote area power supply RAPSIM, 451 reciprocating internal combustion engines, 429, 431 recombinant battery see valve regulated lead–acid battery rectifiers, 434 Red–Dead project, 518 redox flow battery, 356–7 Regional Energy Deployment System, 304 reliability, 458 remote area power supply, 324–5 Remote Water Pumping and Electric Power Generation with Renewable Energy, 290 renewable energy sources fraction, 241 renewable energy sources systems, 8, 29, 266 and desalination methods, 516 desalination economic considerations, 527–31 cost item of wind-based desalination plant, 528 parameters affecting economics, 528

549

desalination plants environmental impacts, 525–7 environmental aspects, 525–6 floating desalination plant, 526–7 energy storage technologies overview, 29–74 chemical energy storage, 53–9 comparison of energy storage systems, 63–72 contemporary energy storage systems, 49–50 customer service, 46–7 electricity applications requirements, 47–9 future trends, 72–4 generation, 42–4 mechanical energy storage, 50–3 supercapacitors, 62–3 superconducting magnetic energy storage, 61–2 transmission and distribution, 44–6 typical energy storage system, 32–42 evaluation in desalination applications, 517 general description, 516, 518 implementation projects, 523–4 implementation projects with hybrid energy systems, 524–5 integrated with desalination plants, 516, 518–25 integration into remote micro-grids, 425–63 architecture for stand-alone hybrid micro-grids, 435–7 control and monitoring of hybrid micro-grids, 437–42 design and construction of hybrid micro-grids, 442–9 future trends, 462–3 hybrid micro-grid options, 427–9 hybrid micro-grids advantages and limitations, 461–2 isolated micro-grids technological components, 429–35 modelling and simulation of hybrid micro-grids, 450–7 optimising integration of hybrid micro-grids, 457–61 RO–wind desalination, 518–20 design issues, 519–20 operational issues, 520 wind–RO configuration possibilities, 521–3 near constant operating conditions, 521 RO unit switching, 522


550

Index

storage devices, 521–2 systems with back-up, 521 systems without back-up, 521 variable operating conditions, 522–3 wind turbine de-rating, 522 Renewable Islands, 278 RES see renewable energy sources RESF see renewable energy sources fraction residential energy consumption, 478 residential sector, 478–9 resin-transfer moulding process, 371 RETscreen, 187 reverse osmosis system, 440, 509, 530 advantages, 512 subsystems, 510 unit flow sheet, 511 see also wind–RO desalination RICE see reciprocating internal combustion engines Ridge Energy, 403–4 RO see reverse osmosis system rotor diameter, 196, 207 round-trip efficiency, 38, 39 RTM see resin-transfer moulding process Rutland 910 turbine, 172 RWE-Chloride, 349 S & C Electric Company, 336 Sandia model, 380 SAPS see stand-alone power systems Satcon Power Systems, 356 SCADA see supervisory control and data acquisition SCIG see squirrel cage induction generators Scott Base diesel power system, 388 sealed lead–acid battery, 342 seawater reverse osmosis, 511 seawater reverse osmosis desalination powered from renewable energy sources, 524 SEI see Sumitomo Electric Industries self-discharge, 39 self-supporting tower, 485 self-utilisation factor, 110 services’ sector, 479–80 SFC see specific fuel cell consumption short-circuit current, 227 Silent Power, 349 silicon semiconductors, 377 Simulink, 457 sinusoidal pulsed width modulated inverter, 200 small wind turbine

comparative costs with PV electricity, 168 generator, 170–3 friction and cogging torque, 172–3 types, 171–2 wind and generator speed, 171 other features, 173 overspeed protection, 176–7 starting and low wind speed behaviour, 175 stopping the turbine, 177 tail fins and yaw behaviour, 173–4 towers, 174–5 technology, 170–7 vs large turbines, 173 small wind–hydropower systems, 283 Smart Energy 25 flywheel, 378 SMES see superconducting magnetic energy storage sodium, 347 sodium chloride, 351 sodium–sulphur battery, 55, 347–50 cell reactions, 349 development and commercialisation, 349–50 recent installation plans, 350 schematic, 348 SOEC see solid oxide electrolysis cells SOFC see solid oxide fuel cells Soft Energy Applications and Environmental Protection Lab, 496 solar energy, 223 solar fraction, 241–2 solar irradiation, 218 solar position, 218 solar radiation, 217 solid beta alumina ceramic electrolyte, 348 solid oxide electrolysis cells, 255 solid oxide fuel cells, 256 SOLSIM, 451 SOMES, 451 specific fuel cell consumption, 493 spinning reserve, 43 squirrel cage induction generators, 172, 198 control schemes, 203, 205, 206 stand-alone power systems, 183–5, 265 stand-alone systems, 283–4 stand-alone towers, 174 stand-alone wind energy systems, 165–89 applications, 13–24 domestic to community level electrification, 20–4


Index experimental unit, 20 remote cell phone base station, 15 small desalination systems, 15–18 small wind turbine adjusted on relay mast, 14 telecommunications stations, 13–15 water pumping, 18–20 wind turbine for water pumping, 19 wind-driven desalination plants, 17 wind-solar hybrid street lamps, 24 control and electronics, 177–83 controller, 178 controller protection, 182 current limiting, 179–80 generator heat issues, 178–9 generator overspeeding, 181 inverter, 178 lightning damage, 182 lightning strikes, 182–3 maximum power point tracking, 181–2 thermal characteristics, 180 unforeseen conditions, 183 description, 4–7 energy storage, 5–6 inverter efficiency curve, 7 operational modes, 7 system electronic devices, 6 typical wind-energy stand-alone system, 5 wind turbine, 4–5 design and performance optimisation, 81–98 future trends, 97–8 scope and objectives, 81–2 energy systems modelling, 82–7 optimisation models, 86–7 scope and type of energy models, 82–4 specific problem types, 84 synthesis, design and operation energy models, 84–6 types and uses of energy models, 83 future trends, 24–6 small wind turbine for electrification in Kansas, 25 with PV panels for electrification in Netherlands, 25 HEW system feasibility assessment, 102–56 case studies analysis, 121–45 cost benefit analysis, 109–12 electricity generation cost, 114–15 first installation cost, 104–7 maintenance and operation cost, 107–9

551

reliability impact-loss of load cost, 112–14 sensitivity analysis of financial behaviour, 145–54 socio-environmental impacts, 115–21 HEW system sensitivity analysis of financial behaviour battery ex-works price, 153 current diesel-oil price, 152 diesel-oil price annual escalation rate, 152 impact of wind potential, 149–51 installation turnkey cost, 153–5 return on investment index, 151 integration into buildings, 475–503 building sector characteristics, 477–8 description of hybrid energy systems in buildings, 482–9 energy consumption in buildings, 478–80 European Union facts about hybrid energy systems in buildings, 481–2 modelling and simulation, 169 overview, 3–26 power systems, 183–5 basic electrical system, 184 wind turbine with PV in Nepal, 184 powered T/C station wind-based system at Osmussaar, Estonia, 15 wind-PV based installation at Cesme-Izmir, Turkey, 16 wind-PV based installation in Turkey, 16 PowerStore Flywheel System, 166 pre-feasibility analysis, 168–9 small wind photovoltaic system, 167 small wind turbine and PV electricity comparative costs, 168 small wind turbine technology, 170–7 fully furled wind turbine, 176 generator, 170–3 ironless stator between permanent magnet rotors, 172 measured wind turbine power curve, 170 other features, 173 simple permanent magnet generator, 171 system sizing, 185–8 battery sizing, 188 power output, 186 practical wind energy measurement, 187 wind maps and software, 186–7


552

Index

wind speed probability distribution and capacity factor, 187 STATCOM, 388 state of charge, 232 Stirling engines, 429, 432–3 storage system unit, 485–8 battery storage systems, 485–6 electrolysis–fuel cell storage systems, 487–8 flywheel storage systems, 486 pump-hydro systems, 486 straflo turbine, 292 strength, weakness, opportunities and threats, 274, 275–7 Subaru wind farm, 359 sulphur, 348 sulphuric acid, 340 Sumitomo Electric Industries, 359, 360 supercapacitors, 62–3 superconducting magnetic energy storage, 426 supervisory control and data acquisition, 442 SWADES, 524 SWOT see strength, weakness, opportunities and threats SWOT analysis, 274 SWRO see seawater reverse osmosis SWRO-RES see seawater reverse osmosis desalination powered from renewable energy sources synchronous generators, 171, 193 system cost analysis, 92 tail fins, 173–4 techno-operational optimisation, 93 telecommunications, 13–15 TEPCO see Tokyo Electric Company tetrachloroaluminate electrolyte (NaAlCl4), 351 thermal desalination, 509 thermal energy storage system, 400 thermometer, 446 tilt-down tower, 485 tip speed ratio, 170, 197 TML see transmission line losses TMLC see transmission line cost Tokyo Electric Company, 349 Tomomae wind farm, 360 towers, 174–5 see also specific type of tower transmission facility deferral, 45 transmission line cost, 411 transmission line losses, 411 Trinity Flywheel, 367 TSR see tip speed ratio

tube turbine, 292 tubular towers, 174 Turgo wheel, 292 turnkey cost, 153–5 UltraBattery, 342 uninterruptible power supply, 339, 367, 378, 489 unmet load, 91 UPS see uninterruptible power supply Urenco Power Technologies (UPT), 371, 373 US Flywheel Safety and Containment Program, 381 V-Fuel Pty Ltd, 361 valve regulated lead–acid battery, 341–2 vanadium bromide redox battery, 361–2 cell reactions, 361 development, 361–2 vanadium redox battery, 57–8, 357–61 cell reactions, 357–8 demonstrations and commercialisation, 359 early prototype, 358 flow cell concept, 357 stacks and electrolyte tanks at King Island G1 VB installation, 360 Tomomae wind farm on Japanese Island of Hokkaido, 360 unique features, 358–9 wind energy demonstrations, 359–61 King Island, Tasmania, Australia, 359–60 Subaru project, Hokkaido, Japan, 360–1 vapour compression distillation, 509 vertical axis wind turbine, 294 Verve Energy, 387 Vestas V-27/225, 407, 409, 410 Vestas V52, 359 voltage efficiency, 334 voltage-current equation, 229 volumetric energy density, 337 G1 VB see vanadium redox battery WALC see wind array levelised cost water pumping systems, 18–20, 285–90 water recovery ratio, 511 WEC see wind energy conversion WECS see wind energy conversion systems Weibull distribution, 221, 416, 490 calculation method, 222 Weibull probability density function, 407


Index WHPS see wind–hydropower systems wind array levelised cost, 411 wind electrolysis applications, 259–60 grid-assisted wind-hydrogen generation, 259 grid-independent integrated windhydrogen energy systems, 260 grid-independent wind-hydrogen generation, 259 integrated wind-hydrogen utility energy system, 260 wind power for grid-electricity and hydrogen generation, 259–60 production systems, 255–60 critical issues for wind-hydrogen systems, 258–9 fuel cells, 256–8 wind energy, 187, 219–23, 264 advantages, 310–11 baseload plant parameters, 413 disadvantages, 311 seasonal storage, 415–16 wind energy conversion systems, 191–2, 230–1, 524 power curve, 232 power flow in watts, 212 WECS–BESS feeding a load, 209–10 WECS–DG–BESS feeding a load, 210–11 wind energy systems compressed air energy storage technologies, 393–419 current status and future progress, 396–403 Ridge Energy wind CAES study, 403–4 wind integration issues, 404–18 daily wind power and load profiles, 326 electrochemical energy storage technologies, 323–63 fundamentals of electrochemical cells, 329, 332–4 large grid-connected wind farms, 328–9 off-grid or remote power systems, 324–5 types, 335–62 wind–diesel grids, 326–8 flywheel energy storage technologies, 366–90 application, 383–90 design and construction, 368–74 features and limitations, 375–7 technology status, 377–82 hybrid RAPS system, 325

553

King Island, Australia location, 327 power system load profile, 328 G1 VB stacks and electrolyte tanks, 360 wind–diesel hybrid system installed in Canary Islands, 431 wind generator, 294 wind maps, 186–7 wind power, 259–60, 284 wind speed, 187, 194 wind turbine, 4–5, 197, 294 arrays and transmission systems, 408–12 arrays, transmission, and CAES, 412–16 characteristic power–speed curves, 452 HEW systems socio-environmental impacts, 115–18 modelling, 451–2 non-dimensionalised power curve, 491 wind turbine capacity factor, 406–8 wind turbine efficiency, 409 wind turbine levelised cost, 407 wind turbine rated power, 489–90 wind velocity, 194–5, 196 wind-diesel hybrid stand-alone system impact on electricity production cost battery bank purchase price impact, 154 diesel-oil price annual escalation rate, 153 investment turnkey price, 154 return on investment index, 151 wind potential, 150 wind-diesel-battery HEW stand-alone system, 145 wind-generated electricity, 419 wind-hydrogen systems see hybrid wind-hydrogen energy systems wind–diesel grids, 326–8 wind–diesel system, 385 wind–hydropower systems, 283–4 advantages, 282, 313 disadvantages, 313–14 see also hybrid wind–hydropower energy systems wind–RO desalination, 518–20 configuration possibilities, 521–3 near constant operating conditions, 521 RO unit switching, 522 storage devices, 521–2 systems with back-up, 521 systems without back-up, 521 variable operating conditions, 522–3 wind turbine de-rating, 522


554

Index

design issues, 519–20 operational issues, 520 windmill, 294 WTLC see wind turbine levelised cost yaw, 173–4 Z-BESS see zinc-bromine energy storage system ZBB Energy Corp, 355 zero emissions battery research activity battery, 350–2 applications, 352

cell reactions, 351–2 schematic, 351 zero-diesel solution, 500 zero-energy buildings, 481 zinc, 346, 347 zinc-bromide battery, 58, 354–7 250 kW h string of five modules in standard shipping container, 356 cell reactions, 354–6 development and commercialisation, 355–6 schematic, 354 zinc-bromine energy storage system, 355


Stand-alone and Hybrid Wind Energy Systems: Technology, Energy Storage and Applications (Woodhead Publishing Series in Energy)

Energy Storage

Wind Energy: Renewable Energy and the Environment

Wind Energy: Renewable Energy and the Environment

Wind Energy: Renewable Energy and the Environment

Wind-Diesel and Wind Autonomous Energy Systems

Wind Energy

Wind Energy Systems: Optimising Design and Construction for Safe and Reliable Operation (Woodhead Publishing Series in Energy)

Sustainable Energy Systems and Applications

Sustainable Energy Systems and Applications

Materials for Energy Efficiency and Thermal Comfort in Buildings (Woodhead Publishing Series in Energy)

Energy Pricing: Economics and Principles (Energy Systems)

Introduction to Wind Energy Systems: Basics, Technology and Operation (Green Energy and Technology)

Energy Harvesting: Solar, Wind, and Ocean Energy Conversion Systems (Energy, Power Electronics, and Machines)

Optimization in the Energy Industry (Energy Systems)

Advances in Wind Energy Conversion Technology

Thermal Energy Storage: Systems and Applications, Second Edition

Wind energy: handbook

Wind Energy Engineering

Wind Power (Energy Today)

Urban Wind Energy

Wind Energy Handbook

Energy Simulation in Energy Simulation

Wind Energy Systems for Electric Power Generation (Green Energy and Technology)

Energy

Energy

Energy

Energy: Production, Conversion, Storage, Conservation, and Coupling (Green Energy and Technology)

Encyclopedia of Energy (Encyclopedia of Energy Series)

Encyclopedia of Energy (Encyclopedia of Energy Series)

Encyclopedia of Energy (Encyclopedia of Energy Series)

Stand-alone and Hybrid Wind Energy Systems: Technology, Energy Storage and Applications (Woodhead Publishing Series in Energy)

Energy Storage

Wind Energy: Renewable Energy and the Environment

Wind Energy: Renewable Energy and the Environment

Wind Energy: Renewable Energy and the Environment

Wind-Diesel and Wind Autonomous Energy Systems

Wind Energy

Wind Energy Systems: Optimising Design and Construction for Safe and Reliable Operation (Woodhead Publishing Series in Energy)

Sustainable Energy Systems and Applications

Sustainable Energy Systems and Applications

Materials for Energy Efficiency and Thermal Comfort in Buildings (Woodhead Publishing Series in Energy)

Energy Pricing: Economics and Principles (Energy Systems)

Introduction to Wind Energy Systems: Basics, Technology and Operation (Green Energy and Technology)

Energy Harvesting: Solar, Wind, and Ocean Energy Conversion Systems (Energy, Power Electronics, and Machines)

Optimization in the Energy Industry (Energy Systems)

Advances in Wind Energy Conversion Technology

Thermal Energy Storage: Systems and Applications, Second Edition

Wind energy: handbook

Wind Energy Engineering

Wind Power (Energy Today)

Urban Wind Energy

Wind Energy Handbook

Energy Simulation in Energy Simulation

Wind Energy Systems for Electric Power Generation (Green Energy and Technology)

Energy

Energy

Energy

Energy: Production, Conversion, Storage, Conservation, and Coupling (Green Energy and Technology)

Encyclopedia of Energy (Encyclopedia of Energy Series)

Encyclopedia of Energy (Encyclopedia of Energy Series)

Encyclopedia of Energy (Encyclopedia of Energy Series)

Recommend Documents