Cover image for Market operations in electric power systems : forecasting, scheduling, and risk management
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Market operations in electric power systems : forecasting, scheduling, and risk management
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New York : John Wiley & Sons, 2002
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9780471443377
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30000010019849 HD9685.U5 S43 2002 Open Access Book Book
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30000010019850 HD9685.U5 S43 2002 Open Access Book Book
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Summary

Summary

An essential overview of post-deregulation market operations inelectrical power systems

Until recently the U.S. electricity industry was dominated byvertically integrated utilities. It is now evolving into adistributive and competitive market driven by market forces andincreased competition. With electricity amounting to a $200 billionper year market in the United States, the implications of thisrestructuring will naturally affect the rest of the world.

Why is restructuring necessary? What are the components ofrestructuring? How is the new structure different from the oldmonopoly? How are the participants strategizing their options tomaximize their revenues? What are the market risks and how are theyevaluated? How are interchange transactions analyzed and approved?Starting with a background sketch of the industry, this hands-onreference provides insights into the new trends in power systemsoperation and control, and highlights advanced issues in thefield.

Written for both technical and nontechnical professionals involvedin power engineering, finance, and marketing, this must-haveresource discusses:
* Market structure and operation of electric power systems
* Load and price forecasting and arbitrage
* Price-based unit commitment and security constrained unitcommitment
* Market power analysis and game theory applications
* Ancillary services auction market design
* Transmission pricing and congestion

Using real-world case studies, this timely survey offers engineers,consultants, researchers, financial managers, university professorsand students, and other professionals in the industry acomprehensive review of electricity restructuring and how itsradical effects will shape the market.


Author Notes

MOHAMMAD SHAHIDEHPOUR is Professor of Electrical and ComputerEngineering at the Illinois Institute of Technology. He is a Fellowof the IEEE and has served as a consultant for major energycompanies during the last twenty years.

HATIM YAMIN, PhD, is with ABB Energy Information Systems, a globalunit of the energy industry worldwide, and has served as a facultymember in the Power Engineering Department at Yarmouk University inJordan.

ZUYI LI, PhD, is with Global Energy Market Solutions, Inc. and hasbeen affiliated with the Electric Power and Power ElectronicsCenter at the Illinois Institute of Technology.


Reviews 1

Choice Review

Shahidehpour (Illinois Institute of Technology) and colleagues from industry have prepared an excellent work, a successful teaching blend of the physics, economics, and statistics required for professionals to perform in today's electric power markets. Readers will be impressed with the comprehensive approach and clear, readable presentation of material that will be read by professionals with applicable experience in one or maybe two of the key disciplines, but rarely with working skills in all three. The book is highly recommended for use in senior undergraduate- and graduate-level university courses open to engineering, business, and government policy students who have career interests in the electric power industry. The volume will serve well also as a resource in online or distance courses by universities or industry training consultants offering continuous education in the changing complexities of profitable electricity market operation. S. R. Walk Maine Maritime Academy


Table of Contents

Prefacep. XIII
1 Market Overview in Electric Power Systemsp. 1
1.1 Introductionp. 1
1.2 Market Structure and Operationp. 2
1.2.1 Objective of Market Operationp. 2
1.2.2 Electricity Market Modelsp. 4
1.2.3 Market Structurep. 5
1.2.4 Power Market Typesp. 9
1.2.5 Market Powerp. 13
1.2.6 Key Components in Market Operationp. 14
1.3 Overview of the Bookp. 15
1.3.1 Information Forecastingp. 15
1.3.2 Unit Commitment in Restructured Marketsp. 17
1.3.3 Arbitrage in Electricity Marketsp. 18
1.3.4 Market Power and Gamingp. 19
1.3.5 Asset Valuation and Risk Managementp. 19
1.3.6 Ancillary Services Auctionp. 19
1.3.7 Transmission Congestion Management and Pricingp. 19
2 Short-Term Load Forecastingp. 21
2.1 Introductionp. 21
2.1.1 Applications of Load Forecastingp. 21
2.1.2 Factors Affecting Load Patternsp. 22
2.1.3 Load Forecasting Categoriesp. 23
2.2 Short-Term Load Forecasting with ANNp. 25
2.2.1 Introduction to ANNp. 25
2.2.2 Application of ANN to STLFp. 29
2.2.3 STLF using MATLAB'S ANN Toolboxp. 31
2.3 ANN Architecture for STLFp. 33
2.3.1 Proposed ANN Architecturep. 33
2.3.2 Seasonal ANNp. 34
2.3.3 Adaptive Weightp. 36
2.3.4 Multiple-Day Forecastp. 37
2.4 Numerical Resultsp. 38
2.4.1 Training and Test Datap. 38
2.4.2 Stopping Criteria for Training Processp. 42
2.4.3 ANN Models for Comparisonp. 43
2.4.4 Performance of One-Day Forecastp. 45
2.4.5 Performance of Multiple-Day Forecastp. 51
2.5 Sensitivity Analysisp. 53
2.4.1 Possible Modelsp. 53
2.4.2 Sensitivity to Input Factorsp. 54
2.4.3 Inclusion of Temperature Implicitlyp. 55
3 Electricity Price Forecastingp. 57
3.1 Introductionp. 57
3.2 Issues of Electricity Pricing and Forecastingp. 60
3.2.1 Electricity Price Basicsp. 60
3.2.2 Electricity Price Volatilityp. 61
3.2.3 Categorization of Price Forecastingp. 63
3.2.4 Factors Considered in Price Forecastingp. 64
3.3 Electricity Price Simulation Modulep. 65
3.3.1 A Sample of Simulation Strategiesp. 66
3.3.2 Simulation Examplep. 67
3.4 Price Forecasting Module based on ANNp. 69
3.4.1 ANN Factors in Price Forecastingp. 70
3.4.2 118-Bus System Price Forecasting with ANNp. 72
3.5 Performance Evaluation of Price Forecastingp. 77
3.5.1 Alternative Methodsp. 77
3.5.2 Alternative MAPE Definitionp. 78
3.6 Practical Case Studiesp. 81
3.6.1 Impact of Data Pre-Processingp. 82
3.6.2 Impact of Quantity of Training Vectorsp. 84
3.6.3 Impact of Quantity of Input Factorsp. 86
3.6.4 Impact of Adaptive Forecastingp. 89
3.6.5 Comparison of ANN Method with Alternative Methodsp. 90
3.7 Price Volatility Analysis Modulep. 91
3.7.1 Price Spikes Analysisp. 91
3.7.2 Probability Distribution of Electricity Pricep. 105
3.8 Applications of Price Forecastingp. 111
3.8.1 Application of Point Price Forecast to Making Generation Schedulep. 111
3.8.2 Application of Probability Distribution of Price to Asset Valuation and Risk Analysisp. 112
3.8.3 Application of Probability Distribution of Price to Options Valuationp. 112
3.8.4 Application of Conditional Probability Distribution of Price on Load to Forward Price Forecastingp. 112
4 Price-Based Unit Commitmentp. 115
4.1 Introductionp. 115
4.2 PBUC Formulationp. 117
4.2.1 System Constraintsp. 118
4.2.2 Unit Constraintsp. 118
4.3 PBUC Solutionp. 119
4.3.1 Solution without Emission or Fuel Constraintsp. 120
4.3.2 Solution with Emission and Fuel Constraintsp. 129
4.4 Discussion on Solution Methodologyp. 134
4.4.1 Energy Purchasep. 134
4.4.2 Derivation of Steps for Updating Multipliersp. 134
4.4.3 Optimality Conditionp. 137
4.5 Additional Features of PBUCp. 139
4.5.1 Different Prices among Busesp. 139
4.5.2 Variable Fuel Price as a Function of Fuel Consumptionp. 140
4.5.3 Application of Lagrangian Augmentationp. 141
4.5.4 Bidding Strategy based on PBUCp. 145
4.6 Case Studiesp. 150
4.5.1 Case Study of 5-Unit Systemp. 150
4.5.2 Case Study of 36-Unit Systemp. 154
4.7 Conclusionsp. 160
5 Arbitrage in Electricity Marketsp. 161
5.1 Introductionp. 161
5.2 Concept of Arbitragep. 161
5.2.1 What is Arbitragep. 161
5.2.2 Usefulness of Arbitragep. 162
5.3 Arbitrage in a Power Marketp. 163
5.3.1 Same-Commodity Arbitragep. 163
5.3.2 Cross-Commodity Arbitragep. 164
5.3.3 Spark Spread and Arbitragep. 164
5.3.4 Applications of Arbitrage Based on PBUCp. 165
5.4 Arbitrage Examples in Power Marketp. 166
5.4.1 Arbitrage between Energy and Ancillary Servicep. 166
5.4.2 Arbitrage of Bilateral Contractp. 171
5.4.3 Arbitrage between Gas and Powerp. 174
5.4.4 Arbitrage of Emission Allowancep. 182
5.4.5 Arbitrage between Steam and Powerp. 186
5.5 Conclusionsp. 188
6 Market Power Analysis Based on Game Theoryp. 191
6.1 Introductionp. 191
6.2 Game Theoryp. 192
6.2.1 An Instructive Examplep. 192
6.2.2 Game Methods in Power Systemsp. 195
6.3 Power Transactions Gamep. 195
6.3.1 Coalitions among Participantsp. 197
6.3.2 Generation Cost for Participantsp. 198
6.3.3 Participant's Objectivep. 201
6.4 Nash Bargaining Problemp. 202
6.4.1 Nash Bargaining Model for Transaction Analysisp. 203
6.4.2 Two-Participant Problem Analysisp. 204
6.4.3 Discussion on Optimal Transaction and Its Pricep. 206
6.4.4 Test Resultsp. 207
6.5 Market Competition with Incomplete Informationp. 215
6.5.1 Participants and Bidding Informationp. 215
6.5.2 Basic Probability Distribution of the Gamep. 216
6.5.3 Conditional Probabilities and Expected Payoffp. 217
6.5.4 Gaming Methodologyp. 218
6.6 Market Competition for Multiple Electricity Productsp. 222
6.6.1 Solution Methodologyp. 222
6.6.2 Study Systemp. 223
6.6.3 Gaming Methodologyp. 225
6.7 Conclusionsp. 230
7 Generation Asset Valuation and Risk Analysisp. 233
7.1 Introductionp. 233
7.1.1 Asset Valuationp. 233
7.1.2 Value at Risk (VaR)p. 234
7.1.3 Application of VaR to Asset Valuation in Power Marketsp. 235
7.2 VaR for Generation Asset Valuationp. 236
7.2.1 Framework of the VaR Calculationp. 236
7.2.2 Spot Market Price Simulationp. 238
7.2.3 A Numerical Examplep. 240
7.2.4 A Practical Examplep. 246
7.2.5 Sensitivity Analysisp. 258
7.3 Generation Capacity Valuationp. 267
7.3.1 Framework of VaR Calculationp. 268
7.3.2 An Examplep. 268
7.3.3 Sensitivity Analysisp. 270
7.4 Conclusionsp. 273
8 Security-Constrained Unit Commitmentp. 275
8.1 Introductionp. 275
8.2 SCUC Problem Formulationp. 276
8.2.1 Discussion on Ramping Constraintsp. 280
8.3 Benders Decomposition Solution of SCUCp. 285
8.3.1 Benders Decompositionp. 286
8.3.2 Application of Benders Decomposition to SCUCp. 287
8.3.3 Master Problem Formulationp. 287
8.4 SCUC to Minimize Network Violationp. 290
8.4.1 Linearization of Network Constraintsp. 290
8.4.2 Subproblem Formulationp. 293
8.4.3 Benders Cuts Formulationp. 296
8.4.4 Case Studyp. 296
8.5 SCUC Application to Minimize EUE - Impact of Reliabilityp. 303
8.5.1 Subproblem Formulation and Solutionp. 303
8.5.2 Case Studyp. 306
8.6 Conclusionsp. 310
9 Ancillary Services Auction Market Designp. 311
9.1 Introductionp. 311
9.2 Ancillary Services for Restructuringp. 313
9.3 Forward Ancillary Services Auction--Sequential Approachp. 315
9.3.1 Two Alternatives in Sequential Ancillary Services Auctionp. 317
9.3.2 Ancillary Services Schedulingp. 318
9.3.3 Design of the Ancillary Services Auction Marketp. 320
9.3.4 Case Studyp. 322
9.3.5 Discussionsp. 334
9.4 Forward Ancillary Services Auction--Simultaneous Approachp. 334
9.4.1 Design Options for Simultaneous Auction of Ancillary Servicesp. 336
9.4.2 Rational Buyer Auctionp. 338
9.4.3 Marginal Pricing Auctionp. 347
9.4.4 Discussionsp. 354
9.5 Automatic Generation Control (AGC)p. 354
9.5.1 AGC Functionsp. 354
9.5.2 AGC Responsep. 356
9.5.3 AGC Units Revenue Adequacyp. 357
9.5.4 AGC Pricingp. 358
9.5.5 Discussionsp. 366
9.6 Conclusionsp. 367
10 Transmission Congestion Management and Pricingp. 369
10.1 Introductionp. 369
10.2 Transmission Cost Allocation Methodsp. 372
10.2.1 Postage-Stamp Rate Methodp. 372
10.2.2 Contract Path Methodp. 373
10.2.3 MW-Mile Methodp. 373
10.2.4 Unused Transmission Capacity Methodp. 374
10.2.5 MVA-Mile Methodp. 376
10.2.6 Counter-Flow Methodp. 376
10.2.7 Distribution Factors Methodp. 376
10.2.8 AC Power Flow Methodp. 379
10.2.9 Tracing Methodsp. 379
10.2.10 Comparison of Cost Allocation Methodsp. 386
10.3 Examples for Transmission Cost Allocation Methodsp. 387
10.3.1 Cost Allocation Using Distribution Factors Methodp. 388
10.3.2 Cost Allocation Using Bialek's Tracing Methodp. 389
10.3.3 Cost Allocation Using Kirschen's Tracing Methodp. 391
10.3.4 Comparing the Three Cost Allocation Methodsp. 392
10.4 LMP, FTR, and Congestion Managementp. 393
10.4.1 Locational Marginal Price (LMP)p. 393
10.4.2 LMP Application in Determining Zonal Boundariesp. 405
10.4.3 Firm Transmission Right (FTR)p. 408
10.4.4 FTR Auctionp. 412
10.4.5 Zonal Congestion Managementp. 421
10.5 A Comprehensive Transmission Pricing Schemep. 431
10.5.1 Outline of the Proposed Transmission Pricing Schemep. 432
10.5.2 Prioritization of Transmission Dispatchp. 434
10.5.3 Calculation of Transmission Usage and Congestion Charges and FTR Creditsp. 439
10.5.4 Numerical Examplep. 443
10.6 Conclusionsp. 453
Appendix
A List of Symbolsp. 455
B Mathematical Derivationp. 461
B.1 Derivation of Probability Distributionp. 461
B.2 Lagrangian Augmentation with Inequality Constraintsp. 462
C RTS Load Datap. 467
D Example Systems Datap. 469
D.1 5-Unit Systemp. 469
D.2 36-Unit Systemp. 472
D.3 6-Unit Systemp. 476
D.4 Modified IEEE 30-Bus Systemp. 477
D.5 118-Bus Systemp. 479
E Game Theory Conceptsp. 483
E.1 Equilibrium in Non-Cooperative Gamesp. 483
E.2 Characteristics Functionp. 484
E.3 N-Players Cooperative Gamesp. 485
E.4 Games with Incomplete Informationp. 486
F Congestion Charges Calculationp. 489
F.1 Calculations of Congestion Charges using Contributions of Generatorsp. 489
F.2 Calculations of Congestion Charges using Contributions of Loadsp. 493
Referencesp. 495
Indexp. 509