Carrión Decision Making Under Uncertainty in Electricity Markets

1. Auflage 2010
ISBN: 978-1-4419-7421-1
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 549 Seiten

Decision Making Under Uncertainty in Electricity Markets provides models and procedures to be used by electricity market agents to make informed decisions under uncertainty. These procedures rely on well established stochastic programming models, which make them efficient and robust. Particularly, these techniques allow electricity producers to derive offering strategies for the pool and contracting decisions in the futures market. Retailers use these techniques to derive selling prices to clients and energy procurement strategies through the pool, the futures market and bilateral contracting. Using the proposed models, consumers can derive the best energy procurement strategies using the available trading floors. The market operator can use the techniques proposed in this book to clear simultaneously energy and reserve markets promoting efficiency and equity. The techniques described in this book are of interest for professionals working on energy markets, and for graduate students in power engineering, applied mathematics, applied economics, and operations research.

Antonio J. Conejo full professor at the Universidad de Castilla - La Mancha, Spain, received the M.S. from MIT and the Ph.D. from the Royal Institute of Technology, Sweden. He has published over 100 papers in prestigious journals and is the author or coauthor of books published by Springer, Wiley, McGraw-Hill and CRC. He has been the principal investigator of many research projects financed by public agencies and the power industry. He is an IEEE Fellow and a member of the editorial board of the IEEE Transactions on Power Systems. Miguel Carrión received the Ingeniero Industrial degree and the PhD degree from the Universidad de Castilla-La Mancha, Ciudad Real, Spain, in 2003 and 2008, respectively. He is currently an Assistant Professor at the Universidad de Castilla-La Mancha, Toledo, Spain. Juan M. Morales received the Ingeniero Industrial degree from the Universidad de Málaga, Spain, in 2006. He is currently working toward the Ph.D. degree at the Universidad de Castilla-La Mancha.

Autoren/Hrsg.

Carrión, Miguel

Inhaltsverzeichnis

1;Decision Making UnderUncertainty in ElectricityMarkets;4
1.1;Preface;8
1.2;Contents;12
1.3;Chapter 1 Electricity Markets;20
1.3.1;1.1 Introduction;20
1.3.2;1.2 Organization and Agents;20
1.3.2.1;1.2.1 Market Organization;21
1.3.2.2;1.2.2 Agents;23
1.3.2.3;1.2.3 Pool;25
1.3.2.4;1.2.4 Futures Market;28
1.3.2.5;1.2.5 Reserve and Regulation Markets;30
1.3.3;1.3 Time Framework and Uncertainty;32
1.3.3.1;1.3.1 Decision Sequence;32
1.3.3.2;1.3.2 Uncertainty;34
1.3.4;1.4 Decision Making;36
1.3.4.1;1.4.1 Consumer;36
1.3.4.2;1.4.2 Retailer;38
1.3.4.3;1.4.3 Producer;39
1.3.4.4;1.4.4 Non-Dispatchable Producer;41
1.3.4.5;1.4.5 Market Operator;42
1.3.4.6;1.4.6 Independent System Operator;43
1.3.5;1.5 Summary;44
1.3.6;1.6 Exercises;44
1.4;Chapter 2 Stochastic Programming Fundamentals;46
1.4.1;2.1 Introduction;46
1.4.2;2.2 Random Variables;48
1.4.3;2.3 Stochastic Processes;50
1.4.4;2.4 Scenarios;51
1.4.5;2.5 Stochastic Programming Problems;53
1.4.5.1;2.5.1 Two-Stage Problems;53
1.4.5.2;2.5.2 Multi-Stage Problems;58
1.4.6;2.6 Quality Metrics;67
1.4.6.1;2.6.1 Expected Value of Perfect Information;68
1.4.6.2;2.6.2 Value of the Stochastic Solution;71
1.4.6.3;2.6.3 Out-of-Sample Assessment;76
1.4.7;2.7 Risk;77
1.4.8;2.8 Solving Stochastic Programming Problems;78
1.4.9;2.9 Summary and Conclusions;80
1.4.10;2.10 Exercises;80
1.5;Chapter 3 Uncertainty Characterization via Scenarios;82
1.5.1;3.1 Introduction;82
1.5.2;3.2 Scenario Generation;85
1.5.2.1;3.2.1 Overview;85
1.5.2.2;3.2.2 Scenario Generation using ARIMA Models;87
1.5.2.3;3.2.3 Generating Scenarios for Unit Availability;94
1.5.2.4;3.2.4 Quality of Scenario Subsets;97
1.5.3;3.3 Scenario Reduction;99
1.5.3.1;3.3.1 Motivation;99
1.5.3.2;3.3.2 Scenario Reduction Using a Probability Distance;100
1.5.3.3;3.3.3 Algorithm;101
1.5.4;3.4 Scenario Generation for Dependent Stochastic Processes;111
1.5.4.1;3.4.1 Overview;111
1.5.4.2;3.4.2 Scenarios for contemporaneous or quasi-contemporaneous stochastic processes;113
1.5.4.3;3.4.3 Scenarios for non-contemporaneous stochastic processes;120
1.5.5;3.5 Case Studies;122
1.5.5.1;3.5.1 Scenario Generation Using ARIMA and Dynamic Regression models: Electricity Price and Demand;122
1.5.5.2;3.5.2 Scenario Generation for Quasi-contemporaneous Stochastic Processes: Wind Speeds at Multiple Sites;127
1.5.6;3.6 Summary and Conclusions;134
1.5.7;3.7 Exercises;136
1.6;Chapter 4 Risk management;139
1.6.1;4.1 Introduction;139
1.6.2;4.2 Risk Control in Stochastic Programming Problems;140
1.6.2.1;4.2.1 Risk-Neutral Decision Making;140
1.6.2.2;4.2.2 Risk-Averse Decision Making;144
1.6.3;4.3 Risk Measures;146
1.6.3.1;4.3.1 Variance;147
1.6.3.2;4.3.2 Shortfall Probability;150
1.6.3.3;4.3.3 Expected Shortage;153
1.6.3.4;4.3.4 Value-at-Risk;157
1.6.3.5;4.3.5 Conditional Value-at-Risk;160
1.6.3.6;4.3.6 Stochastic Dominance;163
1.6.4;4.4 Summary and Conclusions;170
1.6.5;4.5 Exercises;172
1.7;Chapter 5 Producer Pool Trading;175
1.7.1;5.1 Introduction;175
1.7.2;5.2 Decision Framework;176
1.7.3;5.3 Uncertainty Characterization;179
1.7.3.1;5.3.1 Day-ahead, Regulation, and Adjustment Prices;179
1.7.3.2;5.3.2 Scenario Tree;181
1.7.4;5.4 Pool Structure;184
1.7.4.1;5.4.1 Day-Ahead Market;184
1.7.4.2;5.4.2 Regulation Market;187
1.7.4.3;5.4.3 Adjustment Market;189
1.7.5;5.5 Producer Model;193
1.7.5.1;5.5.1 Unit Constraints;193
1.7.5.2;5.5.2 Expected Profit;194
1.7.5.3;5.5.3 Risk Modeling;195
1.7.6;5.6 Formulation;196
1.7.7;5.7 Producer Pool Example;197
1.7.8;5.8 Producer Pool Case Study;204
1.7.9;5.9 Summary and Conclusions;209
1.7.10;5.10 Notation;209
1.7.11;5.11 Exercises;212
1.8;Chapter 6 Pool Trading for Wind Power Producers;213
1.8.1;6.1 Introduction;213
1.8.2;6.2 Decision Framework;215
1.8.3;6.3 The Key Issues;218
1.8.3.1;6.3.1 Mechanism for Imbalance Prices;218
1.8.3.2;6.3.2 Revenue and Imbalance Cost;224
1.8.3.3;6.3.3 Certainty Gain Effect;227
1.8.4;6.4 Uncertainty Characterization;228
1.8.4.1;6.4.1 Day-ahead, Adjustment, and Imbalance Prices;229
1.8.4.2;6.4.2 Wind Power Production;232
1.8.4.3;6.4.3 Scenario Tree;234
1.8.5;6.5 Wind Producer Model;239
1.8.5.1;6.5.1 Basic Model;239
1.8.5.2;6.5.2 Offering Curves;243
1.8.5.3;6.5.3 Risk Modeling;244
1.8.5.4;6.5.4 Adjustment Market;245
1.8.5.5;6.5.5 Formulation;247
1.8.6;6.6 Wind Producer Example;249
1.8.7;6.7 Wind Producer Case Study;257
1.8.8;6.8 Summary and Conclusions;264
1.8.9;6.9 Notation;266
1.8.10;6.10 Exercises;268
1.9;Chapter 7 Futures Market Trading for Producers;270
1.9.1;7.1 Introduction;270
1.9.2;7.2 Decision Framework;270
1.9.3;7.3 Uncertainty Characterization;273
1.9.3.1;7.3.1 Pool Prices;273
1.9.3.2;7.3.2 Unit Availability;274
1.9.3.3;7.3.3 Scenario Tree;275
1.9.4;7.4 Market Structure;276
1.9.4.1;7.4.1 Futures Market;276
1.9.4.2;7.4.2 Pool;279
1.9.5;7.5 Producer Model;280
1.9.5.1;7.5.1 Unit Constraints;280
1.9.5.2;7.5.2 Unit Availability;281
1.9.5.3;7.5.3 Energy Balance;282
1.9.5.4;7.5.4 Expected Profit;282
1.9.5.5;7.5.5 Risk Modeling;283
1.9.6;7.6 Formulation;284
1.9.7;7.7 Producer Futures Market Example. No Unit Unavailability;285
1.9.8;7.8 Producer Futures Market Example. Unit Unavailability;290
1.9.9;7.9 Producer Futures Market Case Study;293
1.9.10;7.10 Summary and Conclusions;298
1.9.11;7.11 Notation;300
1.9.12;7.12 Exercises;302
1.10;Chapter 8 Medium-Term Retailer Trading;303
1.10.1;8.1 Introduction;303
1.10.2;8.2 Decision Framework;305
1.10.3;8.3 Uncertainty Characterization;307
1.10.4;8.4 Market Structure;308
1.10.4.1;8.4.1 Futures Market;309
1.10.4.2;8.4.2 Pool;311
1.10.5;8.5 Retailer Model;312
1.10.5.1;8.5.1 Client Modeling;312
1.10.5.2;8.5.2 Price-Quota Curve;313
1.10.5.3;8.5.3 Revenue from Selling to Clients;315
1.10.5.4;8.5.4 Energy Balance;317
1.10.5.5;8.5.5 Expected Profit;318
1.10.5.6;8.5.6 Risk Modeling;318
1.10.6;8.6 Formulation;320
1.10.7;8.7 Retailer Example;321
1.10.8;8.8 Retailer Case Study;325
1.10.9;8.9 Summary and Conclusions;334
1.10.10;8.10 Notation;334
1.10.11;8.11 Exercises;337
1.11;Chapter 9 Energy Procurement by Consumers;338
1.11.1;9.1 Introduction;338
1.11.2;9.2 Decision Framework and Uncertainty Model;339
1.11.2.1;9.2.1 Decision Framework;339
1.11.2.2;9.2.2 Pool Price and Demand;341
1.11.3;9.3 Model;343
1.11.3.1;9.3.1 Bilateral Contracts;343
1.11.3.2;9.3.2 Pool;346
1.11.3.3;9.3.3 Self-Production;347
1.11.3.4;9.3.4 Energy Balance;348
1.11.3.5;9.3.5 Non-anticipativity;349
1.11.3.6;9.3.6 Expected Cost;351
1.11.3.7;9.3.7 Risk;352
1.11.4;9.4 Formulation;352
1.11.5;9.5 Consumer Example;354
1.11.6;9.6 Consumer Case Study;359
1.11.7;9.7 Summary and Conclusions;366
1.11.8;9.8 Notation;367
1.11.9;9.9 Exercises;369
1.12;Chapter 10 Market Clearing Considering Equipment Failures;371
1.12.1;10.1 Introduction;371
1.12.2;10.2 Stochastic Security-Constrained Market Clearing;372
1.12.2.1;10.2.1 Main Features;372
1.12.2.2;10.2.2 Introducing Security Constraints;373
1.12.2.3;10.2.3 Setting Reserve Requirements: Deterministic and Probabilistic Approaches;374
1.12.2.4;10.2.4 Solution Algorithm;374
1.12.2.5;10.2.5 Security-Related Definitions;375
1.12.3;10.3 Stochastic Security Metrics;375
1.12.3.1;10.3.1 Probabilistic Metrics;376
1.12.3.2;10.3.2 Security Criteria Based on the ELNS;379
1.12.4;10.4 Market-Clearing Formulation;379
1.12.4.1;10.4.1 Assumptions;380
1.12.4.2;10.4.2 Variables;380
1.12.4.3;10.4.3 Structure;381
1.12.4.4;10.4.4 Objective function;381
1.12.4.5;10.4.5 Electricity Market Constraints;383
1.12.4.6;10.4.6 Real-Time Operating Constraints;387
1.12.4.7;10.4.7 Linking constraints;393
1.12.4.8;10.4.8 Formulation;396
1.12.5;10.5 Computing Scenario Probabilities;399
1.12.6;10.6 Market-Clearing Example;401
1.12.7;10.7 Market-Clearing Case Study;409
1.12.8;10.8 Summary and Conclusions;412
1.12.9;10.9 Notation;413
1.12.10;10.10 Exercises;416
1.13;Chapter 11 Market Clearing under Uncertainty: Wind Energy;418
1.13.1;11.1 Introduction;418
1.13.2;11.2 Wind Power Production;419
1.13.2.1;11.2.1 A Look to the Near Future: Wind Generation;419
1.13.2.2;11.2.2 All That Glitters Is Not Gold: Wind Impact on System Security;420
1.13.2.3;11.2.3 Accommodating Wind Uncertainty in Electricity Markets;421
1.13.2.4;11.2.4 The Handicap: The Computational Burden;422
1.13.3;11.3 Market-Clearing Model;423
1.13.3.1;11.3.1 Assumptions;423
1.13.3.2;11.3.2 Wind Uncertainty Characterization;424
1.13.3.3;11.3.3 Wind Uncertainty vs. Equipment Failures;425
1.13.3.4;11.3.4 Breaking Down the Expected Cost;430
1.13.3.5;11.3.5 Wind Spillage Cost;434
1.13.3.6;11.3.6 Formulation;435
1.13.4;11.4 Wind Benefits and Costs at a Glance: Performance Metrics;438
1.13.4.1;11.4.1 Average Benefit (AB);439
1.13.4.2;11.4.2 Average Uncertainty Cost (AUC);439
1.13.4.3;11.4.3 Net Average Benefit (NAB);439
1.13.5;11.5 Market-Clearing Example with Wind Generation;440
1.13.5.1;11.5.1 Impact of wind generator location and network congestion;442
1.13.5.2;11.5.2 Impact of wind spillage cost;446
1.13.5.3;11.5.3 Impact of wind penetration and uncertainty levels;446
1.13.6;11.6 Market-Clearing Case Study with Wind Generation;448
1.13.7;11.7 Summary and Conclusions;454
1.13.8;11.8 Notation;455
1.13.9;11.9 Exercises;458
1.14;Appendix A GAMS codes;460
1.14.1;A.1 Introduction;460
1.14.2;A.2 GAMS code for the Producer Pool Example (Section 5.7);460
1.14.3;A.3 GAMS code for the Wind Producer Example (Section 6.6);465
1.14.4;A.4 GAMS code for the Producer Futures Market Example. No Unit Unavailability (Section 7.7);468
1.14.5;A.5 GAMS code for the Producer Futures Market Example. Unit Unavailability (Section 7.8);470
1.14.6;A.6 GAMS code for the Retailer Example (Section 8.7);473
1.14.7;A.7 GAMS code for the Consumer Example (Section 9.5);476
1.14.8;A.8 GAMS code for the Market-Clearing Example (Section 10.6);479
1.14.9;A.9 GAMS code for the Market-Clearing Example with Wind Generation (Section 11.5);485
1.15;Appendix B 24-Node System Data;491
1.15.1;B.1 Network data;491
1.15.2;B.2 Generator data;491
1.15.3;B.3 Demand data;494
1.16;Appendix C Exercise solutions;497
1.16.1;C.1 Exercises from Chapter 2;497
1.16.2;C.2 Exercises from Chapter 3;501
1.16.3;C.3 Exercises from Chapter 4;508
1.16.4;C.4 Exercises from Chapter 5;512
1.16.5;C.5 Exercises from Chapter 6;517
1.16.6;C.6 Exercises from Chapter 7;520
1.16.7;C.7 Exercises from Chapter 8;523
1.16.8;C.8 Exercises from Chapter 9;526
1.16.9;C.9 Exercises from Chapter 10;530
1.16.10;C.10 Exercises from Chapter 11;534
1.17;References;539
1.18;Index;546
1.19;Biographies;549