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Decision Making UnderUncertainty in ElectricityMarkets |
4 |
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Preface |
8 |
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Contents |
12 |
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Chapter 1 Electricity Markets |
20 |
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1.1 Introduction |
20 |
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1.2 Organization and Agents |
20 |
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1.2.1 Market Organization |
21 |
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1.2.2 Agents |
23 |
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1.2.3 Pool |
25 |
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1.2.4 Futures Market |
28 |
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1.2.5 Reserve and Regulation Markets |
30 |
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1.3 Time Framework and Uncertainty |
32 |
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1.3.1 Decision Sequence |
32 |
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1.3.2 Uncertainty |
34 |
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1.4 Decision Making |
36 |
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1.4.1 Consumer |
36 |
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1.4.2 Retailer |
38 |
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1.4.3 Producer |
39 |
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1.4.4 Non-Dispatchable Producer |
41 |
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1.4.5 Market Operator |
42 |
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1.4.6 Independent System Operator |
43 |
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1.5 Summary |
44 |
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1.6 Exercises |
44 |
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Chapter 2 Stochastic Programming Fundamentals |
46 |
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2.1 Introduction |
46 |
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2.2 Random Variables |
48 |
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2.3 Stochastic Processes |
50 |
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2.4 Scenarios |
51 |
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2.5 Stochastic Programming Problems |
53 |
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2.5.1 Two-Stage Problems |
53 |
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2.5.2 Multi-Stage Problems |
58 |
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2.6 Quality Metrics |
67 |
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2.6.1 Expected Value of Perfect Information |
68 |
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2.6.2 Value of the Stochastic Solution |
71 |
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2.6.3 Out-of-Sample Assessment |
76 |
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2.7 Risk |
77 |
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2.8 Solving Stochastic Programming Problems |
78 |
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2.9 Summary and Conclusions |
80 |
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2.10 Exercises |
80 |
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Chapter 3 Uncertainty Characterization via Scenarios |
82 |
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3.1 Introduction |
82 |
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3.2 Scenario Generation |
85 |
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3.2.1 Overview |
85 |
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3.2.2 Scenario Generation using ARIMA Models |
87 |
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3.2.3 Generating Scenarios for Unit Availability |
94 |
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3.2.4 Quality of Scenario Subsets |
97 |
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3.3 Scenario Reduction |
99 |
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3.3.1 Motivation |
99 |
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3.3.2 Scenario Reduction Using a Probability Distance |
100 |
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3.3.3 Algorithm |
101 |
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3.4 Scenario Generation for Dependent Stochastic Processes |
111 |
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3.4.1 Overview |
111 |
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3.4.2 Scenarios for contemporaneous or quasi-contemporaneous stochastic processes |
113 |
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3.4.3 Scenarios for non-contemporaneous stochastic processes |
120 |
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3.5 Case Studies |
122 |
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3.5.1 Scenario Generation Using ARIMA and Dynamic Regression models: Electricity Price and Demand |
122 |
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3.5.2 Scenario Generation for Quasi-contemporaneous Stochastic Processes: Wind Speeds at Multiple Sites |
127 |
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3.6 Summary and Conclusions |
134 |
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3.7 Exercises |
136 |
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Chapter 4 Risk management |
139 |
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4.1 Introduction |
139 |
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4.2 Risk Control in Stochastic Programming Problems |
140 |
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4.2.1 Risk-Neutral Decision Making |
140 |
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4.2.2 Risk-Averse Decision Making |
144 |
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4.3 Risk Measures |
146 |
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4.3.1 Variance |
147 |
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4.3.2 Shortfall Probability |
150 |
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4.3.3 Expected Shortage |
153 |
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4.3.4 Value-at-Risk |
157 |
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4.3.5 Conditional Value-at-Risk |
160 |
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4.3.6 Stochastic Dominance |
163 |
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4.4 Summary and Conclusions |
170 |
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4.5 Exercises |
172 |
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Chapter 5 Producer Pool Trading |
175 |
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5.1 Introduction |
175 |
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5.2 Decision Framework |
176 |
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5.3 Uncertainty Characterization |
179 |
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5.3.1 Day-ahead, Regulation, and Adjustment Prices |
179 |
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5.3.2 Scenario Tree |
181 |
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5.4 Pool Structure |
184 |
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5.4.1 Day-Ahead Market |
184 |
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5.4.2 Regulation Market |
187 |
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5.4.3 Adjustment Market |
189 |
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5.5 Producer Model |
193 |
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5.5.1 Unit Constraints |
193 |
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5.5.2 Expected Profit |
194 |
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5.5.3 Risk Modeling |
195 |
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5.6 Formulation |
196 |
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5.7 Producer Pool Example |
197 |
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5.8 Producer Pool Case Study |
204 |
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5.9 Summary and Conclusions |
209 |
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5.10 Notation |
209 |
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5.11 Exercises |
212 |
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Chapter 6 Pool Trading for Wind Power Producers |
213 |
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6.1 Introduction |
213 |
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6.2 Decision Framework |
215 |
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6.3 The Key Issues |
218 |
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6.3.1 Mechanism for Imbalance Prices |
218 |
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6.3.2 Revenue and Imbalance Cost |
224 |
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6.3.3 Certainty Gain Effect |
227 |
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6.4 Uncertainty Characterization |
228 |
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6.4.1 Day-ahead, Adjustment, and Imbalance Prices |
229 |
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6.4.2 Wind Power Production |
232 |
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6.4.3 Scenario Tree |
234 |
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6.5 Wind Producer Model |
239 |
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6.5.1 Basic Model |
239 |
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6.5.2 Offering Curves |
243 |
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6.5.3 Risk Modeling |
244 |
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6.5.4 Adjustment Market |
245 |
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6.5.5 Formulation |
247 |
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6.6 Wind Producer Example |
249 |
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6.7 Wind Producer Case Study |
257 |
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6.8 Summary and Conclusions |
264 |
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6.9 Notation |
266 |
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6.10 Exercises |
268 |
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Chapter 7 Futures Market Trading for Producers |
270 |
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7.1 Introduction |
270 |
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7.2 Decision Framework |
270 |
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7.3 Uncertainty Characterization |
273 |
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7.3.1 Pool Prices |
273 |
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7.3.2 Unit Availability |
274 |
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7.3.3 Scenario Tree |
275 |
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7.4 Market Structure |
276 |
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7.4.1 Futures Market |
276 |
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7.4.2 Pool |
279 |
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7.5 Producer Model |
280 |
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7.5.1 Unit Constraints |
280 |
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7.5.2 Unit Availability |
281 |
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7.5.3 Energy Balance |
282 |
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7.5.4 Expected Profit |
282 |
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7.5.5 Risk Modeling |
283 |
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7.6 Formulation |
284 |
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7.7 Producer Futures Market Example. No Unit Unavailability |
285 |
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7.8 Producer Futures Market Example. Unit Unavailability |
290 |
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7.9 Producer Futures Market Case Study |
293 |
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7.10 Summary and Conclusions |
298 |
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7.11 Notation |
300 |
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7.12 Exercises |
302 |
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Chapter 8 Medium-Term Retailer Trading |
303 |
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8.1 Introduction |
303 |
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8.2 Decision Framework |
305 |
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8.3 Uncertainty Characterization |
307 |
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8.4 Market Structure |
308 |
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8.4.1 Futures Market |
309 |
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8.4.2 Pool |
311 |
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8.5 Retailer Model |
312 |
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8.5.1 Client Modeling |
312 |
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8.5.2 Price-Quota Curve |
313 |
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8.5.3 Revenue from Selling to Clients |
315 |
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8.5.4 Energy Balance |
317 |
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8.5.5 Expected Profit |
318 |
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8.5.6 Risk Modeling |
318 |
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8.6 Formulation |
320 |
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8.7 Retailer Example |
321 |
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8.8 Retailer Case Study |
325 |
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8.9 Summary and Conclusions |
334 |
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8.10 Notation |
334 |
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8.11 Exercises |
337 |
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Chapter 9 Energy Procurement by Consumers |
338 |
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9.1 Introduction |
338 |
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9.2 Decision Framework and Uncertainty Model |
339 |
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9.2.1 Decision Framework |
339 |
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9.2.2 Pool Price and Demand |
341 |
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9.3 Model |
343 |
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9.3.1 Bilateral Contracts |
343 |
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9.3.2 Pool |
346 |
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9.3.3 Self-Production |
347 |
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9.3.4 Energy Balance |
348 |
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9.3.5 Non-anticipativity |
349 |
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9.3.6 Expected Cost |
351 |
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9.3.7 Risk |
352 |
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9.4 Formulation |
352 |
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9.5 Consumer Example |
354 |
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9.6 Consumer Case Study |
359 |
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9.7 Summary and Conclusions |
366 |
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9.8 Notation |
367 |
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9.9 Exercises |
369 |
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Chapter 10 Market Clearing Considering Equipment Failures |
371 |
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10.1 Introduction |
371 |
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10.2 Stochastic Security-Constrained Market Clearing |
372 |
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10.2.1 Main Features |
372 |
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10.2.2 Introducing Security Constraints |
373 |
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10.2.3 Setting Reserve Requirements: Deterministic and Probabilistic Approaches |
374 |
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10.2.4 Solution Algorithm |
374 |
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10.2.5 Security-Related Definitions |
375 |
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10.3 Stochastic Security Metrics |
375 |
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10.3.1 Probabilistic Metrics |
376 |
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10.3.2 Security Criteria Based on the ELNS |
379 |
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10.4 Market-Clearing Formulation |
379 |
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10.4.1 Assumptions |
380 |
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10.4.2 Variables |
380 |
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10.4.3 Structure |
381 |
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10.4.4 Objective function |
381 |
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10.4.5 Electricity Market Constraints |
383 |
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10.4.6 Real-Time Operating Constraints |
387 |
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10.4.7 Linking constraints |
393 |
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10.4.8 Formulation |
396 |
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10.5 Computing Scenario Probabilities |
399 |
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10.6 Market-Clearing Example |
401 |
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10.7 Market-Clearing Case Study |
409 |
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10.8 Summary and Conclusions |
412 |
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10.9 Notation |
413 |
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10.10 Exercises |
416 |
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Chapter 11 Market Clearing under Uncertainty: Wind Energy |
418 |
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11.1 Introduction |
418 |
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11.2 Wind Power Production |
419 |
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11.2.1 A Look to the Near Future: Wind Generation |
419 |
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11.2.2 All That Glitters Is Not Gold: Wind Impact on System Security |
420 |
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11.2.3 Accommodating Wind Uncertainty in Electricity Markets |
421 |
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11.2.4 The Handicap: The Computational Burden |
422 |
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11.3 Market-Clearing Model |
423 |
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11.3.1 Assumptions |
423 |
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11.3.2 Wind Uncertainty Characterization |
424 |
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11.3.3 Wind Uncertainty vs. Equipment Failures |
425 |
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11.3.4 Breaking Down the Expected Cost |
430 |
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11.3.5 Wind Spillage Cost |
434 |
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11.3.6 Formulation |
435 |
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11.4 Wind Benefits and Costs at a Glance: Performance Metrics |
438 |
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11.4.1 Average Benefit (AB) |
439 |
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11.4.2 Average Uncertainty Cost (AUC) |
439 |
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11.4.3 Net Average Benefit (NAB) |
439 |
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11.5 Market-Clearing Example with Wind Generation |
440 |
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11.5.1 Impact of wind generator location and network congestion |
442 |
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11.5.2 Impact of wind spillage cost |
446 |
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11.5.3 Impact of wind penetration and uncertainty levels |
446 |
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11.6 Market-Clearing Case Study with Wind Generation |
448 |
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11.7 Summary and Conclusions |
454 |
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11.8 Notation |
455 |
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11.9 Exercises |
458 |
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Appendix A GAMS codes |
460 |
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A.1 Introduction |
460 |
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A.2 GAMS code for the Producer Pool Example (Section 5.7) |
460 |
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A.3 GAMS code for the Wind Producer Example (Section 6.6) |
465 |
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A.4 GAMS code for the Producer Futures Market Example. No Unit Unavailability (Section 7.7) |
468 |
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A.5 GAMS code for the Producer Futures Market Example. Unit Unavailability (Section 7.8) |
470 |
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A.6 GAMS code for the Retailer Example (Section 8.7) |
473 |
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A.7 GAMS code for the Consumer Example (Section 9.5) |
476 |
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A.8 GAMS code for the Market-Clearing Example (Section 10.6) |
479 |
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A.9 GAMS code for the Market-Clearing Example with Wind Generation (Section 11.5) |
485 |
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Appendix B 24-Node System Data |
491 |
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B.1 Network data |
491 |
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B.2 Generator data |
491 |
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B.3 Demand data |
494 |
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Appendix C Exercise solutions |
497 |
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C.1 Exercises from Chapter 2 |
497 |
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C.2 Exercises from Chapter 3 |
501 |
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C.3 Exercises from Chapter 4 |
508 |
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C.4 Exercises from Chapter 5 |
512 |
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C.5 Exercises from Chapter 6 |
517 |
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C.6 Exercises from Chapter 7 |
520 |
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C.7 Exercises from Chapter 8 |
523 |
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C.8 Exercises from Chapter 9 |
526 |
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C.9 Exercises from Chapter 10 |
530 |
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C.10 Exercises from Chapter 11 |
534 |
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References |
539 |
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Index |
546 |
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Biographies |
549 |
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