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Decision Making Under Uncertainty in Electricity Markets

:	Antonio J. Conejo, Miguel Carrión, Juan M. Morales
:	Decision Making Under Uncertainty in Electricity Markets
:	Springer-Verlag
:	9781441974211
:	1
:	CHF 280.20
:

:	Allgemeines, Lexika
:	English

:	549
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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.

	Decision Making UnderUncertainty in ElectricityMarkets	4
	Preface	8
	Contents	12
	Chapter 1 Electricity Markets	20
	1.1 Introduction	20
	1.2 Organization and Agents	20
	1.2.1 Market Organization	21
	1.2.2 Agents	23
	1.2.3 Pool	25
	1.2.4 Futures Market	28
	1.2.5 Reserve and Regulation Markets	30
	1.3 Time Framework and Uncertainty	32
	1.3.1 Decision Sequence	32
	1.3.2 Uncertainty	34
	1.4 Decision Making	36
	1.4.1 Consumer	36
	1.4.2 Retailer	38
	1.4.3 Producer	39
	1.4.4 Non-Dispatchable Producer	41
	1.4.5 Market Operator	42
	1.4.6 Independent System Operator	43
	1.5 Summary	44
	1.6 Exercises	44
	Chapter 2 Stochastic Programming Fundamentals	46
	2.1 Introduction	46
	2.2 Random Variables	48
	2.3 Stochastic Processes	50
	2.4 Scenarios	51
	2.5 Stochastic Programming Problems	53
	2.5.1 Two-Stage Problems	53
	2.5.2 Multi-Stage Problems	58
	2.6 Quality Metrics	67
	2.6.1 Expected Value of Perfect Information	68
	2.6.2 Value of the Stochastic Solution	71
	2.6.3 Out-of-Sample Assessment	76
	2.7 Risk	77
	2.8 Solving Stochastic Programming Problems	78
	2.9 Summary and Conclusions	80
	2.10 Exercises	80
	Chapter 3 Uncertainty Characterization via Scenarios	82
	3.1 Introduction	82
	3.2 Scenario Generation	85
	3.2.1 Overview	85
	3.2.2 Scenario Generation using ARIMA Models	87
	3.2.3 Generating Scenarios for Unit Availability	94
	3.2.4 Quality of Scenario Subsets	97
	3.3 Scenario Reduction	99
	3.3.1 Motivation	99
	3.3.2 Scenario Reduction Using a Probability Distance	100
	3.3.3 Algorithm	101
	3.4 Scenario Generation for Dependent Stochastic Processes	111
	3.4.1 Overview	111
	3.4.2 Scenarios for contemporaneous or quasi-contemporaneous stochastic processes	113
	3.4.3 Scenarios for non-contemporaneous stochastic processes	120
	3.5 Case Studies	122
	3.5.1 Scenario Generation Using ARIMA and Dynamic Regression models: Electricity Price and Demand	122
	3.5.2 Scenario Generation for Quasi-contemporaneous Stochastic Processes: Wind Speeds at Multiple Sites	127
	3.6 Summary and Conclusions	134
	3.7 Exercises	136
	Chapter 4 Risk management	139
	4.1 Introduction	139
	4.2 Risk Control in Stochastic Programming Problems	140
	4.2.1 Risk-Neutral Decision Making	140
	4.2.2 Risk-Averse Decision Making	144
	4.3 Risk Measures	146
	4.3.1 Variance	147
	4.3.2 Shortfall Probability	150
	4.3.3 Expected Shortage	153
	4.3.4 Value-at-Risk	157
	4.3.5 Conditional Value-at-Risk	160
	4.3.6 Stochastic Dominance	163
	4.4 Summary and Conclusions	170
	4.5 Exercises	172
	Chapter 5 Producer Pool Trading	175
	5.1 Introduction	175
	5.2 Decision Framework	176
	5.3 Uncertainty Characterization	179
	5.3.1 Day-ahead, Regulation, and Adjustment Prices	179
	5.3.2 Scenario Tree	181
	5.4 Pool Structure	184