Engineering interrelated electricity markets : an agent-based computational approach

Title:

Personal Author:

Weidlich, Anke

Series:

Contributions to management science

Publication Information:

Heidelberg : Physica-Verlag Heidelberg, 2008

Physical Description:

xxi, 174 p. : ill. ; 24 cm.

ISBN:

9783790820676

Subject Term:

Power resources -- Data processing

Energy industries -- Data processing

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010194123	HD9502.A2 W44 2008	Open Access Book	Book	Searching... Unknown

Due to the characteristics of electricity, power markets rank among the most complex markets operated at present. The requirements of an environmentally sustainable, economically efficient, and secure energy supply have resulted in the emergence of several interrelated markets that have to be carefully engineered in order to ensure efficient market outcomes.

This book presents an agent-based simulation model that facilitates electricity market research. Simulation outcomes from this model are validated against price data from German power markets. The results significantly contribute to existing research in agent-based simulation and electricity market modeling, and provide insights into the impact of the market structure and market design on electricity prices.

The book addresses researchers, lecturers and students who are interested in applying agent-based simulation to power markets. It provides a thorough discussion of the methodology and helpful details for model implementation.

List of Abbreviations	p. xiii
List of Figures	p. xv
List of Tables	p. xxi
Part I Motivation and Fundamentals
1 Introduction	p. 3
1.1 Objectives and Research Questions	p. 4
1.2 Thesis Structure	p. 5
2 Wholesale Electricity and Emissions Trading	p. 7
2.1 Electricity Marketplaces	p. 8
2.1.1 Power Exchanges	p. 8
2.1.2 Balancing Power Markets	p. 11
2.2 Emissions Trading	p. 14
2.3 Interrelations Between Day-Ahead, Balancing and Allowance Markets	p. 17
2.4 Summary	p. 19
3 Agent-Based Computational Economics	p. 21
3.1 Motivation for Agent-Based Methods in Economics	p. 22
3.2 Building Agent-Based Simulation Models	p. 25
3.2.1 Methodology and Main Concepts	p. 25
3.2.2 Validity of Agent-Based Simulation Models	p. 29
3.2.3 Software Toolkits for Agent-Based Simulation	p. 31
3.3 Related Work: ACE Electricity Market Models	p. 33
3.3.1 Simulations Applying Reinforcement Learning	p. 33
3.3.2 Simulations Applying Evolutionary Concepts	p. 39
3.3.3 Other Agent-Based Electricity Market Simulations	p. 42
3.3.4 Discussion of ACE Electricity Approaches	p. 49
3.4 Summary	p. 55
Part II An Agent-Based Simulation Model for Interrelated Electricity Markets
4 Representation of Learning and Adaptation	p. 59
4.1 Reinforcement and Belief-Based Learning	p. 60
4.1.1 Erev and Roth Reinforcement Learning	p. 62
4.1.2 Q-Learning	p. 64
4.1.3 Experience-Weighted Attraction	p. 64
4.2 Evolutionary Learning Models	p. 65
4.3 Analysis of Learning Algorithms for Agent-Based Simulations	p. 67
4.3.1 Criteria for Choosing a Learning Model	p. 67
4.3.2 Simulated Scenario	p. 70
4.3.3 Results	p. 72
4.3.4 Implications for Robust and Valid Agent-Based Simulations	p. 81
4.4 Summary	p. 83
5 The Electricity Sector Simulation Model	p. 85
5.1 Design of the Simulation Model	p. 85
5.1.1 Overall Model Structure	p. 86
5.1.2 The Day-Ahead Electricity Market Model	p. 89
5.1.3 The Balancing Power Market Model	p. 92
5.1.4 The Model of Emissions Trading	p. 94
5.1.5 Learning Reinforcements and Market Interrelations	p. 96
5.1.6 The Graphical User Interface	p. 97
5.2 Validation	p. 99
5.2.1 Simulation Results of the Reference Scenario	p. 100
5.2.2 Sensitivity Analysis	p. 106
5.3 Summary	p. 116
Part III Application, Evaluation and Discussion
6 Simulation Scenarios and Results	p. 121
6.1 Impact of Tendered Balancing Capacity on Electricity Prices	p. 121
6.1.1 Motivation	p. 121
6.1.2 Results	p. 123
6.2 Settlement Rules on the Balancing Power Market	p. 126
6.2.1 Motivation	p. 126
6.2.2 Results	p. 127
6.3 Increasing Supply Side Competition Through Divestiture	p. 130
6.3.1 Motivation	p. 130
6.3.2 Results	p. 131
6.4 Policy Implications and Summary	p. 135
7 Conclusion and Outlook	p. 137
7.1 Original Contribution	p. 137
7.2 Outlook on Possible Future Work	p. 140
Appendix
A Learning Model Testing Scenarios	p. 141
A.1 Definition of the Two-Dimensional Action Domain and Spillover of Reinforcement	p. 141
A.2 Simulation Results for Appropriate Learning Variants	p. 143
B Reference Scenario	p. 147
B.1 Demand Side Data Input for the Day-Ahead Market	p. 147
B.2 Supply Side Data Input: Generators and Plants	p. 148
B.3 Agent Characteristics on the CO[subscript 2] Market	p. 149
B.4 Simulation Results for the Reference Scenario	p. 150
B.5 Confidence Intervals of Sensitivity Analysis	p. 159
C Statistical Analysis of Market Scenarios	p. 161
C.1 Tendered Balancing Capacity	p. 161
C.2 BPM Settlement Rule	p. 162
C.3 Divestiture Scenarios	p. 163
References	p. 167

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