Abstract
A focus of study in auction market design is revenue comparison. Revenue Equivalence Theorem implies that bidders receive the same amount of revenue invariant to the auction type. This paper explores the applicability of this theorem in the context of the electricity market. We develop experimental test cases using agent-based simulation to examine the impact of different pricing rule on total dispatch cost. Using Q-learning in a repetitive trading environment, generator agents can quickly learn the market characteristics and seek to maximize their revenue by adapting their bidding strategies. A look-up table is utilized as a learning mechanism to improve the agent's bidding strategies. We conclude that Revenue Equivalence Theorem holds in a multi-unitmulti- period in symmetric bidding. In asymmetric bidding and when the market share of generator agents differs significantly, the computer simulation allows us to observe the rapid increase in revenue with uniform auction.
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