Using Q-learning to model bidding behaviour in electricity market simulation

Zhigang Liao, Ly Fie Sugianto

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

4 Citations (Scopus)

Abstract

While the choice between two pricing rules, namely Uniform pricing rule and Pay-as-bid pricing rule, has led to a continuous debate in the electricity market establishment process, little attention has been paid to the Vickrey pricing rule. This paper presents an agent-based model to examine the employment of Uniform and Vickrey pricing rules in a deregulated electricity market. Using Q-learning in repetitive trading process, generator agents learn the market characteristics and seek to maximise their revenue by exploring bidding strategies. A look up table is utilised to memorise agents' bidding experience that help the agents improve their strategies. Supply quantity withholding and generators' collusion phenomenon have been observed in this study under certain market arrangements. The implication of these two pricing rules on the total dispatch costs and generators' profit are discussed in this paper.

Original languageEnglish
Title of host publicationIEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011
Subtitle of host publication2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making
Pages1-7
Number of pages7
DOIs
Publication statusPublished - 10 Aug 2011
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, MCDM 2011 - Paris, France
Duration: 11 Apr 201115 Apr 2011

Publication series

NameIEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making

Conference

ConferenceSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, MCDM 2011
CountryFrance
CityParis
Period11/04/1115/04/11

Keywords

  • agent-based model
  • auction market
  • bidding behaviour
  • pricing rules
  • Q-Learning
  • simulation

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