• DocumentCode
    3352570
  • Title

    Effect of Agent´s Action Domain Representation Method in Agent-Based Electricity Market Simulation

  • Author

    Jing, Zhaoxia ; Chen, Haoyong ; Ngan, H.W. ; Wang, Jianhui

  • Author_Institution
    Sch. of Electr. Eng., South China Univ. of Technol., Guangzhou
  • fYear
    2009
  • fDate
    27-31 March 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Bid format is a crucial feature of electricity market mechanism. Correspondingly, in agent based electricity market simulation, how to model agent´s bid format and represent action domain is also an important aspect to construct a valid learning method. In this paper, two methods to generator agents´ action domains are presented and their effects to the simulation result are analyzed on a 4-generator system. The result shows that in agent-based simulation, agent action domain representation can strongly affect the simulation result. With all other parameters the same, different action domain generating methods lead to different average clearing prices which means different capability of executing market power.
  • Keywords
    learning (artificial intelligence); multi-agent systems; power engineering computing; power markets; agent action domain representation method; agent-based electricity market simulation; learning method; reinforcement learning; Costs; Electricity supply industry; Laboratories; Learning systems; Microeconomics; Power generation economics; Power system dynamics; Power system economics; Power system interconnection; Power system modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-2486-3
  • Electronic_ISBN
    978-1-4244-2487-0
  • Type

    conf

  • DOI
    10.1109/APPEEC.2009.4918312
  • Filename
    4918312