The day-ahead electricity market is modeled as a multi-agent system with interacting agents including supplier agents, load-serving entities, and a market operator. Simulation of the market clearing results under the scenario in which agents have learning capabilities is compared with the scenario where agents report true marginal costs. It is shown that, with Q-learning, electricity suppliers are making more profits compared to the scenario without learning due to strategic gaming. As a result, the LMP at each bus is substantially higher. Related work can be accessed at: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/aelect.htm
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Paper provided by Iowa State University, Department of Economics in its series Staff General Research Papers with number
12976.
Length: Date of creation: 19 Aug 2008 Date of revision: Publication status: Published in International Journal of Engineering Intelligent Systems, 2007, Vol. 15, No. 2, pp. 115-121. Handle: RePEc:isu:genres:12976
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Find related papers by JEL classification: B4 - Schools of Economic Thought and Methodology - - Economic Methodology C0 - Mathematical and Quantitative Methods - - General C6 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming C7 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory D4 - Microeconomics - - Market Structure and Pricing D43 - Microeconomics - - Market Structure and Pricing - - - Oligopoly and Other Forms of Market Imperfection D8 - Microeconomics - - Information, Knowledge, and Uncertainty L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
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