English, Article, Journal or magazine article edition: Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing Nicolaisen, James; Petrov, Valentin; Tesfatsion, Leigh

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Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing
Author
  • Nicolaisen, James
  • Petrov, Valentin
  • Tesfatsion, Leigh
Physical Description
  • preprint
Notes
  • This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modifed Roth-Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier electricity study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms. Related work can be accessed at: http:/​/​www.econ.iastate.edu/​tesfatsi/​AMESMarketHome.htm
  • Wholesale electricity market, market power, market efficiency, mult-agent learning, agent-based test bed
  • RePEc:isu:genres:1952
Language
  • English
Contributed by
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