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Article
Market power and efficiency in a computational electricity market with discriminatory double-auction pricing
IEEE Transactions on Evolutionary Computation
  • James Nicolaisen, Iowa State University
  • Valentin Petrov, Iowa State University
  • Leigh Tesfatsion, Iowa State University
Document Type
Article
Publication Version
Submitted Manuscript
Publication Date
1-1-2002
DOI
10.1109/4235.956714
Abstract
Abstract: 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 modified Roth-Erev individual reinforcement learning algorithm (1995) 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 study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms.
Comments

This is a working paper of an article from IEEE Transactions on Evolutionary Computation 5 (2002): 504, doi:10.1109/4235.956714

Citation Information
James Nicolaisen, Valentin Petrov and Leigh Tesfatsion. "Market power and efficiency in a computational electricity market with discriminatory double-auction pricing" IEEE Transactions on Evolutionary Computation Vol. 5 Iss. 5 (2002) p. 504 - 523
Available at: http://works.bepress.com/leigh-tesfatsion/69/