Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing
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 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. Annotated pointers to related work can be accessed here: http://www2.econ.iastate.edu/tesfatsi/aelect.htm
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|Date of creation:||01 Oct 2001|
|Date of revision:|
|Publication status:||Published in IEEE Transactions on Evolutionary Computation, October 2001, vol. 5 no. 5, pp. 504-523|
|Contact details of provider:|| Postal: Iowa State University, Dept. of Economics, 260 Heady Hall, Ames, IA 50011-1070|
Phone: +1 515.294.6741
Fax: +1 515.294.0221
Web page: http://www.econ.iastate.edu
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