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Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing

  • Nicolaisen, James
  • Petrov, Valentin
  • Tesfatsion, Leigh S.

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://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 2050.

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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
Handle: RePEc:isu:genres:2050
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.eduEmail:


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  1. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
  2. Rust, John & Miller, John H. & Palmer, Richard, 1994. "Characterizing effective trading strategies : Insights from a computerized double auction tournament," Journal of Economic Dynamics and Control, Elsevier, vol. 18(1), pages 61-96, January.
  3. Tesfatsion, Leigh, 2001. "Structure, behavior, and market power in an evolutionary labor market with adaptive search," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 419-457, March.
  4. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
  5. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 561-592, March.
  6. Paul Klemperer, 2002. "What Really Matters in Auction Design," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 169-189, Winter.
  7. Klemperer, P., 1999. "Auction Theory: a Guide to the Literature," Economics Papers 1999-w12, Economics Group, Nuffield College, University of Oxford.
  8. Green, Richard & Newbery, David M G, 1991. "Competition in the British Electricity Spot Market," CEPR Discussion Papers 557, C.E.P.R. Discussion Papers.
  9. Gode, Dhananjay K & Sunder, Shyam, 1993. "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality," Journal of Political Economy, University of Chicago Press, vol. 101(1), pages 119-37, February.
  10. von der Fehr, Nils-Henrik Morch & Harbord, David, 1993. "Spot Market Competition in the UK Electricity Industry," Economic Journal, Royal Economic Society, vol. 103(418), pages 531-46, May.
  11. Roth, Alvin E. & Erev, Ido, 1995. "Learning in extensive-form games: Experimental data and simple dynamic models in the intermediate term," Games and Economic Behavior, Elsevier, vol. 8(1), pages 164-212.
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