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

Author

Listed:
  • James Nicolaisen

    (Iowa State University)

  • Valentin Petrov

    (Iowa State University)

  • Leigh Tesfatsion

    (Iowa State University)

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 mid-point pricing. Buyers and sellers use Roth-Erev individual reinforcement learning to determine their price and quantity offers in each auction round. It is shown that market microstructure is strongly predictive for the relative market power of buyers and sellers, and that high market efficiency is generally attained. These findings are robust for tested changes in individual learning parameters. It is also shown that similar relative market power findings are obtained if the electricity buyer and seller populations instead each engage in social mimicry learning via a genetic algorithm. However, market efficiency is substantially reduced.

Suggested Citation

  • James Nicolaisen & Valentin Petrov & Leigh Tesfatsion, 2000. "Market Power and Efficiency in a Computational Electricity Market with Discriminatory Double-Auction Pricing," Computational Economics 0004005, EconWPA.
    • Handle: RePEc:wpa:wuwpco:0004005
    Note: Type of Document - pdf file; prepared on IBM PC -MSWord; to print on HP/PostScript/; pages: 25 ; figures: included
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    References listed on IDEAS

    as
    1. Paul Klemperer, 2002. "What Really Matters in Auction Design," Journal of Economic Perspectives, American Economic Association, vol. 16(1), pages 169-189, Winter.
    2. 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.
    3. 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.
    4. Klemperer, Paul, 1999. " Auction Theory: A Guide to the Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 13(3), pages 227-286, July.
    5. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    6. 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 531-546, May.
    7. 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.
    8. 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.
    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 119-137, February.
    10. Green, Richard J & Newbery, David M, 1992. "Competition in the British Electricity Spot Market," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 929-953, October.
    11. 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.
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    More about this item

    Keywords

    Wholesale electricity market; Electricity restructuring; Double auction; Market power; Efficiency; Concentration; Capacity; Agent-based computational economics; Roth-Erev reinforcement learning; Genetic algorithm social learning.;

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance
    • L5 - Industrial Organization - - Regulation and Industrial Policy
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments

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