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Learning Oligopolistic Competition In Electricty Auctions


  • Eric Guerci

    () (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (... - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015 - 2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur, GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université - EHESS - École des hautes études en sciences sociales)

  • Stefano Ivaldi

    (Chercheur indépendant)

  • Marco Raberto

    (DIME - Dipartimento di ingegneria meccanica, energetica, gestionale e dei trasporti - Universita degli studi di Genova)

  • Silvano Cincotti

    (DIME - Dipartimento di ingegneria meccanica, energetica, gestionale e dei trasporti - Universita degli studi di Genova)


This paper addresses the problem of auction markets efficiency within the context of recently liberalized electricity markets. Two different auction mechanisms, i.e., the uniform and the discriminatory price setting rules, have been employed worldwide in designing electricity markets. In this paper, we study the relative efficiency of the two auction mechanisms in the framework of the learning-in-games approach. The behavior of electricity suppliers are modeled by means of an adaptive learning algorithm and the demand is assumed to be constant and inelastic, according to a common hypothesis in electricity market modeling. Computational experiments results are interpreted according game theoretical solutions, i.e., Nash equilibria and Pareto optima. Different economic scenarios corresponding to a duopoly and a tripoly competition with different level of demand are considered. Results show that in the proposed conditions, sellers learn to play competitive strategies, which correspond to Nash equilibria. Finally, this study establishes that, in the presented computational setting and economic scenarios, the discriminatory auction mechanism results more efficient than the uniform auction one.

Suggested Citation

  • Eric Guerci & Stefano Ivaldi & Marco Raberto & Silvano Cincotti, 2007. "Learning Oligopolistic Competition In Electricty Auctions," Post-Print halshs-00871017, HAL.
  • Handle: RePEc:hal:journl:halshs-00871017
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    Cited by:

    1. Eric Guerci & Stefano Ivaldi & Silvano Cincotti, 2008. "Learning Agents in an Artificial Power Exchange: Tacit Collusion, Market Power and Efficiency of Two Double-auction Mechanisms," Computational Economics, Springer;Society for Computational Economics, vol. 32(1), pages 73-98, September.


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