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Operator's bidding strategies in the liberalized Italian power market

Author

Listed:
  • Eric Guerci

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur, GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Mohammad Ali Rastegar

    (Chercheur indépendant)

  • Silvano Cincotti

    (DIME - Dipartimento di ingegneria meccanica, energetica, gestionale e dei trasporti - UniGe - Università degli studi di Genova = University of Genoa)

Abstract

This paper studies the Italian wholesale electricity market by means of a realistic agent-based computational model of the day-ahead market session, of the thermal-power production pool and of the Italian high-voltage transmission network. The aim of the paper is twofold. Firstly, it studies how the strategic behavior of the thermal power plants can influence the level of price at a national level. Secondly, it performs an empirical validation of the computational model over a period of one month which enables to assess the validity of the proposed model. In particular, three scenarios are studied and compared, i.e., the historical performance, a marginal cost based case and a strategic case where generation companies learn according to a reinforcement learning algorithm their best strategy. Results show that the strategic model reproduces real price dynamic during low- and medium- demand periods, whereas during peak-hours the strategic model tends to underestimate historical performances.

Suggested Citation

  • Eric Guerci & Mohammad Ali Rastegar & Silvano Cincotti, 2009. "Operator's bidding strategies in the liberalized Italian power market," Post-Print halshs-00871077, HAL.
    • Handle: RePEc:hal:journl:halshs-00871077
    DOI: 10.1007/978-3-642-02956-1_5
    as

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