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Supply-side gaming on electricity markets with physical constrained transmission network

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)

  • Federico Delfino

    (DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture - UniGe - Università degli studi di Genova = University of Genoa)

  • Renato Procopio

    (DITEN - Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni / Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture - UniGe - Università degli studi di Genova = University of Genoa)

  • Marco Ruga

    (Chercheur indépendant)

Abstract

This paper proposes an agent-based computational approach to study physical constrained electricity markets. The computational model consists of repeated day-ahead market sessions and a two-zone transmission network. Different inelastic load serving entities configurations are considered for studying how producers learn to strategically decommit their units and how they exercise market power by profiting from transmission network constraints. Learning producers are modeled by different multi-agent learning algorithms, such as the Q-Learning, the EWA learning and the GIGA-WoLF. Computational results point out that all learning models considered are able to learn to appropriately decommit their units and to sustain the exertion of zonal market power.

Suggested Citation

  • Eric Guerci & Mohammad Ali Rastegar & Silvano Cincotti & Federico Delfino & Renato Procopio & Marco Ruga, 2008. "Supply-side gaming on electricity markets with physical constrained transmission network," Post-Print halshs-00871130, HAL.
    • Handle: RePEc:hal:journl:halshs-00871130
    DOI: 10.1109/EEM.2008.4579076
    as

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