• DocumentCode
    173473
  • Title

    Competitive co-evolutionary approach to stochastic modeling in deregulated electricity market

  • Author

    Tiguercha, A. ; Ladjici, A.A. ; Boudour, Mohamed

  • Author_Institution
    Electr. Eng. Dept., Univ. of Sci. & Technol., Algiers, Algeria
  • fYear
    2014
  • fDate
    13-16 May 2014
  • Firstpage
    514
  • Lastpage
    519
  • Abstract
    the main purpose of the paper is to calculate supplier´s optimal biding in a deregulated electricity market, by calculating the Nash equilibrium strategies. In this paper we present the use of competitive coevolutionary algorithm in order to find the optimal biding strategies. A computational Algorithm has been developed to find Nash equilibrium strategies where a stochastic programming model is proposed to maximize the expected profits taking into account the stochastic aspect of spot market parameters. The key feature of our approach is the combination of a powerful learning algorithm to find the optimal strategies, and a scenario formulation to model the market uncertainties through. Each market agents is modeled as an adaptive evolutionary agent learning from market interactions and take part in the forward and spot transactions to act strategically to maximize their profits.
  • Keywords
    evolutionary computation; game theory; learning (artificial intelligence); power engineering computing; power markets; stochastic programming; Nash equilibrium strategy; adaptive evolutionary agent learning; competitive coevolutionary algorithm; deregulated electricity market; market equilibrium; market interactions; optimal bidding strategy; stochastic programming model; two-stage stochastic game; Adaptation models; Electricity supply industry; Games; Nash equilibrium; Sociology; Statistics; Stochastic processes; Competitive Coevolutionary Algorithm; Deregulated Electricity Market; Market Equilibrium; Two Stage Stochastic Game;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Energy Conference (ENERGYCON), 2014 IEEE International
  • Conference_Location
    Cavtat
  • Type

    conf

  • DOI
    10.1109/ENERGYCON.2014.6850475
  • Filename
    6850475