• DocumentCode
    2577205
  • Title

    A bilevel optimization model and a PSO-based algorithm in day-ahead electricity markets

  • Author

    Zhang, Guoli ; Zhang, Guangquan ; Gao, Ya ; Lu, Jie

  • Author_Institution
    Dept. of Math. & Phys., North China Electr. Power Univ., Baoding, China
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    611
  • Lastpage
    616
  • Abstract
    Strategic bidding problems are becoming key issues in competitive electricity markets. This paper applies bilevel optimization theory to deal with this issue. We first analyze generating company strategic bidding behaviors and build a bilevel optimization model for a day-ahead electricity market. In this bilevel optimization model, each generating company will choose their bids in order to maximize their individual profits. A market operator will determine the output power for each unit and uniform marginal price based on the minimization purchase electricity fare. For solving this competitive strategic bidding problem described by the bilevel optimization model, a particle swarm optimization (PSO)-based algorithm is. Experiment results have demonstrated the validity of the PSO-based algorithm in solving the competitive strategic bidding problems for a day-ahead electricity market.
  • Keywords
    particle swarm optimisation; power markets; pricing; bilevel optimization model; competitive electricity markets; day-ahead electricity markets; minimization purchase electricity fare; particle swarm algorithm; strategic bidding problems; uniform marginal price; Constraint optimization; Cybernetics; Electricity supply industry; Energy management; Mathematical model; Nash equilibrium; Particle swarm optimization; Power generation; Power system modeling; USA Councils; bilevel programming; electricity market; optimization; particle swarm algorithm; strategic bidding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
  • Conference_Location
    San Antonio, TX
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4244-2793-2
  • Electronic_ISBN
    1062-922X
  • Type

    conf

  • DOI
    10.1109/ICSMC.2009.5346635
  • Filename
    5346635