• DocumentCode
    2223653
  • Title

    Multi-agent approach for profit based unit commitment

  • Author

    Sharma, Deepak ; Srinivasan, Dipti ; Trivedi, Anupam

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore (NUS), Singapore, Singapore
  • fYear
    2011
  • fDate
    5-8 June 2011
  • Firstpage
    2527
  • Lastpage
    2533
  • Abstract
    Deregulation in the electricity market offers freedom to the generator companies (GENCOs) to schedule their generators in order to maximize their profit without actually satisfying the load and the reserve requirements. Various techniques have been developed for solving the profit based unit commitment (PBUC) problem. Among them, the multi-agent approach is different where each generator unit is referred to as an intelligent agent. In this paper, we develop a new multi-agent approach for PBUC problem in which the rule based intelligence is provided to the independent system operator (ISO) agent. Intelligence of generator agents (GenAgents) is limited to maximize their profit for the given demand and reserve using real-parametric genetic algorithm (GA) and share the results with ISO agent. In this approach, ISO agent commits the maximum profit generating GenAgents for every hour while satisfying the up/down time constraints. ISO agent also asks other GenAgents to calculate their profit for the remaining demand and reserve. The simulation results of 10 units problem for two payment methods are shown and compared with other techniques.
  • Keywords
    genetic algorithms; knowledge based systems; multi-agent systems; power engineering computing; power markets; electricity market; generator agents intelligence; generator companies; independent system operator agent; intelligent agent; multiagent approach; profit based unit commitment problem; real-parametric genetic algorithm; rule based intelligence; Equations; Generators; Genetic algorithms; ISO; ISO standards; Mathematical model; Time factors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation (CEC), 2011 IEEE Congress on
  • Conference_Location
    New Orleans, LA
  • ISSN
    Pending
  • Print_ISBN
    978-1-4244-7834-7
  • Type

    conf

  • DOI
    10.1109/CEC.2011.5949932
  • Filename
    5949932