• DocumentCode
    3360084
  • Title

    Strategy Bidding of Power Generation Enterprises Based on Energy-Saving Generation Dispatching Pattern

  • Author

    Xiao, Fei-Peng ; Weijun Huang

  • Author_Institution
    Inst. of Mountain Hazards & Environ., Chinese Acad. of Sci., Chengdu
  • fYear
    2009
  • fDate
    27-31 March 2009
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The bidding strategies based on energy-saving generation dispatching pattern are dynamic and complex problems. It is very difficult to analyze and compute with the traditional mathematical methods, which is particularly conspicuous in the middle-or long-term transactions. This paper proposes a model about the optimized middle or long-term bidding strategy in two-tiers electricity market, which is based on the optimal power flow (OPF). In this model, uncertainties in the outside world are regarded as the agent (Agent) of "external environment". Under the condition, the agent selects a viable strategy by environment evaluating and guides the purpose of the optimal production by learning from past experiences and competitors\´ behaviors. The adaptability and superiority of this model are tested on a standard IEEE-5 bus 6 notes test system.
  • Keywords
    energy conservation; load flow; power generation dispatch; power generation economics; power markets; IEEE-5 bus test system; bidding strategy; energy-saving generation dispatching pattern; optimal power flow; power generation enterprise; two-tier electricity market; Dispatching; Dynamic programming; Electricity supply industry; Load flow; Power generation; Power generation dispatch; Power markets; Power system dynamics; Power system modeling; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-2486-3
  • Electronic_ISBN
    978-1-4244-2487-0
  • Type

    conf

  • DOI
    10.1109/APPEEC.2009.4918783
  • Filename
    4918783