Title :
Strategic Bidding Model for Power Generation Company Based on Repast Platform
Author :
Zhigang Zhang ; Guangwen Ma
Author_Institution :
Center of Energy Dev. Res., Sichuan Univ., Chengdu
Abstract :
The bidding strategies of power generations in the market are a dynamic and complex problem. It is difficult to analyze and computer with the traditional mathematical methods, which is conspicuous in the middle or long-term transactions. This paper proposes a model, it is the middle or long-term bidding strategy in two-tiers electricity market that is based on the optimal power flow (OPF) . Uncertainties in the outside world are regarded as the agent (Agent) of "external environment." Under this conditions, Agent through environment evaluation judges to select viable strategic. Through learning from experiences and opponent\´s behaviors, Agent guides the purpose of the best production. The adaptability and superiority of this model is tested based on repast with a standard IEEE-5 bus 6 notes test system.
Keywords :
learning (artificial intelligence); multi-agent systems; power engineering computing; power generation economics; power markets; IEEE-5 bus 6 notes test system; multiagent system; optimal power flow; power generation company; power market; reinforcement learning; repast platform; strategic bidding model; Biological system modeling; Computational modeling; Electricity supply industry; Graphical user interfaces; Libraries; Load flow; Object oriented modeling; Power generation; Pricing; Production;
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
DOI :
10.1109/APPEEC.2009.4918782