Modeling and Simulation of Bidding Activities of Power Generation Companies by Multi-agent
In the power market which is introduced competition, a power generation company as an unattached economic entity needs to determine the optimal bidding strategy in order to get the most income. In allusion to this problem, power generation companies’ bidding behavior is modeled and simulated on Repast platform with an idea of Multi-Agent which combines both theories of game and complex adaptive system. The developed model is an incomplete information game model in which each agent (player) can accumulate experience gained during its competition bidding and modify its predict function at every bidding so as to get the maximal payoff. Self-learning ability and alternating behavior of the population of power generation companies are both taken into account. The simulation results show the presented modeling and simulation method are effective and RePast platform is an effective tool for power market simulation research.