Research On Bidding Strategies For Power Plants In The Electric Power Market

In the electric power market, power plants confronts with a brand-new and important problem, which is how to bid properly in order to maximize the profit of enterprises.This paper concerns with the bidding strategy for power plants in electric power markets, analyzes the factors which affect bidding, builds the model with the history load data of Shandong province, and forecasts the short time market clearing price (MCP) basing on the BP neural network. The example of Shandong shows the process of the method. Furthermore, the bidding behaviors of rival power plants are forecasted using Cournot model of game theory. Two kinds of equilibrium solutions are introduced under complete and incomplete information of game theory. Especially, the random optimized model is built using Monte Carlo simulation under incomplete information for the power plants. The bidding results show that the efficiency of the proposed approach.