Bidding Strategy Of Power Generation Company Using Chance-constrained Programming

The world-wide introduction of competition among power generators to break monopoly aims at developing power markets and achieving efficient utilization of social resources. In the all new electricity market environments, competition has been introduced in generation sector through bid-based operation. Generation companies must build optimal bidding strategies to maximize profits when different conditions and situations of generation companies are considered. Hence, how to develop optimal bidding strategies when generation cost is certain has become a major concern of generation companies. Although much research on developing optimal bidding strategies for generation companies has been done, there are still many difficult problems to be solved. Moreover, bidding strategies need to be updated accordingly with the development of electricity markets. First, this paper gives a comprehensive introduction to recent research work in strategic bidding. The emphasis is put on approaches of strategic bidding of generation companies, characteristics of electricity market and trade model, as well as trade style and market rules of electricity market in China. Secondly, it discusses generation cost, including cost control, the cost analysis and curve of cost of generation companies. Thirdly, based on traditional bidding strategies model, the chance-constrained programming is led into the bidding problem, in which a lot of uncertainty factors are involved, and a risk-profit model is established. According to the concept of risk preference, the profit situation of generation companies under different risks is analyzed. Simulation results of an electricity market with several generation companies show that the proposed model is effective. Finally, a new bidding strategy based on utility theory is proposed in this paper. It is carried out by evaluating prices risk of bidding using chance-constrained programming and is accessed using utility theory. The feasibility and effectiveness of the proposed method are proved by simulation of example systems.