The Research On PSO For Bidding Strategy For Generation Company

The power industry in China is now under the way of restructuring and generation markets are expected to be established. In the new electricity market environments, profits of generation companies depend, to a large extent, on bidding strategies employed. Although much research works on developing optimal bidding strategies for generation companies have 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. The thesis focuses on the particle swarm algorithm and CRP (conditional robust profit) mode concerning building bidding strategies for generation companies, and achieved results.The specific research work questions are as follows:1. Study the power generation companies bidding strategy model, analyzed the advantages and disadvantages.2. In view of competitors bidding strategy model is estimated, using an improved two-tier particle swarm optimization I solved EEE30 node in the electricity market. Calculate the profits and the parameters, compared other methods'conclusions, some of the results of the existence of advantages.3. Study the CVaR bidding strategy in the power company's application, and CRP model applied to power companies bidding strategy, and add ISO scheduling model and competitors, to carry out Monte Carlo simulation, calculate CRP power companies bidding strategy model. Finally, IEEE 4 nodes in the electricity market environment use PSO to calculate the results to prove the advantages.