Selective learning for strategic bidding in uniform pricing electricity spot market

In an electricity market, power producers' profits are determined by the market price instead of the regulated price. Therefore, the producers should be cautious in strategic bidding, for which prediction-based approaches are widely used and have been proved effective for many areas. However, in a uniform pricing market, the market environment is so complicated, which is primarily due to the complexity of the participants' interaction, that even the strategies based on machine learning algorithms, which are generally considered as outstanding nonlinear prediction methods, may sometimes lead to unsatisfactory results. Therefore, a selective learning scheme for strategic bidding is proposed to ensure greater effectiveness. The proposed scheme is based on an ensemble technique, where several machine learning algorithms serve as the underlying algorithms to predict the price and generate a bidding recommendation. As the clearing iteration progresses, the most fitting ones will be chosen to dominate the bidding strategy. Considering the characteristics of the electricity market, the prediction method used in the selective learning scheme is modified to achieve higher accuracy. Simulation studies are presented to demonstrate the effectiveness of the proposed scheme, which leads to more reasonable bidding behaviors and higher profits.