A Prediction Based Bidding Strategy for Social Welfare Improvement in Uniform Pricing Mechanism

Abstract

In the new competitive electricity market, bidding plays a significant role in the area of power trading. The participants are partaking in the trading procedure for a fixed amount of power. The price at which a single buyer bids a block of power influences both the net volume of power cleared and the market-clearing price of electricity traded in the entire network. In this paper, different bidding strategies are defined and simulated. The defined strategies show the dependency on the bid price for clearing a bid. The net earnings of the buyers and sellers depend on the bid price. The selection of bid price for each block of power is studied such that the total volume of power cleared is equal to the participating buyers’ total power demand. The study focuses on determining the optimal bid to result in maximum societal benefit. In addition to the bid price effect on the volume of power cleared, the bid price prediction is also performed in this work using linear regression. The predicted Locational Marginal Price (LMP) of the buyers for different volumes of power cleared is estimated. The results are further compared with LMP obtained using an ordinary load flow problem through which a margin change in the net earnings is observed. An IEEE-30 bus system is simulated with different bidding strategies using MATLAB, and the predicted LMP shows a significant increase in net earnings.