An Optimal Bidding Strategy for Social Welfare Maximization of Microgrid

Abstract

Competition in power markets implies benefit to the generators as well as customer. The uncertain nature of bids and behavior of renewables makes the bidding strategy more complex. The power market comprises of Generation companies (GENCOS), Transmission companies (TRANSCOs) and Distribution companies (DISCOs) which actively participate in the auction process. In this paper, a new bidding strategy for a microgrid in day-ahead market is proposed for social welfare maximization considering all constraints. The intermittent sources such as solar, wind power are mitigated in order to get optimal scheduling of the resources. Analog Ensemble method is used to forecast the solar and wind power to minimize intermittent nature of renewables. Moth Flame Algorithm (MFO) is used to optimize the generation cost of the microgrid and then compare the bidding strategy with the uniform pricing method and found to be better than uniform pricing method. Market Clearing Price (MCP) is cleared with the proposed bidding strategy with the transaction matching rule and found to be better than the uniform pricing.

Keywords: Market Clearing Price (MCP), Analog Ensemble (AE), Moth Flame Optimization (MFO), bidding strategy.

Cite this Article: Ankit Kumar Singh, Rajesh Panda, Prashant Kumar Tiwari. An Optimal Bidding Strategy for Social Welfare Maximization of Microgrid. Journal of Network Security. 2020; 8(1): 11–21p.