Publication

Abstract : Electricity forecasting is very important for the electricity generation and distribution markets from the consumer’s point of view. Over the last fifteen years, there has been a significant change in the patterns toward deregulation and restructuring of the power industry. Competition in the wholesale and retail markets together gives open access to the transmission network and attracts customers with many benefits. In this paper, ERCOT electricity market modeling, price analysis, and forecasting the demand based on the market participation using machine and deep learning techniques is considered to advance the profit among the Generating Companies (GenCos) for real smart single-ended bidding in the wholesale electricity market. For efficient market involvement to accommodate the congestion cost, the Locational Marginal Pricing (LMP) method is considered. The impact of the microgrid as a non-dispatchable load with the GenCos and Load Serving Entity (LSE) on an Eight-bus ERCOT system is developed and proposed intelligent bidding approach for effective GenCos participation in meeting the social welfare accommodating the optimal location of Microgrid using AMES python tool to enhance the market operation effectively.