Study on Bidding Strategy for Aggregation Merchants of Electric Vehicle Clusters Using an Expectation-Entropy-Skewness Structural Model Based on Credibility Theory

With the progressive maturation of battery technology and the rapid advancement of new energy solutions, plug-in electric vehicles (PEVs) have become a vital component of the future electric grid, and their aggregated utilization as ancillary frequency regulation resources has emerged as a current research focus. To better manage and harness the potential of electric vehicle resources, aggregators have been introduced. This study addresses the strategy of aggregators participating in bidding in the Day-Ahead Market (DAM) and proposes a three-objective structure model based on fuzzy theory, incorporating expected value, skewness, and entropy. The improved NSGA-II optimization algorithm is employed to solve the model. Simulation results demonstrate the model's ability to offer diversified bidding strategy choices for aggregators, enhancing the economic viability and stability of ancillary services. This research holds significant implications for the rational utilization of power system flexibility resources and the integration of new energy solutions.