An Optimal Competitive Bidding and Pricing Strategy for Electric Vehicle Aggregator Considering the Bounded Rationality of Users

Accurate estimation of the charging demand of electric vehicle (EV) users has a profound impact on the profitability of the EV aggregator (EVA). Existing studies concerning the market strategy of EVA normally assume that EV users are equipped with complete rationality and simplify the real interactive process among EVAs and EV users, making the derived strategy unable to adapt to the practical situations. To address this bottleneck, a novel competitive bidding and pricing strategy for EVA considering both the bounded rationality of EV users and various game relationships among multiple agents is proposed. Firstly, the evolutionary game theory is utilized to simulate the boundedly rational charging behaviors of EV users under differentiated charging prices, while their demand elasticity and uncertainty of driving pattern is also taken into account. Secondly, the complex interactions among EVAs and EV users are modeled, where the competition between EVAs is constructed by a non-cooperative game with incomplete information and the interplay between EVAs and EV users is built as a Stackelberg game. Finally, numerical results validate the effectiveness of the proposed method and demonstrate the pricing strategy preferences of EVAs based on the diverse price elastic coefficients of EV users.