Optimal Dispatch Strategy for Electric Vehicle Aggregators Based on Stackelberg Game Theory Considering User Intention

As a new type of load, large-scale electric vehicles (EV) have great potential in participating in the reserve market and realizing load shaving and valley filling. However, due to the uncertainty of the user intention to participate in the regulation of electric vehicles aggregators (EVA), it is difficult to accurately quantify the reserve capacity of EV clusters, which affects the optimal decision-making of aggregators to participate in the market. Aiming at the uncertainty of user intention and the conflict of interests between EVA and EV users, this paper proposed an EVA Stackelberg game optimization dispatch strategy considering user intention. First of all, the anxiety of battery loss and the anxiety of time to flexibly use EV are portrayed respectively through the coefficient of exclusion psychology combined with Exponential function and decision factors combined with EV online time, and the irrational behavior of users in the decision-making process is reflected through the binomial distribution to determine whether a single user is willing to participate in EVA regulation, so as to quantify the intention of EV user clusters to participate in EVA regulation. Secondly, taking into account user intention, an EVA-EV Stackelberg game model is established with the goal of maximizing self-benefits. A probability distribution transformation and feedback loop iteration solution method of the intention model is proposed to achieve rapid convergence of the dual objective game problem, and the optimal reserve compensation price for EVA and the reserve capacity that the EV cluster can provide under its stimulation are obtained. Finally, the effectiveness of the intention representation method and the feasibility of the iterative solution method of the proposed Stackelberg game model are proved through a numerical example. It can be seen that the Stackelberg game model can significantly improve the economy of EVA and EV users, while achieving load peak shaving and valley filling, and promoting the Economic security operation of the grid. From the perspective of time cost and economic cost, the EV user intention model of the anxiety of battery loss and the anxiety of time to flexibly use EV is described, and the user intention evaluation is carried out based on the binomial distribution, which fully considers the uncertainty in the actual decision-making process of EV users. When the EV cluster reaches a certain size, the intention model is stable and feasible. Based on the above intention model, construct a Stackelberg game with EVA as the main body, achieve price incentives for EV users through cyclic iterative feedback, and optimize EV users' intention to make decisions based on price incentives. Balance the conflict of interest between EVA and EV users through real-time information exchange feedback mechanism, and develop the optimal reserve compensation price for EVA, achieving overall benefit balance between the two. The Stackelberg game model is established on the basis of an optimization model guided by the time-of-use electricity prices, which can achieve effective peak shaving and valley filling effects while achieving economic goals, reduce the peak valley difference of power grid load, and is of great significance for maintaining stable operation of the grid. © 2024 China Machine Press. All rights reserved.