Interaction Strategy Between the Vehicle-station-grid Considering Privacy Protection

Abstract The number of electric vehicles (EVs) is increasing daily, and the charging demand for large-scale EVs has a greater impact on the distribution network, so it is urgent to study the cooperative interaction optimization strategy of the “EV-charging station-distribution grid.” In this paper, we take the charging load of EVs in the regional distribution network as the research object. We establish a multi-intelligence reinforcement learning-based optimization strategy for the interaction between EVs and the grid to obtain the optimal charging decision to minimize the peak-to-valley difference between the user-side economy and the grid-side load. The results show that the proposed EV-grid interaction optimization strategy can effectively reduce the peak-to-valley difference in charging station load and realize the efficient cooperative interaction of the “vehicle-station-grid.”