Multi-Level Collaborative Optimization Control Strategy for Interaction Between Electric Vehicles and the Power Grid

Electric vehicle and grid interaction technology is a key component for achieving energy transition and building a smart grid. This paper addresses the load fluctuations and regulation demands caused by large-scale electric vehicle integration into the grid, and proposes a multi-level coordinated optimization control strategy for electric vehicle and grid interaction. First, a three-level coordinated control architecture covering the regional dispatch layer, aggregator layer, and vehicle layer is constructed, clearly defining the functional positioning and information interaction mechanisms of each layer. Second, considering user travel demands and battery degradation costs, a game theory-based charging scheduling model is established, and a deep reinforcement learning algorithm is used to solve the multi-objective optimization problem. Simulation results show that the proposed strategy can smooth grid load fluctuations, improve renewable energy utilization, and effectively ensure user economic benefits and battery lifespan. This study provides theoretical support and technical reference for the safe and economical operation of the grid under the scenario of large-scale electric vehicle integration.