Deep Q-Network Based Optimal Charging Coordination of Electric Vehicles Considering Vehicle-to-Grid Technology

To further enhance the active participation of electric vehicles in grid interaction and reduce the decision-making costs for electric vehicle aggregators, this paper addresses the challenges in current EV charging and V2G (Vehicle-to-Grid) management. Considering the owners’ willingness to participate, an optimal charging and V2G model for EV charging stations based on a Deep Q-Network is established. The paper analyzes in detail the mutual influence between the level of EV owner participation and the strategies of EV aggregators. Based on the owners’ willingness and the physical constraints of the EVs, an evaluation metric for EV participation in charging scheduling is developed. The Deep Q-Network is employed to make decisions regarding EV participation, thereby enhancing the decision-making capability of the EV aggregator, reducing the instability of its scheduling plans, and improving the reliability of these plans. Simulation results demonstrate that this method can dynamically consider EV owners’ willingness to participate, adaptively optimize the scheduling margin ratio, make global decisions across multiple time periods, and formulate charging and V2G scheduling plans for the EV aggregator.