Effective energy management of electric vehicle (EV) charging stations is critical to supporting the transport sector's sustainable energy transition. This paper addresses the EV charging coordination by considering vehicle-to-vehicle (V2V) energy exchange as the flexibility to harness in EV charging stations. Moreover, this paper takes into account EV user experiences, such as charging satisfaction and fairness. We propose a Multi-Agent Reinforcement Learning (MARL) approach to coordinate EV charging with V2V energy exchange while considering uncertainties in the EV arrival time, energy price, and solar energy generation. The exploration capability of MARL is enhanced by introducing parameter noise into MARL's neural network models. Experimental results demonstrate the superior performance and scalability of our proposed method compared to traditional optimization baselines. The decentralized execution of the algorithm enables it to effectively deal with partial system faults in the charging station.

翻译：电动汽车充电站的有效能源管理对于支持交通领域的可持续能源转型至关重要。本文通过考虑电动汽车之间的能量交换（车-车能量交换）作为充电站的灵活性资源，来协调电动汽车充电。同时，本文考虑了电动汽车用户的使用体验，如充电满意度和公平性。我们提出了一种基于多智能体强化学习的方法，用于在电动汽车到达时间、能源价格和太阳能发电存在不确定性的情况下，协调电动汽车充电与车-车能量交换。通过在强化学习的神经网络模型中引入参数噪声，增强了该方法的探索能力。实验结果表明，与传统优化基线方法相比，我们提出的方法具有更优越的性能和可扩展性。该算法的分散式执行使其能够有效应对充电站中的部分系统故障。