Due to the increasing popularity of electric vehicles (EVs) and the technological advancement of EV electronics, the vehicle-to-grid (V2G) technique and large-scale scheduling algorithms have been developed to achieve a high level of renewable energy and power grid stability. This paper proposes a deep reinforcement learning (DRL) method for the continuous charging/discharging coordination strategy in aggregating large-scale EVs in V2G mode with renewable energy sources (RES). The DRL coordination strategy can efficiently optimize the electric vehicle aggregator's (EVA's) real-time charging/discharging power with the state of charge (SOC) constraints of the EVA and the individual EV. Compared with uncontrolled charging, the load variance is reduced by 97.37$\%$ and the charging cost by 76.56$\%$. The DRL coordination strategy further demonstrates outstanding transfer learning ability to microgrids with RES and large-scale EVA, as well as the complicated weekly scheduling. The DRL coordination strategy demonstrates flexible, adaptable, and scalable performance for the large-scale V2G under realistic operating conditions.

翻译：由于电动车辆越来越受欢迎和EV电子技术的进步,车辆对电网技术和大规模排期算法已经发展,以实现高水平的可再生能源和电网稳定,本文件建议采取深入强化学习(DRL)方法,以持续收费/中断协调战略,将V2G模式中的大规模EV与可再生能源(RES)相结合。DRL协调战略可以有效地优化电动车辆隔离器(EVA)的实时充电/配电能力,使其受到EVA和个人的电源(SOC)限制。与无节制收费相比,负载差异减少97.37美元,收费减少76.56美元。DRL协调战略进一步表明,在现实操作条件下,向有RES和大型EVA的微型电网转移学习能力以及复杂的每周排期。DR协调战略展示了大规模V2G的灵活、可调整和可扩展性业绩。