论文标题
通过可再生能源转移基于深钢筋学习的大规模V2G连续充电协调
Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources
论文作者
论文摘要
由于电动汽车（EV）的普及以及电动汽车电子的技术进步，已经开发了车辆到网格（V2G）技术和大规模调度算法，以实现高水平的可再生能源和电力网格稳定性。本文提出了一种深入的加固学习方法（DRL）方法，用于使用可再生能源（RES）以V2G模式（RES）在V2G模式下汇总大规模电动汽车的连续充电/放电策略。 DRL协调策略可以有效地优化电动汽车聚合器（EVA）的实时充电/放电能力，并使用EVA和个人EV的限制状态（SOC）限制。与不受控制的充电相比，负载差异减少了97.37 $ \％$，充电成本降低了76.56 $ \％$。 DRL协调策略进一步证明了使用RES和大规模EVA以及复杂的每周时间表的微电网的出色转移学习能力。 DRL协调策略在现实的操作条件下显示了大规模V2G的灵活，适应性和可扩展性能。
Due to the increasing popularity of electric vehicles (EVs) and the technological advancement of EV electronics, the vehicle-to-grid (V2G) technique and large-scale scheduling algorithms have been developed to achieve a high level of renewable energy and power grid stability. This paper proposes a deep reinforcement learning (DRL) method for the continuous charging/discharging coordination strategy in aggregating large-scale EVs in V2G mode with renewable energy sources (RES). The DRL coordination strategy can efficiently optimize the electric vehicle aggregator's (EVA's) real-time charging/discharging power with the state of charge (SOC) constraints of the EVA and the individual EV. Compared with uncontrolled charging, the load variance is reduced by 97.37$\%$ 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.
