基于深度强化学习的大规模电动汽车充换电负荷优化调度

Optimal Scheduling of Electric Vehicle Load for Large-scale Battery Charging and Swapping Based on Deep Reinforcement Learning

针对大规模充换电站的聚合优化调度问题,提出一种基于SAC深度强化学习的充换电负荷实时优化调度策略。该策略充分考虑了负荷调控过程中的用户因素、系统因素和市场因素,能够实现大规模电动汽车与各类电力系统主体的友好互动。首先,考虑充换电站的发展规模和调度性能建立联合运行框架;其次,提出考虑多重用户特征的可调性识别模型对电动汽车的实际可调性进行判断;进而,考虑充换电站优化调度的多重时空特征,构建不同场景下可调充换电负荷的优化调度模型;然后,基于SAC算法求解并网充换电负荷的实时调度方案;最后,以电动汽车聚合优化虚拟电厂负荷为例,验证了SAC算法应用于大规模电动汽车充换电负荷实时优化调度的经济性和高效性。

For the optimal scheduling problem of the aggregation in large-scale battery charging and swapping stations, a real-time optimal scheduling strategy is proposed for battery charging and swapping loads based on soft actor-critic(SAC) deep reinforcement learning. The strategy fully considers the user, system and market factors in the load control process, and can realize the friendly interaction between large-scale electric vehicles and various types of power system subjects. Firstly, considering the development scale and scheduling performance of battery charging and swapping stations, a joint operation framework is established. Secondly,an adjustable identification model considering multi-user characteristics is proposed to judge the actual adjustable performance of electric vehicles. Furthermore, considering the multiple spatio-temporal characteristics of the optimal scheduling of battery charging and swapping stations, the optimal scheduling models of adjustable battery charging and swapping loads in different scenarios are constructed. Then, the real-time scheduling scheme of battery charging and swapping loads is solved based on SAC algorithm. Finally, the case of virtual power plant load for electric vehicle load aggregation optimization verifies the economy and high efficiency of the SAC algorithm applied in real-time optimal scheduling of electric vehicle loads for large-scale battery charging and swapping.