The invention relates to the technical field of power systems, in particular to an electric vehicle charging station collaborative peak regulation method based on hierarchical reinforcement learning, and the method comprises the following steps: S1, defining the state of a charging pile, the state of a charging waiting parking space, and the joint state of the waiting parking space, and a system operation mode is formulated according to the service electricity price formulating unit and the charging power control unit. And S2, determining a mapping relationship between the charging service electricity price and the user arrival rate, adjusting the charging power of the charging pile, constructing a peak clipping award function in unit time, and establishing a charging station double-center collaborative peak shaving system. And S3, designing a double-layer collaborative optimization model by taking the SPM as an upper layer and the CPC as a lower layer according to the operation mode of the station. And S4, establishing an optimization objective function of the upper-layer intelligent agent, and solving by adopting a Dueling DQN algorithm. And establishing an optimization objective function of a lower-layer agent, and solving by adopting a TD3 algorithm.
本发明涉及电力系统技术领域,尤其涉及一种基于分层强化学习的电动汽车充电场站协同调峰方法,该方法包括以下步骤:S1、定义充电桩的状态、充电等待车位的状态、等待车位的联合状态,根据服务电价制定单元及充电功率控制单元制定系统运行模式。S2、确定充电服务电价与用户到达率映射关系,调整充电桩充电功率,构造单位时间内削峰奖励函数,建立充电场站双中心协同调峰系统。S3、根据场站的运行模式,以SPM为上层,CPC为下层,设计双层协同优化模型。S4、建立上层智能体的优化目标函数,采用Dueling DQN算法进行求解。建立下层智能体的优化目标函数,采用TD3算法进行求解。
Electric vehicle charging station collaborative peak regulation method based on hierarchical reinforcement learning
基于分层强化学习的电动汽车充电场站协同调峰方法
2023-04-28
Patent
Electronic Resource
Chinese
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