智能电网中电动汽车充电的自适应电价控制方法

Price Control for Electric Vehicle Charging in Smart Grid: Adaptive Dynamic Programming Approach

针对电动汽车的充电提出一种电价控制策略。聚合管理者集中管理电动汽车的电池,并且考虑用电高峰时电网的电能供给有限,通过电价控制调整充电的需求量。采用自适应动态规划,通过在线网络训练,得到最优的电价策略。仿真结果表明,该自适应电价控制方法能够通过学习电动汽车的移动性和充电过程,从而调整实际充电需求量至期望水平,保证智能电网的稳定运行。

With the rapid growing of electric vehicles（EVs）, it is necessary to implement the charging control for huge number of EVs to ensure the reliability of smart grid. In this paper, a strategy of price control is proposed for EV charging. The aggregator manages EV batteries centrally and controls the EV charging demand through price. EV users change their charging demand based on the price information. However, the EV mobility is unknown in advance, which causes the inaccurate prediction of EV state and impacts the performance of price control. Thus, adaptive dynamic programming（ADP） is leveraged to achieve the optimal price policy by using online network training. Simulation results show that the proposed method is able to tune the EV charging demand close to the expected level by learning from the EV charging process and the EV mobility, which ensures the smart grid runs steadily.