针对大规模电动汽车无序接入电网引发的用户充电开销大和电网负荷波动加剧等问题,提出了基于深度强化学习(deep reinforcement learning,DRL)的电动汽车充电行为优化方法.首先,以最小化电网负荷波动和用户充电费用为目标,建立了电动汽车有序充电优化调度模型.其次,将电动汽车的充电行为构建为马尔科夫决策过程(Markov deci-sion process,MDP),根据电网负荷预测信息和分时电价对充电时段进行优先级评定,并根据优先级控制电动汽车充电行为.通过双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)算法对电动汽车有序充电策略进行快速优化.最后,通过算例验证了所提方法在减少用户的充电开销和配电网的负荷波动方面的有效性.
Deep Reinforcement Learning Based Optimization Method for Ordered Charging of Electric Vehicle
The large-scale disordered integration of electric vehicles(EVs)into power grid poses many problems,such as enlarged power fluctuation for grid and increased charging cost for users.To solve these problems,this paper proposes an optimized EV charg-ing method based on deep reinforcement learning(DRL).Firstly,an EV ordered charging scheduling model is established aiming at minimizing the power fluctuation and user charging cost.Secondly,the EV charging behaviour is formulated as a Markov decision process(MDP)which evaluates the priority for each charging period based on load prediction information and time-of-use electricity tariff.The charging behaviour of EVs is controlled by the priority.The twin delayed deep deterministic policy gradient(TD3)algorithm is adopted to solve the MDP problem,which quickly optimizes EV ordered charging strategy.Finally,the effectivenesses of the proposed method in reducing charging cost and load fluctuation of distribution network are verified by case studies based on numerical examples.
electric vehiclesdeep reinforcement learningordered chargingpriority evaluation
万方数据
维普
