基于深度强化学习的电动汽车有序充电优化方法  

Deep Reinforcement Learning Based Optimization Method for Ordered Charging of Electric Vehicle

作　　者：喻磊 谈竹奎 王扬 刘通 肖宁 肖小兵 欧俊杰 林心昊 陈千懿 YU Lei;TAN Zhukui;WANG Yang;LIU Tong;XIAO Ning;XIAO Xiaobing;OU Junjie;LIN Xinhao;CHEN Qianyi(Electric Power Research Institute,CSG,Guangzhou 510663,China;Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,China)

机构地区：[1]南方电网科学研究院,广州510663 [2]贵州电网有限责任公司电力科学研究院,贵阳550002

出　　处：《南方电网技术》2024年第12期148-155,共8页Southern Power System Technology

基　　金：国家重点研发计划资助项目(2022YFE0205300);贵州电网有限责任公司科技项目(GZKJXM20210484)。

摘　　要：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。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.

关 键 词：电动汽车 深度强化学习 有序充电 优先级评定 

分 类 号：TM73[电气工程—电力系统及自动化] U491.8[交通运输工程—交通运输规划与管理]