基于深度强化学习的电动汽车实时调度策略  

Real-time Dispatch Strategy for Electric Vehicles Based on Deep Reinforcement Learning

作　　者：李航[1] 李国杰[1] 汪可友[1] LI Hang;LI Guojie;WANG Keyou(Key Laboratory of Control of Power Transmission and Conversion,Ministry of Education(Shanghai Jiao Tong University),Shanghai 200240,China)

机构地区：[1]电力传输与功率变换控制教育部重点实验室(上海交通大学),上海市200240

出　　处：《电力系统自动化》2020年第22期161-167,共7页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(51877133)。

摘　　要：电动汽车(EV)作为一种分布式储能装置,对抑制功率波动有着巨大的潜力。考虑EV接入的随机性及可再生能源出力和负荷的不确定性,利用不基于模型的深度强化学习方法,建立了以最小功率波动及最小充放电费用为目标的实时调度模型。为满足用户的用电需求,采用充放电能量边界模型表征电动汽车的充放电行为。在对所提模型进行日前训练及参数保存后,针对日内每一时刻系统运行的实时状态量,生成该时刻充放电调度策略。最后以某微电网为例,验证了所提基于深度强化学习的调度方法在满足用户充电需求的前提下,可以有效减小微电网内的功率波动,降低EV充放电费用;日内不需要迭代计算,可以满足实时调度的要求。As distributed energy storage devices,electric vehicles(EVs)have huge potential to curb power fluctuations.Considering the randomness of EV integration,the uncertainty of renewable energy generation and load,a real-time dispatch model with objectives of the minimum power fluctuations and minimum charging and discharging cost is established by using a model-free deep reinforcement learning method.In order to satisfy the user’s demand,a charging and discharging energy boundary model is used to characterize the charging and discharging behaviors of EVs.After day-ahead training and parameter preservation of the proposed model,the corresponding charging and discharging dispatch strategy is generated in real time for the intraday realtime states of the system operation at each moment.Finally,taking a microgrid as an example,it is verified that the proposed deep reinforcement learning based dispatch method can effectively reduce power fluctuations in the microgrid and reduce EV charging and discharging cost on the premise of satisfying users’charging demands,while satisfying the demands of real-time dispatch without the need of iterative calculation for intraday dispatch.

关 键 词：电动汽车 不确定性 深度强化学习 实时调度 能量边界 

分 类 号：TM73[电气工程—电力系统及自动化] TP18[自动化与计算机技术—控制理论与控制工程]