摘要
电动汽车有序充电是智能用电领域的重要议题。传统的模型驱动方法需对充电行为建模,但受相关参数的强随机性等影响,相关模型不能完全反映充电行为的不确定性。考虑到数据驱动下的无模型强化学习(MFRL)具有不依赖先验建模、适应强非线性关系样本数据的优势,提出将其应用于充电站的有序充电负荷优化。针对性地构建以用户充电需求能否获得满足为状态的马尔可夫决策过程(MDP),并利用充电完成度指标和满意度惩罚项改进代价函数。具体采用增量式的时序差分学习(TDL)算法训练历史数据,以保证数据规模下的计算性能。算例以充电站实测数据为环境,结果表明,在无需对充电行为进行先验建模的情况下,所提方法能够准确、快速地制定充电站有序充电计划。
Coordinated charging of electric vehicles(EVs)is becoming an important topic for the smart demand management.Traditional model-driven methods are highly dependent on the accuracy of models for charging behavioral characteristics.However,affected by the strong stochastics of related parameters,etc.,the selection of relevant models cannot fully reflect their uncertainties.Considering that the data-driven model-free reinforcement learning algorithms has the advantages of not relying on pre-modeling,and adapting to data samples with strong nonlinear relationships,it is proposed to be applied to optimize the charging loads of the EV charging stations.In the Markov decision process customized for the satisfaction of EV charging need,both a charging completion degree index and a penalty term for user′s charging satisfaction are introduced to improve the policy evaluating function.Specifically,in order to guarantee the computational speed underneath the volume of charging data,the temporal difference learning algorithm is used for the training with incremental updates.The simulation is carried out with the real-world data from one charging station.Results show that the proposed algorithm can accurately and quickly calculate the coordinated charging schedules without the pre-modeling for the EV charging behavior parameters.
作者
江明
许庆强
季振亚
JIANG Ming;XU Qingqiang;JI Zhenya(State Grid Jiangsu Electric Power Co.,Ltd.,Nanjing 210024,China;School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210046,China)
出处
《电力工程技术》
北大核心
2021年第1期181-187,共7页
Electric Power Engineering Technology
基金
江苏省自然科学青年基金资助项目(BK20190710)。
