Scenario-based Optimal Real-time Charging Strategy of Electric Vehicles with Bayesian Long Short-term Memory Networks  

作　　者：Hongtao Ren Chung-Li Tseng Fushuan Wen Chongyu Wang Guoyan Chen Xiao Li 

机构地区：[1]the College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China [2]UNSW Business School,The University of New South Wales,NSW 2052,Sydney,Australia [3]Hainan Institute,Zhejiang University,Sanya 572000,China [4]the Department of Electrical Engineering,College of Infor‐mation Science and Engineering,Huaqiao University,Xiamen,China [5]Guangzhou Power Supply Bureau of Guangdong Power Grid Co.,Ltd.,Guangzhou,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2024年第5期1572-1583,共12页现代电力系统与清洁能源学报（英文）

基　　金：supported in part by the National Natural Science Foundation of China(No.U1910216);in part by the Science and Technology Project of China Southern Power Grid Company Limited(No.080037KK52190039/GZHKJXM20190100)。

摘　　要：Joint operation optimization for electric vehicles(EVs)and on-site or adjacent photovoltaic generation(PVG)are pivotal to maintaining the security and economics of the operation of the power system concerned.Conventional offline optimization algorithms lack real-time applicability due to uncertainties involved in the charging service of an EV charging station(EVCS).Firstly,an optimization model for real-time EV charging strategy is proposed to address these challenges,which accounts for environmental uncertainties of an EVCS,encompassing EV arrivals,charging demands,PVG outputs,and the electricity price.Then,a scenario-based two-stage optimization approach is formulated.The scenarios of the underlying uncertain environmental factors are generated by the Bayesian long short-term memory(B-LSTM)network.Finally,numerical results substantiate the efficacy of the proposed optimization approach,and demonstrate superior profitability compared with prevalent approaches.

关 键 词：Bayesian neural network charging strategy electric vehicle(EV) long short-term memory(LSTM) scenario analysis 

分 类 号：U491.8[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]