Real-time Locally Optimal Schedule for Electric Vehicle Load via Diversity-maximization NSGA-II  

作　　者：Hongqian Wei Jun Liang Chuanyue Li Youtong Zhang 

机构地区：[1]Low Emission Vehicle(Beijing Key Lab)Research Laboratory,School of Mechanical Engineering,Beijing Institute of Technology,Beijing 100081,China [2]School of Engineering,Cardiff University,Cardiff,CF243AA,U.K

出　　处：《Journal of Modern Power Systems and Clean Energy》2021年第4期940-950,共11页现代电力系统与清洁能源学报（英文）

摘　　要：As distributed energy storage equipments, electric vehicles (EVs) have great potential for applications in power systems. Meanwhile, reasonable optimization of the charging time of EVs can reduce the users’ expense. Thus, the schedule of the EV load requires multi-objective optimization. A diversity-maximization non-dominated sorting genetic algorithm (DM-NSGA)-II is developed to perform multi-objective optimization by considering the power load profile, the users’charging cost, and battery degradation. Furthermore, a real-time locally optimal schedule is adopted by utilizing a flexible time scale. The case study illustrates that the proposed DM-NSGA-II can prevent being trapped in a relatively limited region so as to diversify the optimal results and provide trade-off solutions to decision makers. The simulation analysis shows that the variable time scale can continuously involve the present EVs in the real-time optimization rather than rely on the forecasting data. The schedule of the EV load is more practical without the loss of accuracy.

关 键 词：Electric vehicle(EV) locally optimal schedule multi-objective optimization diversity maximization genetic algorithm 

分 类 号：U492.22[交通运输工程—交通运输规划与管理] TM73[交通运输工程—道路与铁道工程] TP18[电气工程—电力系统及自动化]