基于V2G的电动汽车充放电优化管理研究  

Research on Optimal Management of Electric Vehicle Charging and Discharging Based on V2G

作　　者：张杰 吴成明[1] 李桐 杨逸 尚锦萍 ZHANG Jie;WU Chengming;LI Tong;YANG Yi;SHANG Jinping(Electric And New Energy Faculty of China Three Gorges University,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院

出　　处：《电工材料》2019年第6期58-61,共4页Electrical Engineering Materials

基　　金：湖北省梯级水电站运行与控制重点实验室（三峡大学）开放基金资助项目(2013KJX10)

摘　　要：针对电动汽车充放电优化管理问题,围绕电动汽车无序充电的影响、新能源充电站能量优化管理、配电网中电动汽车充放电行为优化等关键技术,在考虑电网潮流分布、分时用户充放电电价、电动汽车与配电网发生电能交换的情况下建立电网及用户经济效益最优模型。通过原-对偶内点法和非劣性排序遗传算法比较优化求解,可知原-对偶内点法具有良好的收敛性和优化效果且准确性较高,可有效用于电动汽车优化管理研究,为电动汽车的发展提供理论依据和技术支撑。This paper focuses on the optimization and management of electric vehicle charging and discharging, involving electric vehicles the impact of disordered charging, the energy optimization management of new energy charging stations, and the optimization of charging and discharging behavior in distribution networks. On the basis of the distribution model, the time-sharing user charge and discharge price, and the electric energy exchange between the electric vehicle and the distribution network, establish an optimal model for the grid and user economic benefits. By comparing the original-dual interior point method and the non-inferior sorting genetic algorithm, it can be seen that the original-dual interior point method has good convergence and optimization effect and high accuracy, which can be effectively used in electric vehicle optimization management research. The theoretical basis and technical support will be provided to the development of electric vehicle.

关 键 词：电动汽车 V2G 蒙特卡特算法 原-对偶内点法 多目标优化 

分 类 号：TM910.6[电气工程—电力电子与电力传动] TM73