摘要
针对电动汽车充电负荷增加、电网安全性降低,以及停车困难小区只能使用共享充电桩等问题,提出了一种充电桩选择与电动汽车充电优化的组合模型。该模型以共享充电桩之间充电时间标准差和全天充电负荷标准差之和最小为目标。考虑到电动汽车充电桩分配方案以及电动汽车充电方案两者的重要程度并不相同,提出了一种随着迭代次数的增加,变化概率不断改变的动态概率遗传算法。改进后的遗传算法用于求解该模型,通过求解一个算例,进一步阐述所提算法的原理和过程,验证了该算法的可行性和有效性。
Large household electric vehicles charging will decrease the security of the power grid,and they can only charge by using a shared charging hub in the communities.Therefore,aiming at the above problems,this paper proposed a combined model of shared charging hub selection and electric vehicle charging scheme optimization.The model aimed to minimize the sum of the standard deviation of charging time between shared charging hubs and the standard deviation of all day charging load.Conside-ring the importance of electric vehicle charging hub allocation scheme and electric vehicle charging scheme were different,it proposed a dynamic probabilistic genetic algorithm with changing probability with the increase of iteration times to solve the charging pile formula scheme and the optimization of electric vehicle charging scheme.It used the improved genetic algorithm to solve the model.The principle and process of the proposed algorithm are further illustrated by solving an example,which verifies the feasibility and effectiveness of the algorithm.
作者
梁士栋
赵勍
何建佳
何胜学
Liang Shidong;Zhao Qing;He Jianjia;He Shengxue(Business School,University of Shanghai for Science&Technology,Shanghai 200093,China)
出处
《计算机应用研究》
CSCD
北大核心
2022年第12期3688-3693,3700,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(71801153)
上海市自然科学基金资助项目(20ZR1422300)
上海市大学生创新创业项目(SH2022084,XJ2022125)。
关键词
充电负荷
充电方案
电动汽车
共享充电
charging load
charging scheme
electric vehicle
shared charging