基于充电需求预测的电动汽车充电站选址规划研究OA北大核心CSTPCD
Research on location planning of electric vehicle charging station based on prediction of charging demand
针对目前电动汽车充电站选址规划问题,提出了一种基于充电需求分布预测的充电站选址优化策略.该策略建立了基于Dijkstra最短路径的Voronoi图方法和双层动态排队方法的充电站选址定容模型,来满足电动汽车保有量持续增长下的充电需求;在充电站年均建设、运营成本,配电网惩罚成本以及电动汽车充电成本多目标约束下,得到以电动汽车充电站规划总成本最小化为目标函数.最后基于改进粒子群优化算法求解目标函数,对新增充电站进行多场景实例分析.MATLAB和MATPOWER仿真结果表明,在不同电动汽车保有量的场景下,通过合理规划充电站布局,可以提高电动汽车充电站选址规划的经济性,从而验证了模型的有效性,为充电站的选址规划提供理论依据.
Aiming at the current problem of location planning for electric vehicle charging station,a charging sta-tion location optimization strategy based on the prediction of charging demand distribution is proposed.This strategy establishes a charging station location and capacity model based on the Voronoi diagram method of Dijkstra shortest path and the double-layer dynamic queuing method to meet the charging demand under the continuous increase in the number of electric vehicles,on this basis,under the multi-objective constraints of the average annual construc-tion and operation costs of charging stations,penalty costs of distribution network,and charging costs of electric ve-hicles,the objective function is to minimize the total planning costs of electric vehicle charging stations.Finally,the improved particle swarm optimization algorithm is used to solve the objective function,and the newly-added charging station is analyzed in multiple scenarios.The simulation results of MATLAB and MATPOWER show that under the scenarios of different electric vehicle holdings,reasonable planning of the charging station layout can im-prove the economics of electric vehicle charging station location planning,which verifies the validity of the model,and provides a theoretical basis for the location planning of charging stations.
张智禹;王致杰;杨皖昊;张洪玮
上海电机学院 电气学院,上海 201306上海理工大学机械工程学院,上海 200093
动力与电气工程
充电站选址Voronoi图方法动态排队负荷需求预测粒子群优化算法
location selection of charging stationVoronoi diagram methoddynamic queuingload demand pre-dictionparticle swarm optimization algorithm
《电测与仪表》 2024 (010)
39-49 / 11
国家自然科学基金资助项目(61803253);上海市自然科学基金资助项目(15ZR1417300)
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