引用本文:孙亮,申畅,朱童生,杨格林,杨茂,孙艳学.考虑交通流量俘获的电动汽车充电负荷预测和充电站规划[J].电力自动化设备,2024,44(7):263-270
SUN Liang,SHEN Chang,ZHU Tongsheng,YANG Gelin,YANG Mao,SUN Yanxue.Electric vehicle charging load prediction and charging station planning considering traffic flow capture[J].Electric Power Automation Equipment,2024,44(7):263-270
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考虑交通流量俘获的电动汽车充电负荷预测和充电站规划
孙亮1, 申畅1, 朱童生1, 杨格林1, 杨茂1, 孙艳学2
1.东北电力大学 电气工程学院,吉林 吉林 132000;2.国网东北分部 绿源水力发电公司云峰发电厂,吉林 通化 134299
摘要:
针对电动汽车(EV)的充电需求,考虑路径的交通流量,以最大交通流量俘获、最小配电系统网络损耗和最小节点电压偏移为目标,构建了一个多目标决策模型对EV充电站进行规划。运用网络扩展技术确定交通流量俘获路径;运用蒙特卡罗模拟算法,确定规划区内EV的最大充电负荷,从而推算得到充电站的容量;运用超效率数据包络分析评价方法,确定经过归一化处理后各目标函数的权重系数,从而将多目标优化问题转化为单目标优化问题,并采用改进的二进制粒子群优化算法进行求解。以一个包含25个节点的交通网络耦合33节点配电系统为算例进行仿真,验证所建模型和所提方法的有效性,并进一步分析EV最大行驶里程、充电站负荷接入不同节点以及不同时刻对各目标函数的影响。
关键词:  电动汽车  充电站  交通流量俘获  网络扩展技术  蒙特卡罗模拟算法  超效率数据包络分析
DOI:10.16081/j.epae.202312045
分类号:TM715;U469.72
基金项目:国家重点研发计划项目(2022YFB2403000)
Electric vehicle charging load prediction and charging station planning considering traffic flow capture
SUN Liang1, SHEN Chang1, ZHU Tongsheng1, YANG Gelin1, YANG Mao1, SUN Yanxue2
1.School of Electrical Engineering, Northeast Electric Power University, Jilin 132000, China;2.Yunfeng Power Plant of Lvyuan Hydropower Company, Northeast Branch of State Grid, Tonghua 134299, China
Abstract:
Aiming at the charging demand of electric vehicle(EV),considering the traffic flow of the route, a multi-objective decision-making model is constructed to plan EV charging stations, which takes the maximum traffic flow capture, the minimum network loss of the distribution system and minimum bus voltage offset as the objectives. The network extension technology is used to determine the traffic capture path. Monte Carlo simulation algorithm is used to determine the maximum charging load of EV in the planning area, so as to calculate the capacity of the charging stations. Then, the super efficiency data envelopment analysis evaluation method is used to determine the weight coefficients of each objective function after normalization proces-sing, so as to transform the multi-objective optimization problem into a single-objective optimization problem, and it is solved by the improved binary particle swarm optimization algorithm. The 33-bus distribution system coupled with 25-node traffic network is taken as an example to carry on simulation, the results verify the effectiveness of the proposed model and method, and the influences of the maximum driving range of electric vehicle, the access bus and time of charging station load on each objective function are further analyzed.
Key words:  electric vehicles  charging stations  traffic flow capture  network extension technology  Monte Carlo simulation algorithm  super efficiency data envelopment analysis

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