电动汽车集中型充电站选址定容模型

doi:10.16097/j.cnki.1009-6744.2024.03.020

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (3): 204-212.DOI: 10.16097/j.cnki.1009-6744.2024.03.020

电动汽车集中型充电站选址定容模型

杨亚璪^{*a, b}，宾涛^{a, b}

重庆交通大学，a. 交通运输学院；b. 智能综合立体交通重庆市重点实验室，重庆 400074

收稿日期:2024-01-18 修回日期:2024-03-04 接受日期:2024-03-12 出版日期:2024-06-25 发布日期:2024-06-24
作者简介:杨亚璪(1981- )，男，山西大同人，副教授，博士
基金资助:
教育部人文社科基金(17YJCZH220)；国家自然科学基金(61803057)

Electric Vehicle Centralized Charging Station Siting and Capacity Modeling

YANG Yazao^{*a, b}, BIN Tao^{a, b}

a. School of Traffic & Transportation; b. Chongqing Key Laboratory of Intelligent Integrated and Multidimensional Transportation System, Chongqing Jiaotong University, Chongqing 400074, China

Received:2024-01-18 Revised:2024-03-04 Accepted:2024-03-12 Online:2024-06-25 Published:2024-06-24
Supported by:
Humanities and Social Science Fund Project of Ministry of Education (17YJCZH220)；National Natural Science Foundation of China (61803057)

摘要/Abstract

摘要： 针对充电和换电等单一模式的盲目建设，规划不集中带来的资源利用率低与部分充电需求得不到满足等问题，本文提出一种考虑充电和换电与移动充电的多种充电模式下集中型充电站选址与定容方法。首先，分析不同类型电动汽车的充电行为特性，模拟得到规划区域的充电需求分布；然后，基于最近距离原则采用排队论方法计算电动汽车充电损耗成本，优化移动充电设备自充电时刻，求得电动汽车自充电成本；最后，以集中型充电站建设维护成本、用户损失成本及设备自充电成本总和最小为目标建立选址定容模型，结合某城市部分实际道路网为研究区域，采用遗传算法求解模型，确定规划区域内集中型充电站建设数量、位置及不同设备的配置数量。结果表明：规划区域内建设8座集中型充电站总成本达到最低，充电需求车辆数量与充电功率变化对集中型充电站成本均有较大影响，且优化移动充电设备自充电调度管理，可降低集中型充电站高峰时期32.62%的电网负荷，提高了电网稳定性。

关键词: 交通工程, 充电站选址定容, 遗传算法, 集中型充电站, 电动汽车, 移动充电

Abstract: The existing electric vehicle charging station have some problems such as blind construction of single mode for charging and exchanging, low resource utilization rate, and part of the charging demand cannot be met due to the lack of centralized planning. This paper proposes a centralized charging station siting and capacity selection method which considers multiple charging modes such as charging, exchanging and mobile charging. First, the charging behavior characteristics of different types of electric vehicles are analyzed, and the charging demand distribution in the planning area is simulated. Based on the principle of nearest distance, the queuing theory method is used to calculate the charging loss cost of electric vehicles, optimize the self-charging moments of mobile charging equipment, and obtain the self-charging cost of electric vehicles. Then, a site selection and capacity model is developed with the objective function of minimizing the total sum of maintenance cost, user loss cost and equipment self-charging cost. A genetic algorithm is used to solve the model to determine the number of centralized charging stations in the planning area, their locations and the number of different equipment configurations, combining with part of the actual road network of a city as the study area. The results show that: the total cost of constructing eight centralized charging stations in the planning area are the lowest. The number of vehicles with charging demand and the change of charging power have a large impact on the cost of centralized charging stations. The optimization of self-charging scheduling management of mobile charging equipment can reduce the grid load of centralized charging stations in the peak period by 32.62%, which improves the stability of the power grid.

Key words: traffic engineering, charging station siting and capacity, genetic algorithm, centralized changing station; electric vehicle, mobile charging

中图分类号:

U491

杨亚璪, 宾涛. 电动汽车集中型充电站选址定容模型[J]. 交通运输系统工程与信息, 2024, 24(3): 204-212.

YANG Yazao, BIN Tao. Electric Vehicle Centralized Charging Station Siting and Capacity Modeling[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 204-212.

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链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2024.03.020

http://www.tseit.org.cn/CN/Y2024/V24/I3/204

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电动汽车集中型充电站选址定容模型

Electric Vehicle Centralized Charging Station Siting and Capacity Modeling

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