上海交通大学学报

上海交通大学学报 >

2025 , Vol. 59 >Issue 11: 1720 - 1731

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2023.629

新型电力系统与综合能源

考虑行驶特性的电动汽车充电站联合电储能系统最优规划

  • 韩一鸣 ,
  • 贺彬 ,
  • 杨博 ,
  • 李嘉乐
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  • 昆明理工大学 电力工程学院, 昆明 650500
韩一鸣(1993—),讲师,研究方向为人工智能在电力系统中的应用.
杨 博,教授,博士生导师;E-mail:yangbo_ac@outlook.com.

收稿日期: 2023-12-15

  修回日期: 2024-01-12

  录用日期: 2024-03-05

  网络出版日期: 2024-03-19

基金资助

国家自然科学基金资助项目(61963020);国家自然科学基金资助项目(62263014);云南省应用基础研究计划项目-面上项目(202201AT070857)

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Optimal Planning of Electric Vehicle Charging Stations Combined with Battery Energy Storage Systems Considering Driving Characteristics

  • HAN Yiming ,
  • HE Bin ,
  • YANG Bo ,
  • LI Jiale
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  • Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China

Received date: 2023-12-15

  Revised date: 2024-01-12

  Accepted date: 2024-03-05

  Online published: 2024-03-19

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摘要

随着国内电动汽车保有量的不断提升,为了满足电动汽车日益增长的充电需求,电动汽车充电站(EVCS)开始大量接入配电网,给配电网的稳定性、安全性和经济性带来前所未有的挑战.为了缓解EVCS带来配电网冲击的同时保证投资者和电动汽车用户的利益,提出一种考虑电动汽车用户行为特性的EVCS联合电池储能系统(BESS)的多目标规划模型.该模型以最小化EVCS和BESS综合成本、用户等待时间和系统电压波动为目标,通过规划EVCS及BESS实现经济性与稳定性的最佳权衡;并采用非支配排序遗传算法(NSGA-III)分别在扩展的IEEE-33节点测试系统与昆明市呈贡区大学城上进行验证.仿真结果表明:在IEEE-33节点测试系统,与未配置BESS时相比,电压波动与系统网损分别下降36.73%和35.41%,有效提高了配网的稳定性与经济性.

关键词： 电动汽车; 储能系统; 充电需求预测; 选址定容; 非支配排序遗传算法

本文引用格式

韩一鸣 , 贺彬 , 杨博 , 李嘉乐 . 考虑行驶特性的电动汽车充电站联合电储能系统最优规划[J]. 上海交通大学学报, 2025 , 59(11) : 1720 -1731 . DOI: 10.16183/j.cnki.jsjtu.2023.629

Abstract

With the continuous increase in the number of electric vehicles (EVs) in China, EV charging stations (EVCS) are becoming extensively connected to distribution networks to meet the growing charging demand, which poses unprecedented challenges to the stability, safety, and economy of distribution networks. To reduce the impact of EVCS on distribution networks while ensuring the interests of investors and EV users, this paper proposes a multi-objective planning model of EVCS combined battery energy storage system (BESS) which considers the behavioral characteristics of EV users, aiming to minimize the comprehensive cost of EVCS and BESS, waiting time of users, and system voltage fluctuations to achieve the best balance between the economy and stability by planning for EVCS and BESS. Meanwhile, the non-dominated sorting genetic algorithm III (NSGA-III) is used to verify the model on the extended IEEE-33 node testing system and the university town in Chenggong, Kunming. The simulation results show that in the IEEE-33 node test system, compared with the case without BESS configuration, the voltage fluctuation and system network loss decreases by 36.73% and 35.41%, respectively, effectively improving the stability and economy of the distribution network.

Key words： electric vehicles (EVs); energy storage systems; charging demand forecasting; site selection and capacity determination; non-dominated sorting genetic algorithm III (NSGA-III)

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