上海交通大学学报

上海交通大学学报

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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（网络首发）

  

  1. 昆明理工大学电力工程学院
  • 基金资助:
    国家自然科学基金项目(61963020； 62263014)； 云南省应用基础研究计划项目-面上项目(202201AT070857)

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

  1. (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

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摘要： 随着国内电动汽车(electric vehicle， EV)保有量的不断提升，为了满足EV日益增长的充电需求，电动汽车充电站(electric vehicle charging station， EVCS)开始大量接入配电网，使得配电网的稳定性、安全性和经济性面临了前所未有的挑战。为了缓解EVCS对于配电网冲击的同时保证投资者和EV用户的利益，本文提出了一种考虑EV用户行为特性的EVCS联合电池储能系统(battery energy storage system， 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，通过对EVCS及BESS进行规划实现经济性与稳定性的最佳权衡。并采用NSGA-Ⅲ算法分别在扩展的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了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, NSGA-III算法

Abstract: With the continuous increase in the number of electric vehicles (EVs) in China, EV charging stations (EVCS) have begun to be extensively connected to the distribution network to meet the growing charging demand, which posed unprecedented challenges to the stability, safety, and economy of the distribution network. To alleviate the impact of EVCS on the distribution network 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) that considers the behavioral characteristics of EV users. The model aims to minimize the comprehensive cost of EVCS and BESS, user waiting time, and system voltage fluctuations for achieving the best balance between the economy and stability by planning for EVCS and BESS. Meanwhile, the NSGA-III algorithm is used for verification on the extended IEEE-33 node testing system and the university town in Chenggong district, Kunming city. The simulation results show that in the IEEE-33 bus test system, compared with the case without Bess, the voltage fluctuation and system network loss are reduced by 36.73% and 35.41%, respectively, which effectively improves the stability and economy of the distribution network.

Key words: electric vehicles, energy storage systems, charging demand forecasting, site selection and capacity determination, NSGA-III

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