| 引用本文: | 孙雨乐,叶承晋,漆淘懿,等.交通-电力耦合背景下的城市电动汽车承载力评估[J].电力系统保护与控制,2026,54(07):1-12. |
| SUN Yule,YE Chengjin,QI Taoyi,et al.Urban electric vehicle hosting capacity evaluation under traffic-grid coupling[J].Power System Protection and Control,2026,54(07):1-12 |
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| 摘要: |
| 为应对电动汽车的快速发展对城市电力网和交通网的承载力带来的双重挑战，提出一种交通-电力耦合背景下的电动汽车承载力量化评估模型。首先，考虑城市功能区划分、充电选择偏好等因素，建立考虑空间车辆状态的广域动态交通流模型，构建车辆完整的出行链，计算各时段道路通行量和充电场站的充电负荷大小。其次，考虑将平均延误时间、道路通行速度、车流量比作为城市交通系统运行效率指标，以电压、负载率等作为电网安全性指标，建立交通-电力耦合背景下考虑机会约束的城市电动汽车最大承载力评估模型。通过二阶锥松弛建立混合整数规划模型计算区域可承载车辆数上限。最后，以某市主城区的部分交通系统为对象进行分析，说明了在电动汽车承载力评估中考虑交通效率指标的必要性，并分析了道路扩容、充电引导等优化措施对承载力的提升，为城市交通发展、配电网规划提供了重要的理论支撑。 |
| 关键词: 电动汽车 交通评价 承载力评估 优化算法 充电选择 |
| DOI：10.19783/j.cnki.pspc.251023 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(52477131)；浙江省“尖兵”研发攻关计划项目资助(2024C01018) |
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| Urban electric vehicle hosting capacity evaluation under traffic-grid coupling |
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SUN Yule1,2, YE Chengjin2, QI Taoyi3,
XIA Lin4, HUI Hongxun3, SHEN Baiqiang5
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1. Polytechnic Institute, Zhejiang University, Hangzhou 310027, China; 2. College of Electrical Engineering, Zhejiang University,
Hangzhou 310027, China; 3. State Key Laboratory of Internet of Things for Smart City (University of Macau),
Macao 999078, China; 4. State Grid Hangzhou Power Supply Company, Hangzhou 310000, China;
5. State Grid Zhejiang Marketing Service Center, Hangzhou 311121, China
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| Abstract: |
| To address the dual challenges brought by the rapid growth of electric vehicles (EVs) to the hosting capacity of urban power grids and transportation networks, a quantitative evaluation model for urban EV hosting capacity under traffic-power coupling is proposed. First, considering factors such as urban functional zoning and charging preference behavior, a wide-area dynamic traffic flow model incorporating spatial vehicle states is established. A complete trip chain for vehicles is constructed to calculate road traffic volumes and charging loads at stations during different time periods. Second, average delay time, road travel speed, and traffic flow ratio are adopted as operational efficiency indexes for the urban transportation system, while voltage magnitude and line loading rate are used as power grid security indicators. Based on these metrics, a chance-constrained evaluation model for the maximum urban EV hosting capacity under traffic-power coupling is established. A mixed-integer programming model is formulated via second-order cone relaxation to determine the upper limit of regional EV hosting capacity. Finally, a case study of part of the main urban area of a city is conducted. The results demonstrate the necessity of incorporating traffic efficiency indicators in the assessment of EV hosting capacity and analyze the impacts of optimization measures, such as road expansion and charging guidance, on improving hosting capacity. The findings provides important theoretical support for urban transportation development and distribution network planning. |
| Key words: electric vehicles traffic evaluation hosting capacity assessment optimization algorithm charging option |
