电力系统保护与控制2026，Vol.54Issue(7)：1-12,12.DOI:10.19783/j.cnki.pspc.251023
交通-电力耦合背景下的城市电动汽车承载力评估
Urban electric vehicle hosting capacity evaluation under traffic-grid coupling
摘要
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.关键词
电动汽车/交通评价/承载力评估/优化算法/充电选择Key words
electric vehicles/traffic evaluation/hosting capacity assessment/optimization algorithm/charging option引用本文复制引用
孙雨乐,叶承晋,漆淘懿,夏霖,惠红勋,沈百强..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(7):1-12,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52477131). 国家自然科学基金项目资助(52477131) （No.52477131）
浙江省"尖兵"研发攻关计划项目资助(2024C01018) （2024C01018）
