张勇军,林靖淳,林衍扬
ZHANG Yongjun,LIN Jingchun,LIN Yanyang

• 1:华南理工大学电力学院智慧能源工程技术研究中心

摘要(Abstract)：

针对规模化电动汽车并网所造成的配电网负荷时空分布不均、重过载常发等问题，提出了一种基于交能耦合的电动汽车有序充馈电策略。首先，构建了ETS（电力-交通-社会）信息耦合模型，模型整合了电网拓扑、路-网结构及充馈电交易机制等多维信息。其次，提出用户激励阈值概念，以提升电动汽车对调控指令的响应效率。在此基础上，综合考虑慢充-快充等多种充电方式，采用双层粒子群优化算法优化调控指令，其中上层优化以最大化充电聚合商和电网公司收益为目标，下层优化则致力于最小化用户激励成本。仿真分析结果表明，所提策略能有效平抑负荷波动，提高新能源消纳能力，同时实现了多方利益主体的协同优化。
To address spatiotemporal load imbalances and frequent overloading in distribution networks caused by large-scale electric vehicle(EV) integration, this paper proposes an orderly charging/discharging strategy leveraging power-traffic coupling. First, an electricity-transportation-society(ETS) coupling model is developed, integrating grid topology, road-network infrastructure, and transaction mechanisms. Second, the concept of user incentive threshold is introduced to enhance EV response efficiency to control signals. Building upon this, the strategy accommodates multiple charging modes(slow/fast charging) and employs bi-level particle swarm optimization(PSO) for control signal optimization: the upper layer maximizes revenues for both charging aggregators and grid operators, while the lower layer minimizes user incentive costs. Simulations results demonstrate that the strategy effectively mitigates load fluctuations, enhances renewable energy accommodation, and coordinates stakeholder interests.

关键词(KeyWords)： 电动汽车;有序充馈电;交能融合
EV;orderly charging/discharging;power-traffic coupling

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（52177085）

作者(Author): 张勇军,林靖淳,林衍扬
ZHANG Yongjun,LIN Jingchun,LIN Yanyang

DOI: 10.19585/j.zjdl.202510010

参考文献(References)：

• [1]关俊乐，高源，陈方华，等.基于面需求法的充电设施分区选址优化[J].广东电力，2024,37(10):38-45.GUAN Junle,GAO Yuan,CHEN Fanghua,et al.The location optimization of charging facilities based on the plane demand method[J].Guangdong Electric Power,2024,37(10):38-45.
• [2]刘栋晨，季昱，胡岳.交能融合V2G技术研究与实践综述[J].上海交通大学学报，2025,59(1):1-15.LIU Dongchen,JI Yu,HU Yue.Summary of research and practice on V2G technology of transportation and energy fusion[J].Journal of Shanghai Jiao Tong University,2025,59(1):1-15.
• [3]陈明强，高健飞，畅国刚，等.V2G模式下微网电动汽车有序充电策略研究[J].电力系统保护与控制，2020,48(8):141-148.CHEN Mingqiang,GAO Jianfei,CHANG Guogang,et al.Research on orderly charging strategy of micro-grid electric vehicles in V2G model[J].Power System Protection and Control,2020,48(8):141-148.
• [4] YI Y Q,LIU S L,ZHANG Y J,et al.Phase identification of low-voltage distribution network based on stepwise regression method[J]. Journal of Modern Power Systems and Clean Energy,2023,11(4):1224-1234.
• [5]钟永洁，汤成俊，王紫东，等.我国虚拟电厂的发展演进和关键技术及难点分析[J].浙江电力，2025,44(2):13-31.ZHONG Yongjie,TANG Chengjun,WANG Zidong,et al. Analysis of the evolution,key technologies,and challenges of virtual power plants in China[J].Zhejiang Electric Power,2025,44(2):13-31.
• [6] HUANG W,LUO K Y,LI Y K,et al. Multi-objective planning of community microgrid based on electric vehicles’ orderly charging/discharging strategy[J]. Journal of Energy Storage,2025,114:115623.
• [7]文欣，黄学良，高山，等.考虑区域差异性的电动私家车网格化充电需求预测[J].电力系统自动化，2025,49(7):158-168.WEN Xin,HUANG Xueliang,GAO Shan,et al. Grid charging demand forecasting for private electric vehicles considering regional differences[J].Automation of Electric Power Systems,2025,49(7):158-168.
• [8]袁泉，汤奕.基于路-电耦合网络的电动汽车需求响应技术[J].中国电机工程学报，2021,41(5):1627-1637.YUAN Quan,TANG Yi. Electric vehicle demand response technology based on traffic-grid coupling networks[J].Proceedings of the CSEE,2021,41(5):1627-1637.
• [9]周毅，袁泉，汤奕，等.基于路-电耦合网络的电动汽车充电决策优化方法[J].电网技术，2021,45(9):3563-3572.ZHOU Yi,YUAN Quan,TANG Yi,et al.Charging decision optimization for electric vehicles based on traffic-grid coupling networks[J].Power System Technology,2021,45(9):3563-3572.
• [10]袁泉，汤奕.考虑路电耦合的电动汽车集群多时间尺度低碳协同调控策略[J].电网技术，2024,48(12):4969-4979.YUAN Quan,TANG Yi.Multi-time scale decarbonization coordination strategy of electric vehicle fleets considering transportation and electricity coupling[J]. Power System Technology,2024,48(12):4969-4979.
• [11]姜晓锋，魏巍，王永灿，等.“车-路-网”协同优化下的电动汽车有序充电引导策略[J].电力科学与技术学报，2023,38(5):44-56.JIANG Xiaofeng,WEI Wei,WANG Yongcan,et al.Orderly charging guidance strategies for electric vehicles under EVs-Traffic-Distribution collaborative optimization[J]. Journal of Electric Power Science and Technology,2023,38(5):44-56.
• [12]田晟，李乐洋.考虑调度可行性的电动出租车时空双层充电优化[J].南方电网技术，2025,19(2):57-67.TIAN Sheng,LI Leyang.Spatiotemporal bi-level charging optimization of electric taxi considering scheduling feasibility[J].Southern Power System Technology,2025,19(2):57-67.
• [13]周星月，李晓皓，王智东，等.计及预测负荷和用户需求差异的电动汽车实时调度优化[J].全球能源互联网，2022,5(6):543-551.ZHOU Xingyue,LI Xiaohao,WANG Zhidong,et al.Realtime scheduling optimization of electric vehicles considering the predicted load and the difference between users′demand[J].Journal of Global Energy Interconnection,2022,5(6):543-551.
• [14]周星月，黄向敏，张勇军，等.基于需求响应潜力模糊评估的电动汽车实时调控优化模型[J].电力自动化设备，2022,42(10):30-37.ZHOU Xingyue,HUANG Xiangmin,ZHANG Yongjun,et al.Real-time scheduling and optimization model of electric vehicles based on fuzzy evaluation of demand response potential[J]. Electric Power Automation Equipment,2022,42(10):30-37.
• [15]黄廷城，张勇军.计及充电可靠性的电动汽车有序充放电控制策略优化与分析[J].电力科学与技术学报，2019,34(4):85-92.HUANG Tingcheng,ZHANG Yongjun.Optimization and analysis of orderly charging and discharging control strategy for electric vehicles considering charging reliability[J].Journal of Electric Power Science and Technology,2019,34(4):85-92.
• [16]江海燕，李晨阳，孟诗语，等.考虑配电网分区的虚拟电厂分布式协同优化运行模型[J].浙江电力，2025,44(2):53-63.JIANG Haiyan,LI Chenyang,MENG Shiyu,et al.A distributed cooperative optimal operating model for virtual power plants considering distribution network partitions[J].Zhejiang Electric Power,2025,44(2):53-63.
• [17]孙旭.考虑电动汽车需求响应的配电网信息物理系统可靠性评估[D].天津：天津大学，2019.SUN Xu.Reliability assessment of distribution cyber physical system considering electric vehicles demand response[D].Tianjin:Tianjin University,2019.
• [18]曾梦妤，甘露，张迪，等.以数字电网促进新型电力系统IT与OT融合的思考[J].电力自动化设备，2024,44(9):179-188.ZENG Mengyu,GAN Lu,ZHANG Di,et al.Thinking of promoting integration of IT and OT in new power system with digital power grid[J]. Electric Power Automation Equipment,2024,44(9):179-188.
• [19]邓艺璇，黄玉萍，黄周春.基于随机森林算法的电动汽车充放电容量预测[J].电力系统自动化，2021,45(21):181-188.DENG Yixuan,HUANG Yuping,HUANG Zhouchun.Charging and discharging capacity forecasting of electric vehicles based on random forest algorithm[J].Automation of Electric Power Systems,2021,45(21):181-188.
• [20]蒋慧灵，方伟，徐天锋，等.基于Dijkstra算法的室内疏散最优路径规划模型[J].清华大学学报（自然科学版），2025,65(4):742-749.JIANG Huiling,FANG Wei,XU Tianfeng,et al.Optimal indoor evacuation path-planning model based on Dijkstra’s algorithm[J]. Journal of Tsinghua University(Science and Technology),2025,65(4):742-749.
• [21]刘丽军，陈昌，胡鑫，等.基于“车-路-站-网”信息耦合的电动汽车有序充电策略[J].高电压技术，2024,50(2):693-703.LIU Lijun,CHEN Chang,HU Xin,et al.Ordered charging strategy for electric vehicles based on the information coupling of vehicle-road-station-grid[J]. High Voltage Engineering,2024,50(2):693-703.