Eiman Elghanam;Akmal Abdelfatah;Mohamed S. Hassan;Ahmed H. Osman
{"title":"电动汽车充电调度、路由和时空需求协调中的优化技术:系统综述","authors":"Eiman Elghanam;Akmal Abdelfatah;Mohamed S. Hassan;Ahmed H. Osman","doi":"10.1109/OJVT.2024.3420244","DOIUrl":null,"url":null,"abstract":"The growing penetration of electric vehicles (EVs) and the increasing EV energy demand pose several challenges to the power grid, the power distribution networks and the transportation networks. This growing demand drives the need for effective demand management and energy coordination strategies to maximize the demand covered by the EV charging stations, ensure EV users' satisfaction and prevent grid-side overload. As a result, several optimization problems are formulated and solved in the literature to provide optimal EV charging schedules (i.e. temporal coordination) as well as optimal EV-to-charging-station assignments and routing plans (i.e. spatial coordination). This paper presents a review of the state-of-the-art literature on the utilization of different deterministic optimization techniques to develop optimal EV charging coordination strategies. In particular, these works are reviewed according to their domains of operation (i.e. time-based scheduling, spatial coordination, and spatio-temporal charging coordination), their respective objectives (user-, grid- and operator-related objectives), and the solution algorithms adopted to provide the corresponding optimal coordination plans. This helps in identifying key research gaps and provide recommendations for future research directions to develop comprehensive and computationally efficient charging coordination models.","PeriodicalId":34270,"journal":{"name":"IEEE Open Journal of Vehicular Technology","volume":"5 ","pages":"1294-1313"},"PeriodicalIF":5.3000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577180","citationCount":"0","resultStr":"{\"title\":\"Optimization Techniques in Electric Vehicle Charging Scheduling, Routing and Spatio-Temporal Demand Coordination: A Systematic Review\",\"authors\":\"Eiman Elghanam;Akmal Abdelfatah;Mohamed S. Hassan;Ahmed H. Osman\",\"doi\":\"10.1109/OJVT.2024.3420244\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The growing penetration of electric vehicles (EVs) and the increasing EV energy demand pose several challenges to the power grid, the power distribution networks and the transportation networks. This growing demand drives the need for effective demand management and energy coordination strategies to maximize the demand covered by the EV charging stations, ensure EV users' satisfaction and prevent grid-side overload. As a result, several optimization problems are formulated and solved in the literature to provide optimal EV charging schedules (i.e. temporal coordination) as well as optimal EV-to-charging-station assignments and routing plans (i.e. spatial coordination). This paper presents a review of the state-of-the-art literature on the utilization of different deterministic optimization techniques to develop optimal EV charging coordination strategies. In particular, these works are reviewed according to their domains of operation (i.e. time-based scheduling, spatial coordination, and spatio-temporal charging coordination), their respective objectives (user-, grid- and operator-related objectives), and the solution algorithms adopted to provide the corresponding optimal coordination plans. This helps in identifying key research gaps and provide recommendations for future research directions to develop comprehensive and computationally efficient charging coordination models.\",\"PeriodicalId\":34270,\"journal\":{\"name\":\"IEEE Open Journal of Vehicular Technology\",\"volume\":\"5 \",\"pages\":\"1294-1313\"},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2024-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577180\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Open Journal of Vehicular Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10577180/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of Vehicular Technology","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10577180/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Optimization Techniques in Electric Vehicle Charging Scheduling, Routing and Spatio-Temporal Demand Coordination: A Systematic Review
The growing penetration of electric vehicles (EVs) and the increasing EV energy demand pose several challenges to the power grid, the power distribution networks and the transportation networks. This growing demand drives the need for effective demand management and energy coordination strategies to maximize the demand covered by the EV charging stations, ensure EV users' satisfaction and prevent grid-side overload. As a result, several optimization problems are formulated and solved in the literature to provide optimal EV charging schedules (i.e. temporal coordination) as well as optimal EV-to-charging-station assignments and routing plans (i.e. spatial coordination). This paper presents a review of the state-of-the-art literature on the utilization of different deterministic optimization techniques to develop optimal EV charging coordination strategies. In particular, these works are reviewed according to their domains of operation (i.e. time-based scheduling, spatial coordination, and spatio-temporal charging coordination), their respective objectives (user-, grid- and operator-related objectives), and the solution algorithms adopted to provide the corresponding optimal coordination plans. This helps in identifying key research gaps and provide recommendations for future research directions to develop comprehensive and computationally efficient charging coordination models.