Optimization Techniques in Electric Vehicle Charging Scheduling, Routing and Spatio-Temporal Demand Coordination: A Systematic Review

被引:0
|
作者
Elghanam, Eiman [1 ]
Abdelfatah, Akmal [2 ]
Hassan, Mohamed S. [3 ]
Osman, Ahmed H. [3 ]
机构
[1] Amer Univ Sharjah, Dept Ind Engn, Sharjah 26666, U Arab Emirates
[2] Amer Univ Sharjah, Dept Civil Engn, Sharjah 26666, U Arab Emirates
[3] Amer Univ Sharjah, Dept Elect Engn, Sharjah 26666, U Arab Emirates
关键词
Electric vehicle charging; Reviews; Optimization; Routing; Systematics; Costs; Quality of service; Electric vehicles; charging coordination; deterministic optimization; exact methods; heuristics; metaheuristics; routing; scheduling; spatial coordination; spatio-temporal coordination; SMART; STRATEGY; TRANSPORTATION; MANAGEMENT; ASSIGNMENT; ALGORITHM; OPERATION; IMPACTS; NETWORK;
D O I
10.1109/OJVT.2024.3420244
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:1294 / 1313
页数:20
相关论文
共 50 条
  • [1] Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load
    Paevere, Phillip
    Higgins, Andrew
    Ren, Zhengen
    Horn, Mark
    Grozev, George
    McNamara, Cheryl
    SUSTAINABILITY SCIENCE, 2014, 9 (01) : 61 - 76
  • [2] Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network
    Wang, Shengyou
    Chen, Anthony
    Wang, Pinxi
    Zhuge, Chengxiang
    TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2023, 153
  • [3] Spatio-temporal modelling of electric vehicle charging demand and impacts on peak household electrical load
    Phillip Paevere
    Andrew Higgins
    Zhengen Ren
    Mark Horn
    George Grozev
    Cheryl McNamara
    Sustainability Science, 2014, 9 : 61 - 76
  • [4] Spatio-temporal electric vehicle charging optimization considering distribution grid constraints and flexible electricity prices
    Peper, Jan David
    Schmeing, Julia
    Haeger, Ulf
    2023 19TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET, EEM, 2023,
  • [5] An adaptive spatio-temporal graph recurrent network for short-term electric vehicle charging demand prediction
    Wang, Shengyou
    Li, Yuan
    Shao, Chunfu
    Wang, Pinxi
    Wang, Aixi
    Zhuge, Chengxiang
    APPLIED ENERGY, 2025, 383
  • [6] Spatio-Temporal Electric Bus Charging Optimization With Transit Network Constraints
    Bagherinezhad, Avishan
    Palomino, Alejandro D.
    Li, Bosong
    Parvania, Masood
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2020, 56 (05) : 5741 - 5749
  • [7] Optimization Strategies for Electric Vehicle Charging and Routing: A Comprehensive Review
    Karthikeyan, S. Prabhakar
    Thomas, Polly
    GAZI UNIVERSITY JOURNAL OF SCIENCE, 2024, 37 (03): : 1256 - 1285
  • [8] Reinforcement learning for electric vehicle charging scheduling: A systematic review
    Zhao, Zhonghao
    Lee, Carman K. M.
    Yan, Xiaoyuan
    Wang, Haonan
    TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW, 2024, 190
  • [9] Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging
    Zixuan Jia
    Jianing Li
    Xiao-Ping Zhang
    Ray Zhang
    JournalofModernPowerSystemsandCleanEnergy, 2023, 11 (02) : 389 - 400
  • [10] Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle Charging
    Jia, Zixuan
    Li, Jianing
    Zhang, Xiao-Ping
    Zhang, Ray
    JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2023, 11 (02) : 389 - 400