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Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation

Author

Listed:
  • Wu, Hongbin
  • Lan, Xinjie
  • He, Ye
  • Wu, Andrew Y.
  • Ding, Ming

Abstract

As electric vehicles (EVs) are cross-domain entities with dual attributes related to mobile loads and transportation, their travel modes and charging behaviors are stochastic and uncertain in time and space. Large-scale uncoordinated EV charging may cause problems such as the congestion of charging stations (CSs) and overloading of distribution networks (DNs). To address these challenges and the current research gap, and to optimize the spatial-temporal distribution of EV charging loads, a vehicle-road-network collaborative operation framework is constructed in this paper, and a two-stage spatial-temporal scheduling method for EV charging is proposed. In the first stage, real-time traffic and CSs information are introduced to improve Dijkstra's algorithm, and the value preferences of different types of users are considered to further improve the objective function of the algorithm as well. Thus, a dynamic personalized charging navigation model based on improved Dijkstra's algorithm is proposed to provide real-time guidance allowing users to choose their travel paths and CSs. In the second stage, we consider the charging demands of EV users and the operation status of the DN, and an orderly charging model is established to minimize the peak/valley load difference of the DN. Through comprehensive comparisons of experimental results, the variance in the average utilization rates of CSs and the peak valley difference for the DN are reduced by 80.77 % and 16.91 %, respectively. In addition, the time and economic costs of users can be reduced by 9 min and 7.9 RMB, respectively, thereby reducing travel and toll costs and increasing their willingness to participate in scheduling. The simulation experiment proves that the proposed method achieves multi-agent collaborative optimization of EV users, CSs, and DN.

Suggested Citation

  • Wu, Hongbin & Lan, Xinjie & He, Ye & Wu, Andrew Y. & Ding, Ming, 2025. "Orderly charging of electric vehicles: A two-stage spatial-temporal scheduling method based on user-personalized navigation," Applied Energy, Elsevier, vol. 378(PA).
    • Handle: RePEc:eee:appene:v:378:y:2025:i:pa:s0306261924021834
    DOI: 10.1016/j.apenergy.2024.124800
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    as
    1. Zhong, Zewei & Hu, Wuyang & Zhao, Xiaoli, 2024. "Rethinking electric vehicle smart charging and greenhouse gas emissions: Renewable energy growth, fuel switching, and efficiency improvement," Applied Energy, Elsevier, vol. 361(C).
    2. Wang, Yi & Qiu, Dawei & He, Yinglong & Zhou, Quan & Strbac, Goran, 2023. "Multi-agent reinforcement learning for electric vehicle decarbonized routing and scheduling," Energy, Elsevier, vol. 284(C).
    3. Yin, WanJun & Qin, Xuan, 2022. "Cooperative optimization strategy for large-scale electric vehicle charging and discharging," Energy, Elsevier, vol. 258(C).
    4. Wang, Ziqi & Hou, Sizu, 2023. "A real-time strategy for vehicle-to-station recommendation in battery swapping mode," Energy, Elsevier, vol. 272(C).
    5. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).
    6. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    7. Yin, WanJun & Wen, Tao & Zhang, Chao, 2023. "Cooperative optimal scheduling strategy of electric vehicles based on dynamic electricity price mechanism," Energy, Elsevier, vol. 263(PA).
    8. Zhang, Lei & Huang, Zhijia & Wang, Zhenpo & Li, Xiaohui & Sun, Fengchun, 2024. "An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing," Energy, Elsevier, vol. 299(C).
    9. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    10. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    11. Yang, Chengying & Wu, Zhixin & Li, Xuetao & Fars, Ashk, 2024. "Risk-constrained stochastic scheduling for energy hub: Integrating renewables, demand response, and electric vehicles," Energy, Elsevier, vol. 288(C).
    12. Lotfi, Mohamed & Almeida, Tiago & Javadi, Mohammad S. & Osório, Gerardo J. & Monteiro, Cláudio & Catalão, João P.S., 2022. "Coordinating energy management systems in smart cities with electric vehicles," Applied Energy, Elsevier, vol. 307(C).
    13. Xiang, Yue & Jiang, Zhuozhen & Gu, Chenghong & Teng, Fei & Wei, Xiangyu & Wang, Yang, 2019. "Electric vehicle charging in smart grid: A spatial-temporal simulation method," Energy, Elsevier, vol. 189(C).
    14. Liu, Yonggang & Chen, Qianyou & Li, Jie & Zhang, Yuanjian & Chen, Zheng & Lei, Zhenzhen, 2023. "Collaborated eco-routing optimization for continuous traffic flow based on energy consumption difference of multiple vehicles," Energy, Elsevier, vol. 274(C).
    15. D’Auria, Bernardo & Adan, Ivo J.B.F. & Bekker, René & Kulkarni, Vidyadhar, 2022. "An M/M/c queue with queueing-time dependent service rates," European Journal of Operational Research, Elsevier, vol. 299(2), pages 566-579.
    16. Trinko, David & Horesh, Noah & Porter, Emily & Dunckley, Jamie & Miller, Erika & Bradley, Thomas, 2023. "Transportation and electricity systems integration via electric vehicle charging-as-a-service: A review of techno-economic and societal benefits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    17. Luo, Yugong & Zhu, Tao & Wan, Shuang & Zhang, Shuwei & Li, Keqiang, 2016. "Optimal charging scheduling for large-scale EV (electric vehicle) deployment based on the interaction of the smart-grid and intelligent-transport systems," Energy, Elsevier, vol. 97(C), pages 359-368.
    18. Qiu, Dawei & Wang, Yi & Sun, Mingyang & Strbac, Goran, 2022. "Multi-service provision for electric vehicles in power-transportation networks towards a low-carbon transition: A hierarchical and hybrid multi-agent reinforcement learning approach," Applied Energy, Elsevier, vol. 313(C).
    19. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    20. Zhou, Kaile & Cheng, Lexin & Wen, Lulu & Lu, Xinhui & Ding, Tao, 2020. "A coordinated charging scheduling method for electric vehicles considering different charging demands," Energy, Elsevier, vol. 213(C).
    21. Squalli, Jay, 2024. "Environmental hypocrisy? Electric and hybrid vehicle adoption and pro-environmental attitudes in the United States," Energy, Elsevier, vol. 293(C).
    22. Zhang, Xizheng & Wang, Zeyu & Lu, Zhangyu, 2022. "Multi-objective load dispatch for microgrid with electric vehicles using modified gravitational search and particle swarm optimization algorithm," Applied Energy, Elsevier, vol. 306(PA).
    23. Du, Wenyi & Ma, Juan & Yin, Wanjun, 2023. "Orderly charging strategy of electric vehicle based on improved PSO algorithm," Energy, Elsevier, vol. 271(C).
    24. Triviño-Cabrera, Alicia & Aguado, José A. & Torre, Sebastián de la, 2019. "Joint routing and scheduling for electric vehicles in smart grids with V2G," Energy, Elsevier, vol. 175(C), pages 113-122.
    25. Liu, Shan & Jiang, Hai & Chen, Shuiping & Ye, Jing & He, Renqing & Sun, Zhizhao, 2020. "Integrating Dijkstra’s algorithm into deep inverse reinforcement learning for food delivery route planning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    26. Yin, Wanjun & Jia, Leilei & Ji, Jianbo, 2024. "Energy optimal scheduling strategy considering V2G characteristics of electric vehicle," Energy, Elsevier, vol. 294(C).
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