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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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    1. Li, Xiaofeng & Yu, Xinlian & Pu, Ziyuan & Chen, Jingxu, 2025. "Electric vehicle charging optimization with coordinated mobile and fixed chargers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 204(C).
    2. Shen, Xiaonan & Shen, Junjie & Zhang, Yuting & Wu, Haoyu & Wang, Yang, 2025. "A multi-source feature engineering-enhanced framework for mid-to-long-term EV charging load forecasting: Integrating self-adaptive optimization and BiLSTM-iTransformer predictor," Energy, Elsevier, vol. 339(C).
    3. Muhammed Cavus & Huseyin Ayan & Mahmut Sari & Osman Akbulut & Dilum Dissanayake & Margaret Bell, 2025. "Enhancing Smart Grid Reliability Through Data-Driven Optimisation and Cyber-Resilient EV Integration," Energies, MDPI, vol. 18(17), pages 1-24, August.
    4. Zhang, Zihan & Liu, Xiao-Kang & Xiao, Jiang-Wen & He, Yan & Wang, Yan-Wu, 2025. "A spatial-temporal scheduling framework for EV charging with multi-mode coordination and user subjective uncertainty," Applied Energy, Elsevier, vol. 401(PC).
    5. Guan, Qingshu & Cao, Hui & Tan, Junkai & Jia, Lixin & Yan, Dapeng & Chen, Badong, 2025. "Token-specific deep reinforcement learning for energy-efficient capacitated electric vehicle routing problems," Applied Energy, Elsevier, vol. 396(C).
    6. Yang Gao & Xiaohong Zhang & Qingyuan Yan & Yanxue Li, 2025. "Demand Response Strategies for Electric Vehicle Charging and Discharging Behavior Based on Road–Electric Grid Interaction and User Psychology," Sustainability, MDPI, vol. 17(6), pages 1-27, March.

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