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A spatial-temporal scheduling framework for EV charging with multi-mode coordination and user subjective uncertainty

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  • Zhang, Zihan
  • Liu, Xiao-Kang
  • Xiao, Jiang-Wen
  • He, Yan
  • Wang, Yan-Wu

Abstract

With the growing popularity of electric vehicles (EVs) and diversified user demands, charging stations (CS) require multiple charging modes to accommodate diverse needs. This paper proposes a comprehensive spatial-temporal scheduling model for EVs incorporating three charging modes: plug-in charging, battery-swapping, and mobile charging vehicle (MPV) services. The original multi-objective optimization model is solved via a weighted-sum approach. To address non-convexity in the EV charging sub-model, a coordinate descent method (CDM) is developed to transform it into tractable convex sub-problems. Effective scheduling of battery and MPV operations not only enhances the station’s load capacity but also achieves peak shaving and valley filling. User demand diversity is modeled through cost-driven subjective uncertainty, classifying users into three urgency-related categories. Simulation results validate the model’s effectiveness, while comparative studies analyze the impact of user types on scheduling outcomes under varying urgency scenarios.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:401:y:2025:i:pc:s030626192501548x
    DOI: 10.1016/j.apenergy.2025.126818
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    References listed on IDEAS

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