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Self-controlling resource management model for electric vehicle fast charging stations with priority service

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  • Kakillioglu, Emre Anıl
  • Yıldız Aktaş, Melike
  • Fescioglu-Unver, Nilgun

Abstract

Along with the increasing number of electric vehicles (EVs) on the roads, the demand for public fast-charging stations is growing. Long charging times for EVs may lead to congestion in charging stations, queues, and increased waiting times. Different vehicle owners have different sensitivities to waiting time and price. User prioritization is an effective solution for satisfying users with different requests. In this study, we develop a self-controlling resource management model for EV fast-charging stations that provide prioritized service. The model aims to control the delay times of express and normal vehicle classes such that the ratio of their average delay times tracks a target relative delay rate in real time. Each station can determine and change its target relative delay rate according to its policy. The model manages the allocation of resources to user classes in real time through a control mechanism to track the target. The control mechanism uses a simulation model to predict the outcomes of its actions. Numerical studies demonstrate that the model successfully achieves the relative delay target in both steady state and real time under different conditions. The model is applicable to most systems with a dynamically varying workload and a priority-based service goal.

Suggested Citation

  • Kakillioglu, Emre Anıl & Yıldız Aktaş, Melike & Fescioglu-Unver, Nilgun, 2022. "Self-controlling resource management model for electric vehicle fast charging stations with priority service," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s036054422102524x
    DOI: 10.1016/j.energy.2021.122276
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    References listed on IDEAS

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    Cited by:

    1. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. Cong Zhang & Qun Gao & Ke Peng & Yan Jiang, 2023. "An EV Charging Guidance Strategy Based on the Hierarchical Comprehensive Evaluation Method," Energies, MDPI, vol. 16(7), pages 1-16, March.
    3. Nazari-Heris, Morteza & Loni, Abdolah & Asadi, Somayeh & Mohammadi-ivatloo, Behnam, 2022. "Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles," Applied Energy, Elsevier, vol. 311(C).
    4. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    5. Zhao, Zhonghao & Lee, Carman K.M. & Ren, Jingzheng, 2024. "A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile," Applied Energy, Elsevier, vol. 355(C).

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