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Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge

Author

Listed:
  • Huachun Han

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211100, China)

  • Huiyu Miu

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211100, China)

  • Shukang Lv

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211100, China)

  • Xiaodong Yuan

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211100, China)

  • Yi Pan

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211100, China)

  • Fei Zeng

    (The Electric Power Research Institute, Jiangsu Power Grid Company Ltd., Nanjing 211100, China)

Abstract

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

Suggested Citation

  • Huachun Han & Huiyu Miu & Shukang Lv & Xiaodong Yuan & Yi Pan & Fei Zeng, 2024. "Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge," Energies, MDPI, vol. 17(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:18:p:4716-:d:1483013
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    References listed on IDEAS

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    1. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Raiden Skala & Mohamed Ahmed T. A. Elgalhud & Katarina Grolinger & Syed Mir, 2023. "Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging," Energies, MDPI, vol. 16(10), pages 1-21, May.
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