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An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory

Author

Listed:
  • Yan Bao

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Fangyu Chang

    (China Railway Engineering Design and Consulting Group Co., Ltd., Beijing 100055, China)

  • Jinkai Shi

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Pengcheng Yin

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • Weige Zhang

    (National Active Distribution Network Technology Research Center (NANTEC), Beijing Jiaotong University, Beijing 100044, China)

  • David Wenzhong Gao

    (Department of Electrical and Computer Engineering, University of Denver, Denver, CO 80210, USA)

Abstract

Within the context of sustainable development and a low-carbon economy, electric vehicles (EVs) are regarded as a promising alternative to engine vehicles. Since the increase of charging EVs brings new challenges to charging stations and distribution utility in terms of economy and reliability, EV charging should be coordinated to form a friendly and proper load. This paper proposes a novel approach for pricing of charging service fees in a public charging station based on prospect theory. This behavioral economics-based pricing mechanism will guide EV users to coordinated charging spontaneously. By introducing prospect theory, a model that reflects the EV owner’s response to price is established first, considering the price factor and the state-of-charge (SOC) of batteries. Meanwhile, the quantitative relationship between the utility value and the charging price or SOC is analyzed in detail. The EV owner’s response mechanism is used in modeling the charging load after pricing optimization. Accordingly, by using the particle swarm optimization algorithm, pricing optimization is performed to achieve multiple objectives such as minimizing the peak-to-valley ratio and electricity costs of the charging station. Through case studies, the determined time-of-use charging prices by pricing optimization is validated to be effective in coordinating EV users’ behavior, and benefiting both the station operator and power systems.

Suggested Citation

  • Yan Bao & Fangyu Chang & Jinkai Shi & Pengcheng Yin & Weige Zhang & David Wenzhong Gao, 2022. "An Approach for Pricing of Charging Service Fees in an Electric Vehicle Public Charging Station Based on Prospect Theory," Energies, MDPI, vol. 15(14), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5308-:d:868555
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    References listed on IDEAS

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    Cited by:

    1. Xinghao Zhang & Yan Huang & Zhaowei Zhang & Huipin Lin & Yu Zeng & Mingyu Gao, 2022. "A Hybrid Method for State-of-Charge Estimation for Lithium-Ion Batteries Using a Long Short-Term Memory Network Combined with Attention and a Kalman Filter," Energies, MDPI, vol. 15(18), pages 1-26, September.
    2. Fang, Daohong & Tang, Hao & Hatziargyriou, Nikos & Zhang, Tao & Chen, Wenjuan & Zhang, Qianli, 2024. "Dual-center control scheme and FF-DHRL-based collaborative optimization for charging stations under intra-day peak-shaving demand," Applied Energy, Elsevier, vol. 368(C).

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