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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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    1. Samy Faddel & Ali T. Al-Awami & Osama A. Mohammed, 2018. "Charge Control and Operation of Electric Vehicles in Power Grids: A Review," Energies, MDPI, vol. 11(4), pages 1-21, March.
    2. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    3. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    4. Stathopoulos, Amanda & Hess, Stephane, 2012. "Revisiting reference point formation, gains–losses asymmetry and non-linear sensitivities with an emphasis on attribute specific treatment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1673-1689.
    5. Nicholas C. Barberis, 2012. "Thirty Years of Prospect Theory in Economics: A Review and Assessment," NBER Working Papers 18621, National Bureau of Economic Research, Inc.
    6. Xu, Hongli & Lou, Yingyan & Yin, Yafeng & Zhou, Jing, 2011. "A prospect-based user equilibrium model with endogenous reference points and its application in congestion pricing," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 311-328, February.
    7. Jinil Han & Jongyoon Park & Kyungsik Lee, 2017. "Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power," Energies, MDPI, vol. 10(7), pages 1-15, July.
    8. Cui, Shaohua & Yao, Baozhen & Chen, Gang & Zhu, Chao & Yu, Bin, 2020. "The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution," Energy, Elsevier, vol. 198(C).
    9. Xu, Zhiwei & Hu, Zechun & Song, Yonghua & Zhao, Wei & Zhang, Yongwang, 2014. "Coordination of PEVs charging across multiple aggregators," Applied Energy, Elsevier, vol. 136(C), pages 582-589.
    10. Marija Zima-Bockarjova & Antans Sauhats & Lubov Petrichenko & Roman Petrichenko, 2020. "Charging and Discharging Scheduling for Electrical Vehicles Using a Shapley-Value Approach," Energies, MDPI, vol. 13(5), pages 1-21, March.
    11. Weige Zhang & Di Zhang & Biqiang Mu & Le Yi Wang & Yan Bao & Jiuchun Jiang & Hugo Morais, 2017. "Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids," Energies, MDPI, vol. 10(2), pages 1-19, January.
    12. Izabela Zoltowska & Jeremy Lin, 2021. "Optimal Charging Schedule Planning for Electric Buses Using Aggregated Day-Ahead Auction Bids," Energies, MDPI, vol. 14(16), pages 1-18, August.
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    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.

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