IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i14p5308-d868555.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/14/5308/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/14/5308/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gao, Kun & Sun, Lijun & Yang, Ying & Meng, Fanyu & Qu, Xiaobo, 2021. "Cumulative prospect theory coupled with multi-attribute decision making for modeling travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 1-21.
    2. Plante, Charles & Lassoued, Rim & Phillips, Peter W.B., 2017. "The Social Determinants of Cognitive Bias: The Effects of Low Capability on Decision Making in a Framing Experiment," SocArXiv u62cx, Center for Open Science.
    3. Zhou, Yuyang & Wang, Peiyu & Zheng, Shuyan & Zhao, Minhe & Lam, William H.K. & Chen, Anthony & Sze, N.N. & Chen, Yanyan, 2024. "Modeling dynamic travel mode choices using cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    4. Tian, Ye & Li, Yudi & Sun, Jian, 2022. "Stick or carrot for traffic demand management? Evidence from experimental economics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 235-254.
    5. Ji, Xiangfeng & Chu, Yanyu, 2020. "A target-oriented bi-attribute user equilibrium model with travelers’ perception errors on the tolled traffic network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    6. Li, Xue-yan & Li, Xue-mei & Yang, Lingrun & Li, Jing, 2018. "Dynamic route and departure time choice model based on self-adaptive reference point and reinforcement learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 77-92.
    7. Kemel, Emmanuel & Paraschiv, Corina, 2013. "Prospect Theory for joint time and money consequences in risk and ambiguity," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 81-95.
    8. Xu, Junxiang & Zhang, Jin & Guo, Jingni, 2021. "Contribution to the field of traffic assignment: A boundedly rational user equilibrium model with uncertain supply and demand," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    9. Xiangfeng Ji & Xiaoyu Ao, 2021. "Travelers’ Bi-Attribute Decision Making on the Risky Mode Choice with Flow-Dependent Salience Theory," Sustainability, MDPI, vol. 13(7), pages 1-24, April.
    10. Geng, Kexin & Wang, Yacan & Cherchi, Elisabetta & Guarda, Pablo, 2023. "Commuter departure time choice behavior under congestion charge: Analysis based on cumulative prospect theory," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    11. Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    12. Joo, M. Hashemi & Parhizgari, A.M., 2021. "A behavioral explanation of credit ratings and leverage adjustments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    13. Li, Xue-yan & Li, Xue-mei & Li, Xue-wei & Qiu, He-ting, 2017. "Multi-agent fare optimization model of two modes problem and its analysis based on edge of chaos," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 405-419.
    14. Xie, Chi & Liu, Zugang, 2014. "On the stochastic network equilibrium with heterogeneous choice inertia," Transportation Research Part B: Methodological, Elsevier, vol. 66(C), pages 90-109.
    15. Bahamonde-Birke, Francisco J., 2018. "Estimating the reference frame: A smooth twice-differentiable utility function for non-compensatory loss-averse decision-making," Journal of choice modelling, Elsevier, vol. 28(C), pages 71-81.
    16. Qinghui Xu & Xiangfeng Ji, 2020. "User Equilibrium Analysis Considering Travelers’ Context-Dependent Route Choice Behavior on the Risky Traffic Network," Sustainability, MDPI, vol. 12(17), pages 1-25, August.
    17. Giselle Moraes Ramos & Winnie Daamen & Serge Hoogendoorn, 2014. "A State-of-the-Art Review: Developments in Utility Theory, Prospect Theory and Regret Theory to Investigate Travellers' Behaviour in Situations Involving Travel Time Uncertainty," Transport Reviews, Taylor & Francis Journals, vol. 34(1), pages 46-67, January.
    18. Trond G. Husby & Elco E. Koks, 2017. "Household migration in disaster impact analysis: incorporating behavioural responses to risk," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 87(1), pages 287-305, May.
    19. Donato Masciandaro & Davide Romelli, 2018. "To Be or not to Be a Euro Country? The Behavioural Political Economics of Currency Unions," BAFFI CAREFIN Working Papers 1883, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    20. Xinming Zang & Zhenqi Guo & Jingai Ma & Yongguang Zhong & Xiangfeng Ji, 2021. "Target-Oriented User Equilibrium Considering Travel Time, Late Arrival Penalty, and Travel Cost on the Stochastic Tolled Traffic Network," Sustainability, MDPI, vol. 13(17), pages 1-22, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:14:p:5308-:d:868555. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.