IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v348y2023ics0306261923007766.html
   My bibliography  Save this article

Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations

Author

Listed:
  • Sheng, Yujie
  • Zeng, Hongtai
  • Guo, Qinglai
  • Yu, Yang
  • Li, Qiang

Abstract

The rapid development of electric vehicles (EVs) has increased the demand for public fast-charging stations (FCSs). Multiple self-interested charging service operators (CSOs) in an urban transportation network compete with each other through pricing signals to maximize their respective profits, where the information about customer demands and heterogeneous preferences work as important decision criteria. With the prevailing data transaction, the information superiority differences between CSOs may significantly affect the competitive pattern in the charging market. In this study, a Nash-Stackelberg-Nash game framework is established to investigate the strategic pricing of CSOs under different information distribution scenarios. The customer response pattern, namely, the routing and charging choices of EV drivers to pricing signals, is described via a computationally efficient extended stochastic user equilibrium model comprehensively considering heterogeneous customer preferences and decision stochasticity. Then, aiming at different data forms, both parameter-driven and sample-driven CSO pricing models are proposed. The pricing models are reformulated as a tractable single-level mixed-integer linear program, and the game equilibrium is solved alternately via Gauss-Seidel iteration. Numerical simulations are conducted to analyze the impact of single CSO information superiority and double CSO information distribution on CSO profitability and customer experience in both data forms. The influences of endogenous factors like CSO data amount and processing ability, as well as exogenous scenario parameters like customer preference distribution and perception error distribution, are discussed.

Suggested Citation

  • Sheng, Yujie & Zeng, Hongtai & Guo, Qinglai & Yu, Yang & Li, Qiang, 2023. "Impact of customer portrait information superiority on competitive pricing of EV fast-charging stations," Applied Energy, Elsevier, vol. 348(C).
  • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923007766
    DOI: 10.1016/j.apenergy.2023.121412
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923007766
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2023.121412?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dirk Bergemann & Alessandro Bonatti, 2019. "Markets for Information: An Introduction," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 85-107, August.
    2. Wang, Ning & Tian, Hangqi & Zhu, Shunbo & Li, Yuan, 2022. "Analysis of public acceptance of electric vehicle charging scheduling based on the technology acceptance model," Energy, Elsevier, vol. 258(C).
    3. He, Fang & Wu, Di & Yin, Yafeng & Guan, Yongpei, 2013. "Optimal deployment of public charging stations for plug-in hybrid electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 87-101.
    4. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    5. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    6. Pierre Pinson & Liyang Han & Jalal Kazempour, 2022. "Regression markets and application to energy forecasting," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 533-573, October.
    7. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    8. Wang, Bohong & Guo, Qinglai & Yu, Yang, 2022. "Mechanism design for data sharing: An electricity retail perspective," Applied Energy, Elsevier, vol. 314(C).
    9. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    10. Wang, Bohong & Guo, Qinglai & Yang, Tianyu & Xu, Luo & Sun, Hongbin, 2021. "Data valuation for decision-making with uncertainty in energy transactions: A case of the two-settlement market system," Applied Energy, Elsevier, vol. 288(C).
    11. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    12. Omar Isaac Asensio & Kevin Alvarez & Arielle Dror & Emerson Wenzel & Catharina Hollauer & Sooji Ha, 2020. "Real-time data from mobile platforms to evaluate sustainable transportation infrastructure," Nature Sustainability, Nature, vol. 3(6), pages 463-471, June.
    13. He, Fang & Yin, Yafeng & Lawphongpanich, Siriphong, 2014. "Network equilibrium models with battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 306-319.
    14. Dong, Xiaohong & Mu, Yunfei & Xu, Xiandong & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qi, Yan, 2018. "A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks," Applied Energy, Elsevier, vol. 225(C), pages 857-868.
    15. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
    16. Jiwoong Shin & K. Sudhir, 2010. "A Customer Management Dilemma: When Is It Profitable to Reward One's Own Customers?," Marketing Science, INFORMS, vol. 29(4), pages 671-689, 07-08.
    17. Zhou, Zhe & Moura, Scott J. & Zhang, Hongcai & Zhang, Xuan & Guo, Qinglai & Sun, Hongbin, 2021. "Power-traffic network equilibrium incorporating behavioral theory: A potential game perspective," Applied Energy, Elsevier, vol. 289(C).
    18. Sheng, Yujie & Guo, Qinglai & Chen, Feng & Xu, Luo & Zhang, Yang, 2021. "Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing," Applied Energy, Elsevier, vol. 301(C).
    19. Si, Zhiyuan & Yang, Ming & Yu, Yixiao & Ding, Tingting, 2021. "Photovoltaic power forecast based on satellite images considering effects of solar position," Applied Energy, Elsevier, vol. 302(C).
    20. Yanyan Xu & Serdar Çolak & Emre C. Kara & Scott J. Moura & Marta C. González, 2018. "Planning for electric vehicle needs by coupling charging profiles with urban mobility," Nature Energy, Nature, vol. 3(6), pages 484-493, June.
    Full references (including those not matched with items on IDEAS)

    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. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    2. Shafqat Jawad & Junyong Liu, 2020. "Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends," Energies, MDPI, vol. 13(13), pages 1-24, July.
    3. 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.
    4. 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).
    5. Zhou, Guanyu & Dong, Qianyu & Zhao, Yuming & Wang, Han & Jian, Linni & Jia, Youwei, 2023. "Bilevel optimization approach to fast charging station planning in electrified transportation networks," Applied Energy, Elsevier, vol. 350(C).
    6. Li, Zepeng & Wu, Qiuwei & Li, Hui & Nie, Chengkai & Tan, Jin, 2024. "Distributed low-carbon economic dispatch of integrated power and transportation system," Applied Energy, Elsevier, vol. 353(PA).
    7. Liu, Haoxiang & Wang, David Z.W., 2017. "Locating multiple types of charging facilities for battery electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 30-55.
    8. Crozier, Constance & Morstyn, Thomas & McCulloch, Malcolm, 2020. "The opportunity for smart charging to mitigate the impact of electric vehicles on transmission and distribution systems," Applied Energy, Elsevier, vol. 268(C).
    9. Lingshu Zhong & Mingyang Pei, 2020. "Optimal Design for a Shared Swap Charging System Considering the Electric Vehicle Battery Charging Rate," Energies, MDPI, vol. 13(5), pages 1-16, March.
    10. Chen, Zhibin & He, Fang & Yin, Yafeng, 2016. "Optimal deployment of charging lanes for electric vehicles in transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 344-365.
    11. Xu, Min & Meng, Qiang & Liu, Kai & Yamamoto, Toshiyuki, 2017. "Joint charging mode and location choice model for battery electric vehicle users," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 68-86.
    12. Cen, Xuekai & Lo, Hong K. & Li, Lu & Lee, Enoch, 2018. "Modeling electric vehicles adoption for urban commute trips," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 431-454.
    13. Liu, Haoxiang & Zou, Yuncheng & Chen, Ya & Long, Jiancheng, 2021. "Optimal locations and electricity prices for dynamic wireless charging links of electric vehicles for sustainable transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    14. Shen, Max & Li, Meng & He , Fang & Jia, Yinghao, 2016. "Strategic Charging Infrastructure Deployment for Electric Vehicles," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6rp6n4sf, Institute of Transportation Studies, UC Berkeley.
    15. Wang, Hua & Meng, Qiang & Wang, Jing & Zhao, De, 2021. "An electric-vehicle corridor model in a dense city with applications to charging location and traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 79-99.
    16. Liu, Shaojun & Wang, David Z.W. & Tian, Qingyun & Lin, Yun Hui, 2024. "Optimal configuration of dynamic wireless charging facilities considering electric vehicle battery capacity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    17. Ke, Jintao & Cen, Xuekai & Yang, Hai & Chen, Xiqun & Ye, Jieping, 2019. "Modelling drivers’ working and recharging schedules in a ride-sourcing market with electric vehicles and gasoline vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 125(C), pages 160-180.
    18. Sheng, Yujie & Guo, Qinglai & Chen, Feng & Xu, Luo & Zhang, Yang, 2021. "Coordinated pricing of coupled urban Power-Traffic Networks: The value of information sharing," Applied Energy, Elsevier, vol. 301(C).
    19. Nie, Yu (Marco) & Ghamami, Mehrnaz & Zockaie, Ali & Xiao, Feng, 2016. "Optimization of incentive polices for plug-in electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 103-123.
    20. Li, Xiaopeng & Ma, Jiaqi & Cui, Jianxun & Ghiasi, Amir & Zhou, Fang, 2016. "Design framework of large-scale one-way electric vehicle sharing systems: A continuum approximation model," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 21-45.

    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:eee:appene:v:348:y:2023:i:c:s0306261923007766. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.