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

Author

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  • 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
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    References listed on IDEAS

    as
    1. Wang, Bohong & Guo, Qinglai & Yu, Yang, 2022. "Mechanism design for data sharing: An electricity retail perspective," Applied Energy, Elsevier, vol. 314(C).
    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. 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).
    5. 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).
    6. 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.
    7. 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).
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Dirk Bergemann & Alessandro Bonatti, 2019. "Markets for Information: An Introduction," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 85-107, August.
    14. 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).
    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)

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