IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v257y2022ics0360544222016036.html
   My bibliography  Save this article

Electric vehicle charging station diffusion: An agent-based evolutionary game model in complex networks

Author

Listed:
  • Huang, Xingjun
  • Lin, Yun
  • Lim, Ming K.
  • Zhou, Fuli
  • Liu, Feng

Abstract

The “chicken-and-egg” link between charging infrastructure and electric vehicle adoption complicates charging station investment, yet existing research lacks significant understanding of this relationship, particularly in complex network settings. To this end, our research designs a novel agent-based evolutionary game model that incorporates consumers' microscopic behavior into the dynamics of charging station diffusion. Based on a case study, the diffusion of charging stations and electric vehicles under current market conditions is simulated and the impact of the network topology is investigated. Results show that: (1) combined with existing policies, the carbon tax policy could increase the charging station proportion by 17.06%; (2) there is an inverted U-shaped effect between electricity prices and the proliferation of charging stations and electric vehicles; (3) the negative impact of electric vehicle social networks can be transferred to charging station proliferation; (4) there are two priorities for the proliferation of the two industries: prioritizing increasing the clustering coefficient, followed by decreasing the average path length, and increasing the clustering coefficient is better than increasing the individual degree; (5) relevant factors (e.g., construction subsidies, carbon taxes, early high electricity prices, high clustering factor networks) contribute to the conversion of plug-in electric vehicles to battery electric vehicles.

Suggested Citation

  • Huang, Xingjun & Lin, Yun & Lim, Ming K. & Zhou, Fuli & Liu, Feng, 2022. "Electric vehicle charging station diffusion: An agent-based evolutionary game model in complex networks," Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:energy:v:257:y:2022:i:c:s0360544222016036
    DOI: 10.1016/j.energy.2022.124700
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222016036
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124700?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. Neaimeh, Myriam & Salisbury, Shawn D. & Hill, Graeme A. & Blythe, Philip T. & Scoffield, Don R. & Francfort, James E., 2017. "Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles," Energy Policy, Elsevier, vol. 108(C), pages 474-486.
    2. Fetene, Gebeyehu M. & Hirte, Georg & Kaplan, Sigal & Prato, Carlo G. & Tscharaktschiew, Stefan, 2016. "The economics of workplace charging," Transportation Research Part B: Methodological, Elsevier, vol. 88(C), pages 93-118.
    3. Ardeshiri, Ali & Rashidi, Taha Hossein, 2020. "Willingness to pay for fast charging station for electric vehicles with limited market penetration making," Energy Policy, Elsevier, vol. 147(C).
    4. Globisch, Joachim & Plötz, Patrick & Dütschke, Elisabeth & Wietschel, Martin, 2019. "Consumer preferences for public charging infrastructure for electric vehicles," Transport Policy, Elsevier, vol. 81(C), pages 54-63.
    5. Li, Jingjing & Jiao, Jianling & Tang, Yunshu, 2019. "An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network," Energy Policy, Elsevier, vol. 129(C), pages 1-12.
    6. Wang, Ning & Tang, Linhao & Zhang, Wenjian & Guo, Jiahui, 2019. "How to face the challenges caused by the abolishment of subsidies for electric vehicles in China?," Energy, Elsevier, vol. 166(C), pages 359-372.
    7. Zhao, Dan & Ji, Shou-feng & Wang, He-ping & Jiang, Li-wen, 2021. "How do government subsidies promote new energy vehicle diffusion in the complex network context? A three-stage evolutionary game model," Energy, Elsevier, vol. 230(C).
    8. Marion, Justin & Muehlegger, Erich, 2018. "Tax compliance and fiscal externalities: Evidence from U.S. diesel taxation," Journal of Public Economics, Elsevier, vol. 160(C), pages 1-13.
    9. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    10. Shi, Yingying & Wei, Zixiang & Shahbaz, Muhammad & Zeng, Yongchao, 2021. "Exploring the dynamics of low-carbon technology diffusion among enterprises: An evolutionary game model on a two-level heterogeneous social network," Energy Economics, Elsevier, vol. 101(C).
    11. Silvia, Chris & Krause, Rachel M., 2016. "Assessing the impact of policy interventions on the adoption of plug-in electric vehicles: An agent-based model," Energy Policy, Elsevier, vol. 96(C), pages 105-118.
    12. Tan, Ruipeng & Lin, Boqiang, 2020. "Are people willing to support the construction of charging facilities in China?," Energy Policy, Elsevier, vol. 143(C).
    13. Qian, Lixian & Grisolía, Jose M. & Soopramanien, Didier, 2019. "The impact of service and government-policy attributes on consumer preferences for electric vehicles in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 122(C), pages 70-84.
    14. Huang, Xingjun & Lin, Yun & Lim, Ming K. & Zhou, Fuli & Ding, Rui & Zhang, Zusheng, 2022. "Evolutionary dynamics of promoting electric vehicle-charging infrastructure based on public–private partnership cooperation," Energy, Elsevier, vol. 239(PD).
    15. Münzel, Christiane & Plötz, Patrick & Sprei, Frances & Gnann, Till, 2019. "How large is the effect of financial incentives on electric vehicle sales? – A global review and European analysis," Energy Economics, Elsevier, vol. 84(C).
    16. Encarnação, Sara & Santos, Fernando P. & Santos, Francisco C. & Blass, Vered & Pacheco, Jorge M. & Portugali, Juval, 2018. "Paths to the adoption of electric vehicles: An evolutionary game theoretical approach," Transportation Research Part B: Methodological, Elsevier, vol. 113(C), pages 24-33.
    17. Quan, Ji & Yang, Xiukang & Wang, Xianjia, 2018. "Spatial public goods game with continuous contributions based on Particle Swarm Optimization learning and the evolution of cooperation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 973-983.
    18. Li, Junqiang & Ren, Hao & Wang, Mingyue, 2021. "How to escape the dilemma of charging infrastructure construction? A multi-sectorial stochastic evolutionary game model," Energy, Elsevier, vol. 231(C).
    19. Pagani, M. & Korosec, W. & Chokani, N. & Abhari, R.S., 2019. "User behaviour and electric vehicle charging infrastructure: An agent-based model assessment," Applied Energy, Elsevier, vol. 254(C).
    20. Xu, Hedong & Fan, Suohai & Tian, Cunzhi & Xiao, Xinrong, 2019. "Evolutionary investor sharing game on networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 138-145.
    21. Yang, Han-Xin & Chen, Xiaojie, 2018. "Promoting cooperation by punishing minority," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 460-466.
    22. Sun, Xiaohua & Liu, Xiaoling & Wang, Yun & Yuan, Fang, 2019. "The effects of public subsidies on emerging industry: An agent-based model of the electric vehicle industry," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 281-295.
    23. Herberz, Mario & Hahnel, Ulf J.J. & Brosch, Tobias, 2020. "The importance of consumer motives for green mobility: A multi-modal perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 102-118.
    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. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    2. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(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. Wang, Yitong & Fan, Ruguo & Du, Kang & Bao, Xuguang, 2023. "Exploring incentives to promote electric vehicles diffusion under subsidy abolition: An evolutionary analysis on multiplex consumer social networks," Energy, Elsevier, vol. 276(C).
    2. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Hubacek, Klaus, 2022. "Strategies for applying carbon trading to the new energy vehicle market in China: An improved evolutionary game analysis for the bus industry," Energy, Elsevier, vol. 259(C).
    3. Chen, Rongkai & Fan, Ruguo & Wang, Dongxue & Yao, Qianyi, 2023. "Effects of multiple incentives on electric vehicle charging infrastructure deployment in China: An evolutionary analysis in complex network," Energy, Elsevier, vol. 264(C).
    4. Philip, Thara & Whitehead, Jake & Prato, Carlo G., 2023. "Adoption of electric vehicles in a laggard, car-dependent nation: Investigating the potential influence of V2G and broader energy benefits on adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 167(C).
    5. Huang, Xingjun & Lin, Yun & Lim, Ming K. & Zhou, Fuli & Ding, Rui & Zhang, Zusheng, 2022. "Evolutionary dynamics of promoting electric vehicle-charging infrastructure based on public–private partnership cooperation," Energy, Elsevier, vol. 239(PD).
    6. Li, Jingjing & Jiao, Jianling & Tang, Yunshu, 2020. "Analysis of the impact of policies intervention on electric vehicles adoption considering information transmission—based on consumer network model," Energy Policy, Elsevier, vol. 144(C).
    7. Shi, Yingying & Wei, Zixiang & Shahbaz, Muhammad & Zeng, Yongchao, 2021. "Exploring the dynamics of low-carbon technology diffusion among enterprises: An evolutionary game model on a two-level heterogeneous social network," Energy Economics, Elsevier, vol. 101(C).
    8. Yao, Xusheng & Ma, Shoufeng & Bai, Yin & Jia, Ning, 2022. "When are new energy vehicle incentives effective? Empirical evidence from 88 pilot cities in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 207-224.
    9. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    10. Zou, Chen & Huang, Yongchun & Hu, Shiliang & Huang, Zhan, 2023. "Government participation in low-carbon technology transfer: An evolutionary game study," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    11. Aditya Ramji & Daniel Sperling & Lewis Fulton, 2024. "Sustainable Market Incentives -- Lessons from European Feebates for a ZEV Future," Papers 2401.15069, arXiv.org.
    12. Tao Li & Lei Ma & Zheng Liu & Chaonan Yi & Kaitong Liang, 2023. "Dual Carbon Goal-Based Quadrilateral Evolutionary Game: Study on the New Energy Vehicle Industry in China," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
    13. Peng, Yuan & Bai, Xuemei, 2023. "What EV users say about policy efficacy: Evidence from Shanghai," Transport Policy, Elsevier, vol. 132(C), pages 16-26.
    14. Brozynski, Max T. & Leibowicz, Benjamin D., 2022. "A multi-level optimization model of infrastructure-dependent technology adoption: Overcoming the chicken-and-egg problem," European Journal of Operational Research, Elsevier, vol. 300(2), pages 755-770.
    15. Ruguo Fan & Rongkai Chen, 2022. "Promotion Policies for Electric Vehicle Diffusion in China Considering Dynamic Consumer Preferences: A Network-Based Evolutionary Analysis," IJERPH, MDPI, vol. 19(9), pages 1-21, April.
    16. Khatua, Apalak & Ranjan Kumar, Rajeev & Kumar De, Supriya, 2023. "Institutional enablers of electric vehicle market: Evidence from 30 countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    17. Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    18. Liu, Yajie & Dong, Feng, 2022. "What are the roles of consumers, automobile production enterprises, and the government in the process of banning gasoline vehicles? Evidence from a tripartite evolutionary game model," Energy, Elsevier, vol. 238(PC).
    19. Chi, Yuan-Ying & Wang, Yuan-Yuan & Xu, Jin-Hua, 2021. "Estimating the impact of the license plate quota policy for ICEVs on new energy vehicle adoption by using synthetic control method," Energy Policy, Elsevier, vol. 149(C).
    20. Fan, Ruguo & Bao, Xuguang & Du, Kang & Wang, Yuanyuan & Wang, Yitong, 2022. "The effect of government policies and consumer green preferences on the R&D diffusion of new energy vehicles: A perspective of complex network games," Energy, Elsevier, vol. 254(PA).

    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:energy:v:257:y:2022:i:c:s0360544222016036. 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.journals.elsevier.com/energy .

    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.