4.7 Article

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

期刊

ENERGY
卷 257, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.124700

关键词

Agent -based evolutionary game model; Consumer adoption behavior; Charging station diffusion; Complex networks; China; Government intervention

资金

  1. Chinese National Funding of Social Science [18BJY066]
  2. Fundamental Research Funds for the Central Universities [2021CDJSKJC14]
  3. Chongqing Technology Innovation and Application Development Special [cstc2021jscx-gksbX0069]

向作者/读者索取更多资源

This research develops a novel agent-based evolutionary game model to investigate the relationship between charging infrastructure and electric vehicle adoption. The study finds that carbon tax policy, electricity prices, and network topology have significant impacts on the diffusion of charging stations and electric vehicles.
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 evolu-tionary 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 pro-liferation 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 pro-liferation 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. (c) 2022 Elsevier Ltd. All rights reserved.

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