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Evaluation of the multi-dimensional growth potential of China's public charging facilities for electric vehicles through 2030

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  • Zhang, Ziqi
  • Chen, Zhong
  • Xing, Qiang
  • Ji, Zhenya
  • Zhang, Tian

Abstract

Based on almost 6 million charging records of 1588 charging stations in Jiangsu Province, China, this paper quantitatively analyzes the growth potential of public charging facilities for electric vehicles (EVs) in China towards 2030, from both aspects of saving power cost and providing flexible regulation capability. Based on the single EV charging optimization model, the unsupervised method is adopted to handle the efficiency problem of sampling calculation for massive actual data, and loop calculations were then conducted for sampling optimization. Bus charging stations (BCS), urban public charging stations (UPCS), and highway charging stations (HWCS)) are considered. Calculation results show that charging operators or users can save more than 20% of the power purchase cost (about 88.1 billion yuan) through intelligent charging management. By 2030, large-scale adoption of EVs would enable schedulable capacity that accounts for more than 10% of China's annual electricity generation from renewable energy sources (wind and photovoltaic energy). Moreover, electrical buses have the most potential now, while the figure for electrical passenger cars charged in UPCS would account for the greatest proportion in the future.

Suggested Citation

  • Zhang, Ziqi & Chen, Zhong & Xing, Qiang & Ji, Zhenya & Zhang, Tian, 2022. "Evaluation of the multi-dimensional growth potential of China's public charging facilities for electric vehicles through 2030," Utilities Policy, Elsevier, vol. 75(C).
  • Handle: RePEc:eee:juipol:v:75:y:2022:i:c:s0957178722000108
    DOI: 10.1016/j.jup.2022.101344
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    References listed on IDEAS

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    1. 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.
    2. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    3. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    4. Uddin, Kotub & Dubarry, Matthieu & Glick, Mark B., 2018. "The viability of vehicle-to-grid operations from a battery technology and policy perspective," Energy Policy, Elsevier, vol. 113(C), pages 342-347.
    5. Malec, Peter & Schienle, Melanie, 2014. "Nonparametric kernel density estimation near the boundary," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 57-76.
    6. Zachary A. Needell & James McNerney & Michael T. Chang & Jessika E. Trancik, 2016. "Potential for widespread electrification of personal vehicle travel in the United States," Nature Energy, Nature, vol. 1(9), pages 1-7, September.
    7. Wu, Xing, 2018. "Role of workplace charging opportunities on adoption of plug-in electric vehicles – Analysis based on GPS-based longitudinal travel data," Energy Policy, Elsevier, vol. 114(C), pages 367-379.
    8. Yan, Xing & Ozturk, Yusuf & Hu, Zechun & Song, Yonghua, 2018. "A review on price-driven residential demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 411-419.
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    Cited by:

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    2. Rosa, Carmen Brum & Francescatto, Matheus Binotto & Neuenfeldt Júnior, Alvaro Luiz & Bernardon, Daniel Pinheiro & dos Santos, Laura Lisiane Callai, 2023. "Regulatory analysis of E-mobility for Brazil: A comparative review and outlook," Utilities Policy, Elsevier, vol. 84(C).

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