IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v139y2023icp87-98.html
   My bibliography  Save this article

Charging station forecasting and scenario analysis in China

Author

Listed:
  • Liu, Bingchun
  • Song, Jiangji
  • Wang, Qingshan
  • Xu, Yan
  • Liu, Yifan

Abstract

China's electric vehicle industry has gained momentum due to multiple factors, but there is still a gap in demand for charging stations. China's subsidy policy for EVs is gradually withdrawing from the market and increasing subsidies for charging facilities to stabilize the growth of EVs. Taking the number of EVs as the decision parameter, this paper proposes a multi-factor combination prediction model of grey correlation and long and short-term memory. The model predicts that the number of EVs in China will reach 1.02 billion by 2030. The importance of factors is discussed qualitatively through scenario setting analysis of EV electricity consumption and power supply. It is found that charging stations in China will show varying degrees of growth from 2021 to 2030. Finally, corresponding suggestions are put forward for charging stations, including charging subsidies, technology R&D innovation policies, and licensing quotas.

Suggested Citation

  • Liu, Bingchun & Song, Jiangji & Wang, Qingshan & Xu, Yan & Liu, Yifan, 2023. "Charging station forecasting and scenario analysis in China," Transport Policy, Elsevier, vol. 139(C), pages 87-98.
    • Handle: RePEc:eee:trapol:v:139:y:2023:i:c:p:87-98
    DOI: 10.1016/j.tranpol.2023.05.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X23001439
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2023.05.012?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. Khan, Muhammad Arshad, 2015. "Modelling and forecasting the demand for natural gas in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 1145-1159.
    2. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    3. Wang, Zeyi & Zhang, Hengjia & Wang, Yingying & Wang, Yong & Lei, Lian & Liang, Chao & Wang, Yucai, 2023. "Deficit irrigation decision-making of indigowoad root based on a model coupling fuzzy theory and grey relational analysis," Agricultural Water Management, Elsevier, vol. 275(C).
    4. Nazari-Heris, Morteza & Loni, Abdolah & Asadi, Somayeh & Mohammadi-ivatloo, Behnam, 2022. "Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles," Applied Energy, Elsevier, vol. 311(C).
    5. Peng, Lu & Wang, Lin & Xia, De & Gao, Qinglu, 2022. "Effective energy consumption forecasting using empirical wavelet transform and long short-term memory," Energy, Elsevier, vol. 238(PB).
    6. Li, Wenbo & Long, Ruyin & Chen, Hong & Geng, Jichao, 2017. "A review of factors influencing consumer intentions to adopt battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 318-328.
    7. Li, Hongtao & Bai, Juncheng & Li, Yongwu, 2019. "A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    8. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2008. "Grey relation analysis of motor vehicular energy consumption in Taiwan," Energy Policy, Elsevier, vol. 36(7), pages 2556-2561, July.
    9. Zhang, Hao & Cai, Guixin, 2020. "Subsidy strategy on new-energy vehicle based on incomplete information: A Case in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    10. Sarraf, Fatemeh & Nejad, Shabnam Hashemi, 2020. "Improving performance evaluation based on balanced scorecard with grey relational analysis and data envelopment analysis approaches: Case study in water and wastewater companies," Evaluation and Program Planning, Elsevier, vol. 79(C).
    11. Sahraei, Mohammad Ali & Çodur, Merve Kayaci, 2022. "Prediction of transportation energy demand by novel hybrid meta-heuristic ANN," Energy, Elsevier, vol. 249(C).
    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. Huang, Wenxin & Wang, Jianguo & Wang, Jianping & Zeng, Haiyan & Zhou, Mi & Cao, Jinxin, 2025. "Assessment of the technical economic viability and carbon reduction potential of urban-scale photovoltaic generation for electric vehicle charging station," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    2. Zhang, Chengyu & Rezgui, Yacine & Luo, Zhiwen & Jiang, Ben & Zhao, Tianyi, 2024. "Simultaneous community energy supply-demand optimization by microgrid operation scheduling optimization and occupant-oriented flexible energy-use regulation," Applied Energy, Elsevier, vol. 373(C).
    3. Cao, Jianing & Han, Yuhang & Pan, Nan & Zhang, Jingcheng & Yang, Junwei, 2024. "A data-driven approach to urban charging facility expansion based on bi-level optimization: A case study in a Chinese city," Energy, Elsevier, vol. 300(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. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Wu, Zezhou & He, Qiufeng & Li, Jiarun & Bi, Guoqiang & Antwi-Afari, Maxwell Fordjour, 2023. "Public attitudes and sentiments towards new energy vehicles in China: A text mining approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    3. Liu, Bingchun & Song, Chengyuan & Wang, Qingshan & Zhang, Xinming & Chen, Jiali, 2022. "Research on regional differences of China's new energy vehicles promotion policies: A perspective of sales volume forecasting," Energy, Elsevier, vol. 248(C).
    4. Yi Xiao & Minghu Xie & Yi Hu & Ming Yi, 2023. "Effective multi‐step ahead container throughput forecasting under the complex context," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1823-1843, November.
    5. Huang, Youlin & Qian, Lixian, 2021. "Consumer adoption of electric vehicles in alternative business models," Energy Policy, Elsevier, vol. 155(C).
    6. Wang, Zhiwei & Lei, Tingzhou & Chang, Xia & Shi, Xinguang & Xiao, Ju & Li, Zaifeng & He, Xiaofeng & Zhu, Jinling & Yang, Shuhua, 2015. "Optimization of a biomass briquette fuel system based on grey relational analysis and analytic hierarchy process: A study using cornstalks in China," Applied Energy, Elsevier, vol. 157(C), pages 523-532.
    7. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    8. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    9. Zhou, Jianguo & Xu, Zhongtian, 2023. "Optimal sizing design and integrated cost-benefit assessment of stand-alone microgrid system with different energy storage employing chameleon swarm algorithm: A rural case in Northeast China," Renewable Energy, Elsevier, vol. 202(C), pages 1110-1137.
    10. Duan, Sudong & Zhang, Zhonghui & Wang, Zhaojun & Xiong, Xiaoyue & Chen, Xinhan & Que, Xiaoyu, 2025. "A study on mobile charging station combined with integrated energy system: Emphasis on energy dispatch strategy and multi-scenario analysis," Renewable Energy, Elsevier, vol. 239(C).
    11. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    12. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    13. Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
    14. Cheng, Min-Yuan & Vu, Quoc-Tuan, 2024. "Bio-inspired bidirectional deep machine learning for real-time energy consumption forecasting and management," Energy, Elsevier, vol. 302(C).
    15. Pei, Jingyin & Dong, Yunxuan & Guo, Pinghui & Wu, Thomas & Hu, Jianming, 2024. "A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting," Energy, Elsevier, vol. 305(C).
    16. Yurong Chen & Juan Zhang, 2024. "Impact of Consumer Subsidy Considering Subsidy Threshold on New Energy Vehicle Firms and Environmental Benefits," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    17. Wang, Yong & Yang, Zhongsen & Zhou, Ying & Liu, Hao & Yang, Rui & Sun, Lang & Sapnken, Flavian Emmanuel & Narayanan, Govindasami, 2025. "A novel structure adaptive new information priority grey Bernoulli model and its application in China's renewable energy production," Renewable Energy, Elsevier, vol. 239(C).
    18. Wen, Shenglin & Cui, Ningbo & Wang, Yaosheng & Gong, Daozhi & Xing, Liwen & Wu, Zongjun & Zhang, Yixuan & Zhao, Long & Fan, Junliang & Wang, Zhihui, 2024. "Optimizing deficit drip irrigation to improve yield,quality, and water productivity of apple in Loess Plateau of China," Agricultural Water Management, Elsevier, vol. 296(C).
    19. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    20. Jiang, Wenbing & Du, Lei & Wei, Huafei & Sun, Helin, 2024. "The influence of urban new energy development orientation on new energy technology innovation in firms," Renewable Energy, Elsevier, vol. 237(PD).

    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:trapol:v:139:y:2023:i:c:p:87-98. 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/30473/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.