IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v280y2023ics0360544223016249.html
   My bibliography  Save this article

Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

Author

Listed:
  • Yang, Xiong
  • Peng, Zhenhan
  • Wang, Pinxi
  • Zhuge, Chengxiang

Abstract

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors.

Suggested Citation

  • Yang, Xiong & Peng, Zhenhan & Wang, Pinxi & Zhuge, Chengxiang, 2023. "Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016249
    DOI: 10.1016/j.energy.2023.128230
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223016249
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128230?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. Wenxia Liu & Shuya Niu & Huiting Xu & Xiaoying Li, 2016. "A New Method to Plan the Capacity and Location of Battery Swapping Station for Electric Vehicle Considering Demand Side Management," Sustainability, MDPI, vol. 8(6), pages 1-17, June.
    2. Zhang, Li & Shaffer, Brendan & Brown, Tim & Scott Samuelsen, G., 2015. "The optimization of DC fast charging deployment in California," Applied Energy, Elsevier, vol. 157(C), pages 111-122.
    3. Doucette, Reed T. & McCulloch, Malcolm D., 2011. "Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions," Applied Energy, Elsevier, vol. 88(7), pages 2315-2323, July.
    4. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    5. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    6. 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).
    7. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    8. Wang, Ying-Wei & Lin, Chuah-Chih, 2009. "Locating road-vehicle refueling stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 821-829, September.
    9. Naireeta Deb & Rajendra Singh & Richard R. Brooks & Kevin Bai, 2021. "A Review of Extremely Fast Charging Stations for Electric Vehicles," Energies, MDPI, vol. 14(22), pages 1-27, November.
    10. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    11. Aleksandar Janjić & Lazar Velimirović & Jelena Velimirović & Petar Vranić, 2021. "Estimating the optimal number and locations of electric vehicle charging stations: the application of multi-criteria p-median methodology," Transportation Planning and Technology, Taylor & Francis Journals, vol. 44(8), pages 827-842, November.
    12. Mubarak, Mamdouh & Üster, Halit & Abdelghany, Khaled & Khodayar, Mohammad, 2021. "Strategic network design and analysis for in-motion wireless charging of electric vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    13. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    14. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeswar Mahanta, 2018. "Review of recent trends in charging infrastructure planning for electric vehicles," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    15. Joonho Ko & Daejin Kim & Daisik Nam & Taekyung Lee, 2017. "Determining locations of charging stations for electric taxis using taxi operation data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(4), pages 420-433, May.
    16. Ramesh Chandra Majhi & Prakash Ranjitkar & Mingyue Sheng & Grant A. Covic & Doug James Wilson, 2021. "A systematic review of charging infrastructure location problem for electric vehicles," Transport Reviews, Taylor & Francis Journals, vol. 41(4), pages 432-455, July.
    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. Hasanien, Hany M. & Alsaleh, Ibrahim & Tostado-Véliz, Marcos & Zhang, Miao & Alateeq, Ayoob & Jurado, Francisco & Alassaf, Abdullah, 2024. "Hybrid particle swarm and sea horse optimization algorithm-based optimal reactive power dispatch of power systems comprising electric vehicles," Energy, Elsevier, vol. 286(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. Golsefidi, Atefeh Hemmati & Hüttel, Frederik Boe & Peled, Inon & Samaranayake, Samitha & Pereira, Francisco Câmara, 2023. "A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    2. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    3. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    4. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    5. Hassan S. Hayajneh & Xuewei Zhang, 2019. "Evaluation of Electric Vehicle Charging Station Network Planning via a Co-Evolution Approach," Energies, MDPI, vol. 13(1), pages 1-11, December.
    6. Zhao, Li & Li, Yuqi & Li, Shuai & Ke, Hanchen, 2023. "A frequency item mining based embedded feature selection algorithm and its application in energy consumption prediction of electric bus," Energy, Elsevier, vol. 271(C).
    7. Rabl, Regina & Reuter-Oppermann, Melanie & Jochem, Patrick E.P., 2024. "Charging infrastructure for electric vehicles in New Zealand," Transport Policy, Elsevier, vol. 148(C), pages 124-144.
    8. 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).
    9. Zhan, Weipeng & Wang, Zhenpo & Zhang, Lei & Liu, Peng & Cui, Dingsong & Dorrell, David G., 2022. "A review of siting, sizing, optimal scheduling, and cost-benefit analysis for battery swapping stations," Energy, Elsevier, vol. 258(C).
    10. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhao, Yiwen & Zhan, Weipeng, 2023. "Stacking regression technology with event profile for electric vehicle fast charging behavior prediction," Applied Energy, Elsevier, vol. 336(C).
    11. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    12. Tao, Miaomiao, 2024. "Dynamics between electric vehicle uptake and green development: Understanding the role of local government competition," Transport Policy, Elsevier, vol. 146(C), pages 227-240.
    13. Wei, Ran & Liu, Xiaoyue & Ou, Yi & Kiavash Fayyaz, S., 2018. "Optimizing the spatio-temporal deployment of battery electric bus system," Journal of Transport Geography, Elsevier, vol. 68(C), pages 160-168.
    14. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    15. Varga, Bogdan Ovidiu, 2013. "Electric vehicles, primary energy sources and CO2 emissions: Romanian case study," Energy, Elsevier, vol. 49(C), pages 61-70.
    16. Audoly, Richard & Vogt-Schilb, Adrien & Guivarch, Céline & Pfeiffer, Alexander, 2018. "Pathways toward zero-carbon electricity required for climate stabilization," Applied Energy, Elsevier, vol. 225(C), pages 884-901.
    17. 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).
    18. Youssef Amry & Elhoussin Elbouchikhi & Franck Le Gall & Mounir Ghogho & Soumia El Hani, 2022. "Electric Vehicle Traction Drives and Charging Station Power Electronics: Current Status and Challenges," Energies, MDPI, vol. 15(16), pages 1-30, August.
    19. Davidov, Sreten, 2020. "Optimal charging infrastructure planning based on a charging convenience buffer," Energy, Elsevier, vol. 192(C).
    20. 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.

    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:280:y:2023:i:c:s0360544223016249. 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.