IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v328y2022ics0306261922013812.html
   My bibliography  Save this article

Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning

Author

Listed:
  • Tikka, Ville
  • Haapaniemi, Jouni
  • Räisänen, Otto
  • Honkapuro, Samuli

Abstract

From the perspective of electricity distribution networks and the energy system, the increasing numbers of electric vehicles are among the most topical and challenging problems. The paper investigates a novel approach of a convolutional neural network-based modeling method for estimating the spatial distribution of electric vehicles. The proposed model extracts features from multilayer socioeconomic input raster data that are sequenced in strides and outputs a spatial estimation of EV distribution. Spatial forecasting or area forecasting is at the core of the distribution system operators’ planning and development process as it provides a solid foundation for stochastic load modeling and load development analysis. Present models mostly focus on stochastic load modeling, lacking the spatial forecasting aspect of EV distribution. The proposed model aims to enhance EV load modeling by providing a more accurate spatial approach to the models. The study uses large actual socioeconomic and vehicle registration data sets to tackle the modeling challenge. In comparison with previous studies on similar topics, the present study benefits from more samples resulting from an increase in the adoption of electric vehicles. The proposed model architecture performs adequately in predicting a spatial electric vehicle distribution; the CNN model reached a weighted average precision score of 0.91. The proposed methodology greatly enhances stochastic EV load modeling by providing a good spatial forecast of the initial EV locations, and the results can be further aggregated to support the electricity distribution system planning process. An energy-, material-, and cost-efficient electricity distribution system is the backbone of the modern energy system.

Suggested Citation

  • Tikka, Ville & Haapaniemi, Jouni & Räisänen, Otto & Honkapuro, Samuli, 2022. "Convolutional neural networks in estimating the spatial distribution of electric vehicles to support electricity grid planning," Applied Energy, Elsevier, vol. 328(C).
  • Handle: RePEc:eee:appene:v:328:y:2022:i:c:s0306261922013812
    DOI: 10.1016/j.apenergy.2022.120124
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922013812
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.120124?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. Jamali Jahromi, Ali & Mohammadi, Mohammad & Afrasiabi, Shahabodin & Afrasiabi, Mousa & Aghaei, Jamshid, 2022. "Probability density function forecasting of residential electric vehicles charging profile," Applied Energy, Elsevier, vol. 323(C).
    2. Kim, Jae D., 2019. "Insights into residential EV charging behavior using energy meter data," Energy Policy, Elsevier, vol. 129(C), pages 610-618.
    3. Erdem, Cumhur & Sentürk, Ismail & Simsek, Türker, 2010. "Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in Turkey: A case of hybrids," Energy Policy, Elsevier, vol. 38(6), pages 3038-3043, June.
    4. Pareschi, Giacomo & Küng, Lukas & Georges, Gil & Boulouchos, Konstantinos, 2020. "Are travel surveys a good basis for EV models? Validation of simulated charging profiles against empirical data," Applied Energy, Elsevier, vol. 275(C).
    5. Saarenpää, Jukka & Kolehmainen, Mikko & Niska, Harri, 2013. "Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption," Applied Energy, Elsevier, vol. 107(C), pages 456-464.
    6. Siddique, Choudhury & Afifah, Fatima & Guo, Zhaomiao & Zhou, Yan, 2022. "Data mining of plug-in electric vehicles charging behavior using supply-side data," Energy Policy, Elsevier, vol. 161(C).
    7. Benjamin Deneu & Maximilien Servajean & Pierre Bonnet & Christophe Botella & François Munoz & Alexis Joly, 2021. "Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-21, April.
    8. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    Full references (including those not matched with items on IDEAS)

    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. Prateek Bansal & Akanksha Sinha & Rubal Dua & Ricardo Daziano, 2019. "Eliciting Preferences of Ridehailing Users and Drivers: Evidence from the United States," Papers 1904.06695, arXiv.org.
    2. Brown, Marilyn A. & Kale, Snehal & Cha, Min-Kyeong & Chapman, Oliver, 2023. "Exploring the willingness of consumers to electrify their homes," Applied Energy, Elsevier, vol. 338(C).
    3. Sanguinetti, Angela & Favetti, Matthew & Hirschfelt, Kate & Kong, Nathaniel & Chakraborty, Debapriya & Alston-Stepnitz, Eli & Ma, Howard, 2023. "Developing a Vehicle Cost Calculator to Promote Electric Vehicle Adoption Among TNC Drivers," Institute of Transportation Studies, Working Paper Series qt1v44b5kp, Institute of Transportation Studies, UC Davis.
    4. Simona Bigerna & Silvia Micheli, 2018. "Attitudes Toward Electric Vehicles: The Case of Perugia Using a Fuzzy Set Analysis," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    5. Aritra Ghosh, 2020. "Possibilities and Challenges for the Inclusion of the Electric Vehicle (EV) to Reduce the Carbon Footprint in the Transport Sector: A Review," Energies, MDPI, vol. 13(10), pages 1-22, May.
    6. Hu, Dingding & Zhou, Kaile & Li, Fangyi & Ma, Dawei, 2022. "Electric vehicle user classification and value discovery based on charging big data," Energy, Elsevier, vol. 249(C).
    7. Jingchao, Zhang & Kotani, Koji & Saijo, Tatsuyoshi, 2018. "Public acceptance of environmentally friendly heating in Beijing: A case of a low temperature air source heat pump," Energy Policy, Elsevier, vol. 117(C), pages 75-85.
    8. Chorus, Caspar G. & Koetse, Mark J. & Hoen, Anco, 2013. "Consumer preferences for alternative fuel vehicles: Comparing a utility maximization and a regret minimization model," Energy Policy, Elsevier, vol. 61(C), pages 901-908.
    9. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    10. Zhang, Yong & Yu, Yifeng & Zou, Bai, 2011. "Analyzing public awareness and acceptance of alternative fuel vehicles in China: The case of EV," Energy Policy, Elsevier, vol. 39(11), pages 7015-7024.
    11. Hasan-Basri, Bakti & Mohd Mustafa, Muzafarshah & Bakar, Normizan, 2019. "Are Malaysian Consumers Willing to Pay for Hybrid Cars’ Attributes?," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 53(1), pages 121-134.
    12. Andriosopoulos, Kostas & Bigerna, Simona & Bollino, Carlo Andrea & Micheli, Silvia, 2018. "The impact of age on Italian consumers' attitude toward alternative fuel vehicles," Renewable Energy, Elsevier, vol. 119(C), pages 299-308.
    13. Dingyi Lu & Yunqian Lu & Kexin Zhang & Chuyuan Zhang & Shao-Chao Ma, 2023. "An Application Designed for Guiding the Coordinated Charging of Electric Vehicles," Sustainability, MDPI, vol. 15(14), pages 1-16, July.
    14. Brinkel, N.B.G. & Schram, W.L. & AlSkaif, T.A. & Lampropoulos, I. & van Sark, W.G.J.H.M., 2020. "Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits," Applied Energy, Elsevier, vol. 276(C).
    15. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    16. Syed Muhammad Ahsan & Hassan Abbas Khan & Sarmad Sohaib & Anas M. Hashmi, 2023. "Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations," Energies, MDPI, vol. 16(13), pages 1-15, June.
    17. Poder, Thomas G. & He, Jie, 2017. "Willingness to pay for a cleaner car: The case of car pollution in Quebec and France," Energy, Elsevier, vol. 130(C), pages 48-54.
    18. Muhammad Naveed Iqbal & Lauri Kütt & Matti Lehtonen & Robert John Millar & Verner Püvi & Anton Rassõlkin & Galina L. Demidova, 2021. "Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles," Sustainability, MDPI, vol. 13(3), pages 1-14, February.
    19. Peng, Ruoqing & Tang, Justin Hayse Chiwing G. & Yang, Xiong & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2024. "Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States," Applied Energy, Elsevier, vol. 355(C).
    20. Lin, Boqiang & Tan, Ruipeng, 2017. "Estimation of the environmental values of electric vehicles in Chinese cities," Energy Policy, Elsevier, vol. 104(C), pages 221-229.

    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:appene:v:328:y:2022:i:c:s0306261922013812. 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/405891/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.