IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i4p873-d1589619.html
   My bibliography  Save this article

A Bi-Level Method for Flexibility Feature Extraction and User Clustering Based on Real-World Data from Independent Smart Meters of Residential Electric Vehicle Users

Author

Listed:
  • Jian Zhang

    (State Grid Tianjin Electric Power Company, Hebei District, Tianjin 300010, China)

  • Shujun Li

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Lili Li

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Guoqiang Zu

    (State Grid Tianjin Electric Power Company, Hebei District, Tianjin 300010, China)

  • Yongchun Wang

    (Sichuan Energy Internet Research Institute, Tsinghua University, Chengdu 610042, China)

  • Ting Yang

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

Residential electric vehicle (EV) chargers in China are typically paired with independent smart meters. A bi-level method for extracting flexibility features and clustering residential EV users is proposed based on real-world smart meter data, offering significant advantages in terms of data accessibility and implementation feasibility. First, real-world smart meter data from over 5000 residential EV users are partitioned into charging segments, which are then filtered and grouped. At the charging segment level, three charging features are extracted using the Pearson correlation method, and six categories of charging segments are clustered using a Gaussian Mixture Model (GMM). Subsequently, at the user level, two flexibility features are extracted based on the charging segment clustering results, and five categories of residential EV users are clustered using the K-means algorithm. The results of the bi-level method are presented and analyzed, with its effectiveness validated by comparing the trends of flexibility features across different user categories between the training dataset and the validation dataset. In the concluding section, the limitations of the current research are discussed, and potential directions for further research are outlined.

Suggested Citation

  • Jian Zhang & Shujun Li & Lili Li & Guoqiang Zu & Yongchun Wang & Ting Yang, 2025. "A Bi-Level Method for Flexibility Feature Extraction and User Clustering Based on Real-World Data from Independent Smart Meters of Residential Electric Vehicle Users," Energies, MDPI, vol. 18(4), pages 1-23, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:873-:d:1589619
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/4/873/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/4/873/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Minseok Jang & Hyun-Cheol Jeong & Taegon Kim & Sung-Kwan Joo, 2021. "Load Profile-Based Residential Customer Segmentation for Analyzing Customer Preferred Time-of-Use (TOU) Tariffs," Energies, MDPI, vol. 14(19), pages 1-12, September.
    3. Li, Kehua & Ma, Zhenjun & Robinson, Duane & Ma, Jun, 2018. "Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering," Applied Energy, Elsevier, vol. 231(C), pages 331-342.
    4. Kim, Jae D., 2019. "Insights into residential EV charging behavior using energy meter data," Energy Policy, Elsevier, vol. 129(C), pages 610-618.
    5. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
    6. Alexandra Märtz & Uwe Langenmayr & Sabrina Ried & Katrin Seddig & Patrick Jochem, 2022. "Charging Behavior of Electric Vehicles: Temporal Clustering Based on Real-World Data," Energies, MDPI, vol. 15(18), pages 1-26, September.
    7. 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).
    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. Huang, Pei & Ma, Zhenliang, 2024. "Unveiling electric vehicle (EV) charging patterns and their transformative role in electricity balancing and delivery: Insights from real-world data in Sweden," Renewable Energy, Elsevier, vol. 236(C).
    2. Fu, Zhi & Liu, Xiaochen & Zhang, Ji & Zhang, Tao & Liu, Xiaohua & Jiang, Yi, 2025. "Orderly solar charging of electric vehicles and its impact on charging behavior: A year-round field experiment," Applied Energy, Elsevier, vol. 381(C).
    3. 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).
    4. Zhao, Yang & Jiang, Ziyue & Chen, Xinyu & Liu, Peng & Peng, Tianduo & Shu, Zhan, 2023. "Toward environmental sustainability: data-driven analysis of energy use patterns and load profiles for urban electric vehicle fleets," Energy, Elsevier, vol. 285(C).
    5. 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.
    6. 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).
    7. 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).
    8. E. Catalina Vallejo-Coral & Ricardo Garzón & Miguel Darío Ortega López & Javier Martínez-Gómez & Marcelo Moya, 2024. "Determine the Profiles of Power Consumption in Commercial Buildings in a Very Hot Humid Climate Using a Temporary Series," Sustainability, MDPI, vol. 16(22), pages 1-18, November.
    9. Meintjes, Tiago & Castro, Rui & Pires, A.J., 2021. "Impact of vehicle charging on Portugal's national electricity load profile in 2030," Utilities Policy, Elsevier, vol. 73(C).
    10. Mauro Zucca & Vincenzo Cirimele & Jorge Bruna & Davide Signorino & Erika Laporta & Jacopo Colussi & Miguel Angel Alonso Tejedor & Federico Fissore & Umberto Pogliano, 2021. "Assessment of the Overall Efficiency in WPT Stations for Electric Vehicles," Sustainability, MDPI, vol. 13(5), pages 1-19, February.
    11. Minseok Jang & Hyun Cheol Jeong & Taegon Kim & Dong Hee Suh & Sung-Kwan Joo, 2021. "Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape," Energies, MDPI, vol. 14(22), pages 1-15, November.
    12. Liu, Aaron & Miller, Wendy & Cholette, Michael E. & Ledwich, Gerard & Crompton, Glenn & Li, Yong, 2021. "A multi-dimension clustering-based method for renewable energy investment planning," Renewable Energy, Elsevier, vol. 172(C), pages 651-666.
    13. Aree Wangsupphaphol & Surachai Chaitusaney, 2022. "Subsidizing Residential Low Priority Smart Charging: A Power Management Strategy for Electric Vehicle in Thailand," Sustainability, MDPI, vol. 14(10), pages 1-15, May.
    14. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    15. Zheng, Wen & Xu, Xiao & Huang, Yuan & Zhu, Feng & Yang, Yuyan & Liu, Junyong & Hu, Weihao, 2023. "Adaptive robust scheduling optimization of a smart commercial building considering joint energy and reserve markets," Energy, Elsevier, vol. 283(C).
    16. 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).
    17. 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).
    18. Ignacio Benítez & José-Luis Díez, 2022. "Automated Detection of Electric Energy Consumption Load Profile Patterns," Energies, MDPI, vol. 15(6), pages 1-26, March.
    19. Jasmine Ramsebner & Albert Hiesl & Reinhard Haas, 2020. "Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings," Energies, MDPI, vol. 13(22), pages 1-23, November.
    20. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).

    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:gam:jeners:v:18:y:2025:i:4:p:873-:d:1589619. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.