IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i7p1651-d1366767.html
   My bibliography  Save this article

Charging Behavior Portrait of Electric Vehicle Users Based on Fuzzy C-Means Clustering Algorithm

Author

Listed:
  • Aixin Yang

    (Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Guiqing Zhang

    (Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Chenlu Tian

    (Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Wei Peng

    (Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Yechun Liu

    (Shandong Key Laboratory of Intelligent Building Technology, School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China)

Abstract

The rapid increase in electric vehicles (EVs) has led to a continuous expansion of electric vehicle (EV) charging stations, imposing significant load pressures on the power grid. Implementing orderly charging scheduling for EVs can mitigate the impact of large-scale charging on the power grid. However, the charging behavior of EVs significantly impacts the efficiency of orderly charging plans. By integrating user portrait technology and conducting research on optimized scheduling for EV charging, EV users can be accurately classified to meet the diverse needs of various user groups. This study establishes a user portrait model suitable for park areas, providing user group classification based on the user response potential for scheduling optimization. First, the FCM and feature aggregation methods are utilized to classify the quantities of features of EV users, obtaining user portrait classes. Second, based on these classes, a user portrait inventory for each EV is derived. Third, based on the priority of user response potential, this study presents a method for calculating the feature data of different user groups. The individual data information and priorities from the user portrait model are inputted into the EV-optimized scheduling model. The optimization focuses on the user charging cost and load fluctuation, with the non-dominated sorting genetic algorithm II utilized to obtain the solutions. The results demonstrate that the proposed strategy effectively addresses the matching issue between the EV user response potential and optimal scheduling modes without compromising the normal use of EVs by users. This classification approach facilitates the easier acceptance of scheduling tasks by participating users, leading to optimized outcomes that better meet practical requirements.

Suggested Citation

  • Aixin Yang & Guiqing Zhang & Chenlu Tian & Wei Peng & Yechun Liu, 2024. "Charging Behavior Portrait of Electric Vehicle Users Based on Fuzzy C-Means Clustering Algorithm," Energies, MDPI, vol. 17(7), pages 1-26, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1651-:d:1366767
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/7/1651/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/7/1651/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiong, Yingqi & Wang, Bin & Chu, Chi-cheng & Gadh, Rajit, 2018. "Vehicle grid integration for demand response with mixture user model and decentralized optimization," Applied Energy, Elsevier, vol. 231(C), pages 481-493.
    2. Zhou, Yue & Wen, Ruoxi & Wang, Hewu & Cai, Hua, 2020. "Optimal battery electric vehicles range: A study considering heterogeneous travel patterns, charging behaviors, and access to charging infrastructure," Energy, Elsevier, vol. 197(C).
    3. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).
    4. Meng, Jian & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Yu, Xiaodan & Qu, Bo, 2016. "Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system," Applied Energy, Elsevier, vol. 162(C), pages 966-979.
    5. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(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. Dariusz Bober & Piotr Miller & Paweł Pijarski & Bartłomiej Mroczek, 2024. "Sustainable Charging of Electric Transportation Based on Power Modes Model—A Practical Case of an Integrated Factory Grid with RES," Sustainability, MDPI, vol. 17(1), pages 1-28, December.

    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. Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
    2. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    3. Ren, Yilong & Lan, Zhengxing & Yu, Haiyang & Jiao, Gangxin, 2022. "Analysis and prediction of charging behaviors for private battery electric vehicles with regular commuting: A case study in Beijing," Energy, Elsevier, vol. 253(C).
    4. Colmenar-Santos, Antonio & Muñoz-Gómez, Antonio-Miguel & Rosales-Asensio, Enrique & López-Rey, África, 2019. "Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario," Energy, Elsevier, vol. 183(C), pages 61-74.
    5. Kandpal, Bakul & Pareek, Parikshit & Verma, Ashu, 2022. "A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid," Energy, Elsevier, vol. 249(C).
    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. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    8. Ahmad Almaghrebi & Kevin James & Fares Al Juheshi & Mahmoud Alahmad, 2024. "Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling," Energies, MDPI, vol. 17(4), pages 1-20, February.
    9. Müller, Mathias & Blume, Yannic & Reinhard, Janis, 2022. "Impact of behind-the-meter optimised bidirectional electric vehicles on the distribution grid load," Energy, Elsevier, vol. 255(C).
    10. Muhssin, Mazin T. & Cipcigan, Liana M. & Sami, Saif Sabah & Obaid, Zeyad Assi, 2018. "Potential of demand side response aggregation for the stabilization of the grids frequency," Applied Energy, Elsevier, vol. 220(C), pages 643-656.
    11. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
    12. Mousavizade, Mirsaeed & Bai, Feifei & Garmabdari, Rasoul & Sanjari, Mohammad & Taghizadeh, Foad & Mahmoudian, Ali & Lu, Junwei, 2023. "Adaptive control of V2Gs in islanded microgrids incorporating EV owner expectations," Applied Energy, Elsevier, vol. 341(C).
    13. Freitas Gomes, Icaro Silvestre & Perez, Yannick & Suomalainen, Emilia, 2020. "Coupling small batteries and PV generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 126(C).
    14. Iwona Krzyżewska & Katarzyna Chruzik, 2023. "Maintenance and Exploitation of Electric, Hybrid, and Internal Combustion Vehicles," Energies, MDPI, vol. 16(23), pages 1-22, November.
    15. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    16. Andrea Di Martino & Seyed Mahdi Miraftabzadeh & Michela Longo, 2022. "Strategies for the Modelisation of Electric Vehicle Energy Consumption: A Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    17. Katarzyna Markowska & Agnieszka Sękala & Kinga Stecuła & Tomasz Kawka & Kirill Sirovitskiy & Oksana Pankova & Nataliia Vnukova & Mikhail Shulyak & Serhii Kharchenko & Taras Shchur & Ewa Siudyka, 2023. "Comparison of the Sustainability and Economic Efficiency of an Electric Car and an Aircraft—A Case Study," Sustainability, MDPI, vol. 15(2), pages 1-11, January.
    18. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf_v1, Center for Open Science.
    19. Jia, Hongjie & Li, Xiaomeng & Mu, Yunfei & Xu, Chen & Jiang, Yilang & Yu, Xiaodan & Wu, Jianzhong & Dong, Chaoyu, 2018. "Coordinated control for EV aggregators and power plants in frequency regulation considering time-varying delays," Applied Energy, Elsevier, vol. 210(C), pages 1363-1376.
    20. Yuxuan Wang & Bingxu Zhang & Chenyang Li & Yongzhang Huang, 2022. "Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility," Energies, MDPI, vol. 15(8), pages 1-22, April.

    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:17:y:2024:i:7:p:1651-:d:1366767. 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.