IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5281-d803597.html
   My bibliography  Save this article

A Time-Varying Potential Evaluation Method for Electric Vehicle Group Demand Response Driven by Small Sample Data

Author

Listed:
  • Sheng Ding

    (State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited, Wuhan 430074, China)

  • Chengmei Xu

    (State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited, Wuhan 430074, China)

  • Yao Rao

    (State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Company Limited, Wuhan 430074, China)

  • Zhaofang Song

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Wangwang Yang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zexu Chen

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zitong Zhang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Electric vehicle (EV) loads are playing an increasingly important role in improving the flexibility of power grid operation. The prerequisite for EV loads to participate in demand response (DR) is that the DR regulation strategy and corresponding DR potential must be accurately analyzed and evaluated. However, due to the uncertainty and differences in travel and charging behavior, DR potentials of EVs exhibit randomness and differ in time and space. In addition, it is difficult to obtain refined travel data and charging load data of large-scale EVs. Accordingly, this paper focuses on how to consider the various influencing factors of potential, and realize the quantitative evaluation of DR time-varying potential of an EV group based on small sample data. First, a travel activity model of the EV is established. Based on the actual travel data, the probability distributions of the key parameters of the travel model are obtained by kernel density estimation and probability statistical fitting. Then, combined with the charging behavior model, and based on Monte Carlo simulation, the load curve of the EV in a residential area is predicted. Considering the travel need of the EV, the peak-shaving potential, vehicle-to-grid discharge potential, and valley-filling potential of the EV under different DR strategies are calculated and analyzed, and the time-varying characteristics of the potential are analyzed. Finally, a case study is carried out with the actual data. The results show that the DR time-varying potential under different time periods and control strategies can be effectively evaluated. The maximum peak-shaving potential of 1000 EV aggregates is 2.7 MW, and the minimum is 0.25 MW. The maximum valley-filling potential is 2.1 MW, and the minimum is 0.3 MW. The research results can provide effective guidance for EVs to participate in day-ahead scheduling, and for the screening of target EVs.

Suggested Citation

  • Sheng Ding & Chengmei Xu & Yao Rao & Zhaofang Song & Wangwang Yang & Zexu Chen & Zitong Zhang, 2022. "A Time-Varying Potential Evaluation Method for Electric Vehicle Group Demand Response Driven by Small Sample Data," Sustainability, MDPI, vol. 14(9), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5281-:d:803597
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5281/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/5281/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lidan Chen & Yao Zhang & Antonio Figueiredo, 2019. "Spatio-Temporal Model for Evaluating Demand Response Potential of Electric Vehicles in Power-Traffic Network," Energies, MDPI, vol. 12(10), pages 1-20, May.
    2. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    3. Arshad Mohammad & Mohd Zuhaib & Imtiaz Ashraf & Marwan Alsultan & Shafiq Ahmad & Adil Sarwar & Mali Abdollahian, 2021. "Integration of Electric Vehicles and Energy Storage System in Home Energy Management System with Home to Grid Capability," Energies, MDPI, vol. 14(24), pages 1-27, December.
    4. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    5. Ziwen Ling & Christopher R. Cherry & Yi Wen, 2021. "Determining the Factors That Influence Electric Vehicle Adoption: A Stated Preference Survey Study in Beijing, China," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    6. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(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. Anna Auza & Ehsan Asadi & Behrang Chenari & Manuel Gameiro da Silva, 2023. "A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles," Energies, MDPI, vol. 16(13), pages 1-25, June.

    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. Wu, Jiabin & Li, Qihang & Bie, Yiming & Zhou, Wei, 2024. "Location-routing optimization problem for electric vehicle charging stations in an uncertain transportation network: An adaptive co-evolutionary clustering algorithm," Energy, Elsevier, vol. 304(C).
    2. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    3. Iogansen, Xiatian & Wang, Kailai & Bunch, David & Matson, Grant & Circella, Giovanni, 2023. "Deciphering the factors associated with adoption of alternative fuel vehicles in California: An investigation of latent attitudes, socio-demographics, and neighborhood effects," Transportation Research Part A: Policy and Practice, Elsevier, vol. 168(C).
    4. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    5. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    6. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    7. Alexander Wahl & Christoph Wellmann & Björn Krautwig & Patrick Manns & Bicheng Chen & Christof Schernus & Jakob Andert, 2022. "Efficiency Increase through Model Predictive Thermal Control of Electric Vehicle Powertrains," Energies, MDPI, vol. 15(4), pages 1-21, February.
    8. 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).
    9. Abdulaziz Alshammari & Rakan C. Chabaan, 2023. "Metaheruistic Optimization Based Ensemble Machine Learning Model for Designing Detection Coil with Prediction of Electric Vehicle Charging Time," Sustainability, MDPI, vol. 15(8), pages 1-17, April.
    10. Huachun Han & Huiyu Miu & Shukang Lv & Xiaodong Yuan & Yi Pan & Fei Zeng, 2024. "Fast Charging Guidance and Pricing Strategy Considering Different Types of Electric Vehicle Users’ Willingness to Charge," Energies, MDPI, vol. 17(18), pages 1-21, September.
    11. Aliakbari Sani, Sajad & Bahn, Olivier & Delage, Erick, 2022. "Affine decision rule approximation to address demand response uncertainty in smart Grids’ capacity planning," European Journal of Operational Research, Elsevier, vol. 303(1), pages 438-455.
    12. Yin, Linfei & Zhang, Bin, 2023. "Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems," Applied Energy, Elsevier, vol. 330(PA).
    13. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    14. Thanh Tung Ha & Thanh Chuong Nguyen & Sy Sua Tu & Minh Hieu Nguyen, 2023. "Investigation of Influential Factors of Intention to Adopt Electric Vehicles for Motorcyclists in Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-16, May.
    15. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    16. Hafiz Abdul Muqeet & Rehan Liaqat & Mohsin Jamil & Asharf Ali Khan, 2023. "A State-of-the-Art Review of Smart Energy Systems and Their Management in a Smart Grid Environment," Energies, MDPI, vol. 16(1), pages 1-23, January.
    17. Secinaro, Silvana & Calandra, Davide & Lanzalonga, Federico & Ferraris, Alberto, 2022. "Electric vehicles’ consumer behaviours: Mapping the field and providing a research agenda," Journal of Business Research, Elsevier, vol. 150(C), pages 399-416.
    18. Carli, Raffaele & Dotoli, Mariagrazia & Jantzen, Jan & Kristensen, Michael & Ben Othman, Sarah, 2020. "Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø," Energy, Elsevier, vol. 198(C).
    19. Amro M Elshurafa & Abdel Rahman Muhsen, 2019. "The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    20. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(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:jsusta:v:14:y:2022:i:9:p:5281-:d:803597. 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.