IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7015-d665378.html
   My bibliography  Save this article

Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk

Author

Listed:
  • Yilu Wang

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Zixuan Jia

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Jianing Li

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Xiaoping Zhang

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Ray Zhang

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

Abstract

With the global net-zero strategy implementation, decarbonisation of transport by massive deployment of electric vehicles (EVs) has been considered to be an essential solution. However, charging EVs and integration into electricity grids is going to be a fundamental challenge to future electricity systems. Hence, in this situation, how to effectively deploy massive numbers of EVs, and in the meantime what can be developed to deliver vehicle-to-grid (V2G) services, become a fundamental yet interesting tech-economical issues. Furthermore, uncertainty in lack of vehicle availability and EV battery degradation could lead to revenue loss when using EVs as ancillary services aggregators. With such considerations, this paper presents a new optimised V2G aggregator scheduling service that has taken into consideration of a number of risks, including EV availability and battery degradation through conditional value-at-risk. The proposed method for V2G scheduling service, as an independent aggregator, is formulated as a bi-level optimisation problem. The performance of the proposed method is to be evaluated through case studies on the Birmingham International Airport parking lot with onsite renewable generation. Uncertainties of EVs and the differences in weekdays and weekends are also compared.

Suggested Citation

  • Yilu Wang & Zixuan Jia & Jianing Li & Xiaoping Zhang & Ray Zhang, 2021. "Optimal Bi-Level Scheduling Method of Vehicle-to-Grid and Ancillary Services of Aggregators with Conditional Value-at-Risk," Energies, MDPI, vol. 14(21), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7015-:d:665378
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7015/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7015/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Choi, Hyunhong & Shin, Jungwoo & Woo, JongRoul, 2018. "Effect of electricity generation mix on battery electric vehicle adoption and its environmental impact," Energy Policy, Elsevier, vol. 121(C), pages 13-24.
    2. Wu, Jiekang & Wu, Zhijiang & Wu, Fan & Tang, Huiling & Mao, Xiaoming, 2018. "CVaR risk-based optimization framework for renewable energy management in distribution systems with DGs and EVs," Energy, Elsevier, vol. 143(C), pages 323-336.
    3. Hafez, Omar & Bhattacharya, Kankar, 2017. "Optimal design of electric vehicle charging stations considering various energy resources," Renewable Energy, Elsevier, vol. 107(C), pages 576-589.
    4. Triviño-Cabrera, Alicia & Aguado, José A. & Torre, Sebastián de la, 2019. "Joint routing and scheduling for electric vehicles in smart grids with V2G," Energy, Elsevier, vol. 175(C), pages 113-122.
    5. Saad Ullah Khan & Khawaja Khalid Mehmood & Zunaib Maqsood Haider & Muhammad Kashif Rafique & Chul-Hwan Kim, 2018. "A Bi-Level EV Aggregator Coordination Scheme for Load Variance Minimization with Renewable Energy Penetration Adaptability," Energies, MDPI, vol. 11(10), pages 1-28, October.
    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. Mariusz Pyra, 2023. "A Scenario Analysis for the Decarbonisation Process in Poland’s Road Transport Sector," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 411-432.
    2. 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. Kyuho Maeng & Sungmin Ko & Jungwoo Shin & Youngsang Cho, 2020. "How Much Electricity Sharing Will Electric Vehicle Owners Allow from Their Battery? Incorporating Vehicle-to-Grid Technology and Electricity Generation Mix," Energies, MDPI, vol. 13(16), pages 1-25, August.
    2. Yang, Jie & Yu, Fan & Ma, Kai & Yang, Bo & Yue, Zhiyuan, 2024. "Optimal scheduling of electric-hydrogen integrated charging station for new energy vehicles," Renewable Energy, Elsevier, vol. 224(C).
    3. Duan, Ditao & Poursoleiman, Roza, 2021. "Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station," Utilities Policy, Elsevier, vol. 72(C).
    4. Nenming Wang & Guwen Tang, 2022. "A Review on Environmental Efficiency Evaluation of New Energy Vehicles Using Life Cycle Analysis," Sustainability, MDPI, vol. 14(6), pages 1-35, March.
    5. Justin Fraselle & Sabine Louise Limbourg & Laura Vidal, 2021. "Cost and Environmental Impacts of a Mixed Fleet of Vehicles," Sustainability, MDPI, vol. 13(16), pages 1-16, August.
    6. Kim, Junghun & Seung, Hyunchan & Lee, Jongsu & Ahn, Joongha, 2020. "Asymmetric preference and loss aversion for electric vehicles: The reference-dependent choice model capturing different preference directions," Energy Economics, Elsevier, vol. 86(C).
    7. Ming Tang & Jian Wang & Xiaohua Wang, 2020. "Adaptable Source-Grid Planning for High Penetration of Renewable Energy Integrated System," Energies, MDPI, vol. 13(13), pages 1-26, June.
    8. Lei Yao & Chongtao Bai & Hao Fu & Suhua Lou & Yan Fu, 2023. "Optimization of Expressway Microgrid Construction Mode and Capacity Configuration Considering Carbon Trading," Energies, MDPI, vol. 16(18), pages 1-17, September.
    9. Woo, JongRoul & Moon, Sungho & Choi, Hyunhong, 2022. "Economic value and acceptability of advanced solar power systems for multi-unit residential buildings: The case of South Korea," Applied Energy, Elsevier, vol. 324(C).
    10. 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.
    11. Aminu Bugaje & Mathias Ehrenwirth & Christoph Trinkl & Wilfried Zörner, 2021. "Electric Two-Wheeler Vehicle Integration into Rural Off-Grid Photovoltaic System in Kenya," Energies, MDPI, vol. 14(23), pages 1-27, November.
    12. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    13. Mehrjerdi, Hasan & Bornapour, Mosayeb & Hemmati, Reza & Ghiasi, Seyyed Mohammad Sadegh, 2019. "Unified energy management and load control in building equipped with wind-solar-battery incorporating electric and hydrogen vehicles under both connected to the grid and islanding modes," Energy, Elsevier, vol. 168(C), pages 919-930.
    14. Olabi, A.G. & Wilberforce, Tabbi & Abdelkareem, Mohammad Ali, 2021. "Fuel cell application in the automotive industry and future perspective," Energy, Elsevier, vol. 214(C).
    15. Abu Bakar Siddique & Hossam A. Gabbar, 2023. "Adaptive Mixed-Integer Linear Programming-Based Energy Management System of Fast Charging Station with Nuclear–Renewable Hybrid Energy System," Energies, MDPI, vol. 16(2), pages 1-22, January.
    16. Mohd Bilal & Ibrahim Alsaidan & Muhannad Alaraj & Fahad M. Almasoudi & Mohammad Rizwan, 2022. "Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-40, March.
    17. Zhang, Xingxing & Lovati, Marco & Vigna, Ilaria & Widén, Joakim & Han, Mengjie & Gal, Csilla & Feng, Tao, 2018. "A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions," Applied Energy, Elsevier, vol. 230(C), pages 1034-1056.
    18. Ko, Sungmin & Shin, Jungwoo, 2023. "Projection of fuel cell electric vehicle demand reflecting the feedback effects between market conditions and market share affected by spatial factors," Energy Policy, Elsevier, vol. 173(C).
    19. Mittelviefhaus, Moritz & Pareschi, Giacomo & Allan, James & Georges, Gil & Boulouchos, Konstantinos, 2021. "Optimal investment and scheduling of residential multi-energy systems including electric mobility: A cost-effective approach to climate change mitigation," Applied Energy, Elsevier, vol. 301(C).
    20. Mehrjerdi, Hasan & Hemmati, Reza, 2020. "Coordination of vehicle-to-home and renewable capacity resources for energy management in resilience and self-healing building," Renewable Energy, Elsevier, vol. 146(C), pages 568-579.

    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:14:y:2021:i:21:p:7015-:d:665378. 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.