IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i5p1240-d329854.html
   My bibliography  Save this article

Plug-in Electric Vehicles for Grid Services Provision: Proposing an Operational Characterization Procedure for V2G Systems

Author

Listed:
  • Ângelo Casaleiro

    (Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Campo Grande 016, 1749-016 Lisboa, Portugal)

  • Rodrigo Amaro e Silva

    (Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Campo Grande 016, 1749-016 Lisboa, Portugal)

  • João Serra

    (Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Campo Grande 016, 1749-016 Lisboa, Portugal)

Abstract

Plug-in electric vehicles (PEVs) are expected to play a role as power grid ancillary service providers through vehicle-to-grid (V2G) chargers, enabling higher levels of renewable electricity penetration. However, to fully exploit the storage capacity of PEVs and fast responsiveness, it is crucial to understand their operational characteristics. This work proposes a characterization procedure for V2G systems providing grid services. It extends the existing literature on response time, AC/DC conversion and reactive power assessment. Illustrative results were obtained by implementing the procedure using a Nissan Leaf battery electric vehicle (BEV) connected to a remotely operated commercial V2G CHAdeMO charger. The V2G system was characterized as having a relative inaccuracy and variability of response inferior to 3% and 0.4%, respectively. Its average communication and ramping times are 2.37 s and 0.26 s/kW, respectively. Its conversion efficiency and power factor both showed degradation in the power values below 50% of the charger’s nominal power. Moreover, the proposed visualizations revealed that: i) the V2G system implements power requests for the DC power flow; ii) the power factor control algorithm was nonoperational; and iii) the acquired data can leverage statistical models that describe the operation of V2G systems (which is of extreme value for researchers and operators).

Suggested Citation

  • Ângelo Casaleiro & Rodrigo Amaro e Silva & João Serra, 2020. "Plug-in Electric Vehicles for Grid Services Provision: Proposing an Operational Characterization Procedure for V2G Systems," Energies, MDPI, vol. 13(5), pages 1-12, March.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1240-:d:329854
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/5/1240/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/5/1240/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    2. Marisca Zweistra & Stan Janssen & Frank Geerts, 2020. "Large Scale Smart Charging of Electric Vehicles in Practice," Energies, MDPI, vol. 13(2), pages 1-13, January.
    3. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(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. Josip Vasilj & Damir Jakus & Petar Sarajcev, 2020. "Virtual Storage-Based Model for Estimation of Economic Benefits of Electric Vehicles in Renewable Portfolios," Energies, MDPI, vol. 13(9), pages 1-19, May.

    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. 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).
    2. Gönül, Ömer & Duman, A. Can & Güler, Önder, 2021. "Electric vehicles and charging infrastructure in Turkey: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    3. Ma, Shao-Chao & Yi, Bo-Wen & Fan, Ying, 2022. "Research on the valley-filling pricing for EV charging considering renewable power generation," Energy Economics, Elsevier, vol. 106(C).
    4. Gschwendtner, Christine & Sinsel, Simon R. & Stephan, Annegret, 2021. "Vehicle-to-X (V2X) implementation: An overview of predominate trial configurations and technical, social and regulatory challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    5. Will, Christian & Lehmann, Nico & Baumgartner, Nora & Feurer, Sven & Jochem, Patrick & Fichtner, Wolf, 2022. "Consumer understanding and evaluation of carbon-neutral electric vehicle charging services," Applied Energy, Elsevier, vol. 313(C).
    6. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. 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).
    8. Md. Mosaraf Hossain Khan & Amran Hossain & Aasim Ullah & Molla Shahadat Hossain Lipu & S. M. Shahnewaz Siddiquee & M. Shafiul Alam & Taskin Jamal & Hafiz Ahmed, 2021. "Integration of Large-Scale Electric Vehicles into Utility Grid: An Efficient Approach for Impact Analysis and Power Quality Assessment," Sustainability, MDPI, vol. 13(19), pages 1-18, October.
    9. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    10. Tuğba Yeğin & Muhammad Ikram, 2022. "Analysis of Consumers’ Electric Vehicle Purchase Intentions: An Expansion of the Theory of Planned Behavior," Sustainability, MDPI, vol. 14(19), pages 1-27, September.
    11. Parlikar, Anupam & Schott, Maximilian & Godse, Ketaki & Kucevic, Daniel & Jossen, Andreas & Hesse, Holger, 2023. "High-power electric vehicle charging: Low-carbon grid integration pathways with stationary lithium-ion battery systems and renewable generation," Applied Energy, Elsevier, vol. 333(C).
    12. Asaad Mohammad & Ramon Zamora & Tek Tjing Lie, 2020. "Integration of Electric Vehicles in the Distribution Network: A Review of PV Based Electric Vehicle Modelling," Energies, MDPI, vol. 13(17), pages 1-20, September.
    13. Sajjad Haider & Peter Schegner, 2020. "Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid," Energies, MDPI, vol. 13(22), pages 1-19, November.
    14. Tan, Kang Miao & Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Mansor, Muhamad & Teh, Jiashen & Guerrero, Josep M., 2023. "Factors influencing global transportation electrification: Comparative analysis of electric and internal combustion engine vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    15. Shi You & Junjie Hu & Charalampos Ziras, 2016. "An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †," Energies, MDPI, vol. 9(11), pages 1-18, November.
    16. Wang, Qi & Huang, Chunyi & Wang, Chengmin & Li, Kangping & Xie, Ning, 2024. "Joint optimization of bidding and pricing strategy for electric vehicle aggregator considering multi-agent interactions," Applied Energy, Elsevier, vol. 360(C).
    17. Yu, Haiquan & Zhou, Jianxin & Si, Fengqi & Nord, Lars O., 2022. "Combined heat and power dynamic economic dispatch considering field operational characteristics of natural gas combined cycle plants," Energy, Elsevier, vol. 244(PA).
    18. Kim, Hyunjung & Kim, Dae-Wook & Kim, Man-Keun, 2022. "Economics of charging infrastructure for electric vehicles in Korea," Energy Policy, Elsevier, vol. 164(C).
    19. Zhenya Ji & Xueliang Huang & Changfu Xu & Houtao Sun, 2016. "Accelerated Model Predictive Control for Electric Vehicle Integrated Microgrid Energy Management: A Hybrid Robust and Stochastic Approach," Energies, MDPI, vol. 9(11), pages 1-18, November.
    20. Kazmi, Hussain & Munné-Collado, Íngrid & Mehmood, Fahad & Syed, Tahir Abbas & Driesen, Johan, 2021. "Towards data-driven energy communities: A review of open-source datasets, models and tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(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:13:y:2020:i:5:p:1240-:d:329854. 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.