IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p4961-d613695.html
   My bibliography  Save this article

Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data

Author

Listed:
  • Vincent Barthel

    (Energy Informatics, Computer Science 7, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany)

  • Jonas Schlund

    (Energy Informatics, Computer Science 7, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany)

  • Philipp Landes

    (The Mobility House, 81669 Munich, Germany)

  • Veronika Brandmeier

    (The Mobility House, 81669 Munich, Germany)

  • Marco Pruckner

    (Energy Informatics, Computer Science 7, Friedrich-Alexander-University Erlangen-Nürnberg, 91058 Erlangen, Germany)

Abstract

A successful transformation of the energy and transportation sector is one of the main targets for our society today. Battery electric vehicles can play a key role in future renewable-based energy supply systems because of their ability to store electrical power. Additionally, they provide significant charging flexibility due to the long parking durations. In this paper, we provide insights into the temporal and power-specific flexibility behavior of three different vehicle fleets. These fleets are pool vehicles of office employees, a public authority, and a logistics company. Several parameters, such as the average charging power per charging event or the average plug-in duration per charging event, are discussed. Additionally, we investigate different charging rates and their impact on the temporal flexibility of the charging events. The data analysis shows that the logistics site has the most homogeneous charging profile as well as high charging flexibility, in contrast to the office and public agency site. The results are of significant importance for future applications in the field of smart charging and ancillary services provision.

Suggested Citation

  • Vincent Barthel & Jonas Schlund & Philipp Landes & Veronika Brandmeier & Marco Pruckner, 2021. "Analyzing the Charging Flexibility Potential of Different Electric Vehicle Fleets Using Real-World Charging Data," Energies, MDPI, vol. 14(16), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4961-:d:613695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/4961/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/4961/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Martin Spitzer & Jonas Schlund & Elpiniki Apostolaki-Iosifidou & Marco Pruckner, 2019. "Optimized Integration of Electric Vehicles in Low Voltage Distribution Grids," Energies, MDPI, vol. 12(21), pages 1-19, October.
    2. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    3. Manu Lahariya & Dries F. Benoit & Chris Develder, 2020. "Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data," Energies, MDPI, vol. 13(16), pages 1-18, August.
    4. 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).
    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. Meng, Yihao & Zou, Yuan & Ji, Chengda & Zhai, Jianyang & Zhang, Xudong & Zhang, Zhaolong, 2024. "Data-driven electric vehicle usage and charging analysis of logistics vehicle in Shenzhen, China," Energy, Elsevier, vol. 307(C).
    2. Afentoulis, Konstantinos D. & Bampos, Zafeirios N. & Vagropoulos, Stylianos I. & Keranidis, Stratos D. & Biskas, Pantelis N., 2022. "Smart charging business model framework for electric vehicle aggregators," Applied Energy, Elsevier, vol. 328(C).
    3. Kreft, Markus & Brudermueller, Tobias & Fleisch, Elgar & Staake, Thorsten, 2024. "Predictability of electric vehicle charging: Explaining extensive user behavior-specific heterogeneity," Applied Energy, Elsevier, vol. 370(C).
    4. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
    5. Amra Jahic & Felix Heider & Maik Plenz & Detlef Schulz, 2022. "Flexibility Quantification and the Potential for Its Usage in the Case of Electric Bus Depots with Unidirectional Charging," Energies, MDPI, vol. 15(10), pages 1-18, 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. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    2. Murat Akil & Emrah Dokur & Ramazan Bayindir, 2021. "The SOC Based Dynamic Charging Coordination of EVs in the PV-Penetrated Distribution Network Using Real-World Data," Energies, MDPI, vol. 14(24), pages 1-19, December.
    3. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
    4. 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).
    5. Soomin Woo & Zhe Fu & Elpiniki Apostolaki-Iosifidou & Timothy E. Lipman, 2021. "Economic and Environmental Benefits for Electricity Grids from Spatiotemporal Optimization of Electric Vehicle Charging," Energies, MDPI, vol. 14(24), pages 1-22, December.
    6. Verónica Anadón Martínez & Andreas Sumper, 2023. "Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review," Energies, MDPI, vol. 16(14), pages 1-41, July.
    7. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    8. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    9. 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).
    10. Syed Muhammad Ahsan & Hassan Abbas Khan & Sarmad Sohaib & Anas M. Hashmi, 2023. "Optimized Power Dispatch for Smart Building and Electric Vehicles with V2V, V2B and V2G Operations," Energies, MDPI, vol. 16(13), pages 1-15, June.
    11. Tommy Lundgren & Mattias Vesterberg, 2024. "Efficiency in electricity distribution in Sweden and the effects of small-scale generation, electric vehicles and dynamic tariffs," Journal of Productivity Analysis, Springer, vol. 62(2), pages 121-137, October.
    12. Milan Straka & Pasquale De Falco & Gabriella Ferruzzi & Daniela Proto & Gijs van der Poel & Shahab Khormali & v{L}ubov{s} Buzna, 2019. "Predicting popularity of EV charging infrastructure from GIS data," Papers 1910.02498, arXiv.org.
    13. Bruno Pinto & Filipe Barata & Constantino Soares & Carla Viveiros, 2020. "Fleet Transition from Combustion to Electric Vehicles: A Case Study in a Portuguese Business Campus," Energies, MDPI, vol. 13(5), pages 1-24, March.
    14. Tuchnitz, Felix & Ebell, Niklas & Schlund, Jonas & Pruckner, Marco, 2021. "Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning," Applied Energy, Elsevier, vol. 285(C).
    15. Lixing Chen & Xueliang Huang & Hong Zhang, 2020. "Modeling the Charging Behaviors for Electric Vehicles Based on Ternary Symmetric Kernel Density Estimation," Energies, MDPI, vol. 13(7), pages 1-17, March.
    16. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M., 2016. "A multi-agent based scheduling algorithm for adaptive electric vehicles charging," Applied Energy, Elsevier, vol. 177(C), pages 354-365.
    17. Wei Wei & Ming Cao & Qianling Jiang & Sheng-Jung Ou & Hong Zou, 2020. "What Influences Chinese Consumers’ Adoption of Battery Electric Vehicles? A Preliminary Study Based on Factor Analysis," Energies, MDPI, vol. 13(5), pages 1-15, February.
    18. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    19. Josh Eichman & Marc Torrecillas Castelló & Cristina Corchero, 2022. "Reviewing and Exploring the Qualitative Impacts That Different Market and Regulatory Measures Can Have on Encouraging Energy Communities Based on Their Organizational Structure," Energies, MDPI, vol. 15(6), pages 1-19, March.
    20. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.

    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:16:p:4961-:d:613695. 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.