IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i13p4748-d850863.html
   My bibliography  Save this article

Probabilistic Load Profile Model for Public Charging Infrastructure to Evaluate the Grid Load

Author

Listed:
  • Andreas Weiß

    (Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 Munich, Germany
    School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich, Germany)

  • Florian Biedenbach

    (Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 Munich, Germany)

  • Mathias Müller

    (Forschungsstelle für Energiewirtschaft (FfE) e.V., 80995 Munich, Germany
    School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich, Germany)

Abstract

The shift toward electric mobility in Germany is a major component of the German climate protection program. In this context, public charging is growing in importance, especially in high-density urban areas, which causes an additional load on the distribution grid. In order to evaluate this impact and prevent possible overloads, realistic models are required. Methods for implementing such models and their application in the context of grid load are research topics that are only minorly addressed in the literature. This paper aims to demonstrate the entire process chain from the selection of a modelling method to the implementation and application of the model within a case study. Applying a stochastic approach, charging points are modelled via probabilities to determine the start of charging, plug-in duration, and charged energy. Subsequently, load profiles are calculated, integrated into an energy system model and applied in order to analyze the effects of a high density of public charging points on the urban distribution grid. The case study highlights a possible application of the implemented probabilistic load profile model, but also reveals its limitations. The primary results of this paper are the identification and evaluation of relevant criteria for modelling the load profiles of public charging points as well as the demonstration of the model and its comparison to real charging processes. By publishing the determined probabilities and the model for calculating the charging load profiles, a comprehensive tool is provided.

Suggested Citation

  • Andreas Weiß & Florian Biedenbach & Mathias Müller, 2022. "Probabilistic Load Profile Model for Public Charging Infrastructure to Evaluate the Grid Load," Energies, MDPI, vol. 15(13), pages 1-28, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4748-:d:850863
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/13/4748/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/13/4748/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mart van der Kam & Annemijn Peters & Wilfried van Sark & Floor Alkemade, 2019. "Agent-Based Modelling of Charging Behaviour of Electric Vehicle Drivers," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-7.
    2. Mathias Müller & Florian Biedenbach & Janis Reinhard, 2020. "Development of an Integrated Simulation Model for Load and Mobility Profiles of Private Households," Energies, MDPI, vol. 13(15), pages 1-33, July.
    3. Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    Full references (including those not matched with items on IDEAS)

    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. Michel Noussan & Matteo Jarre, 2021. "Assessing Commuting Energy and Emissions Savings through Remote Working and Carpooling: Lessons from an Italian Region," Energies, MDPI, vol. 14(21), pages 1-19, November.
    2. 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.
    3. Julia Vopava & Christian Koczwara & Anna Traupmann & Thomas Kienberger, 2019. "Investigating the Impact of E-Mobility on the Electrical Power Grid Using a Simplified Grid Modelling Approach," Energies, MDPI, vol. 13(1), pages 1-23, December.
    4. 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).
    5. Visaria, Anant Atul & Jensen, Anders Fjendbo & Thorhauge, Mikkel & Mabit, Stefan Eriksen, 2022. "User preferences for EV charging, pricing schemes, and charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 120-143.
    6. Muhammad Naveed Iqbal & Lauri Kütt & Matti Lehtonen & Robert John Millar & Verner Püvi & Anton Rassõlkin & Galina L. Demidova, 2021. "Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles," Sustainability, MDPI, vol. 13(3), pages 1-14, February.
    7. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    8. Monika Topel & Josefine Grundius, 2020. "Load Management Strategies to Increase Electric Vehicle Penetration—Case Study on a Local Distribution Network in Stockholm," Energies, MDPI, vol. 13(18), pages 1-16, September.
    9. Francesco Mancini & Benedetto Nastasi, 2020. "Solar Energy Data Analytics: PV Deployment and Land Use," Energies, MDPI, vol. 13(2), pages 1-18, January.
    10. Khaki, Behnam & Chu, Chicheng & Gadh, Rajit, 2019. "Hierarchical distributed framework for EV charging scheduling using exchange problem," Applied Energy, Elsevier, vol. 241(C), pages 461-471.
    11. Pampa Sinha & Kaushik Paul & Sanchari Deb & Sulabh Sachan, 2023. "Comprehensive Review Based on the Impact of Integrating Electric Vehicle and Renewable Energy Sources to the Grid," Energies, MDPI, vol. 16(6), pages 1-39, March.
    12. Kern, Timo & Dossow, Patrick & Morlock, Elena, 2022. "Revenue opportunities by integrating combined vehicle-to-home and vehicle-to-grid applications in smart homes," Applied Energy, Elsevier, vol. 307(C).
    13. Albert Hiesl & Jasmine Ramsebner & Reinhard Haas, 2021. "Modelling Stochastic Electricity Demand of Electric Vehicles Based on Traffic Surveys—The Case of Austria," Energies, MDPI, vol. 14(6), pages 1-19, March.
    14. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    15. Cruz, José Ramón Serrano & López, J. Javier & Climent, Héctor & Gómez-Vilanova, Alejandro, 2023. "Method for turbocharging and supercharging 2-stroke engines, applied to an opposed-piston new concept for hybrid powertrains," Applied Energy, Elsevier, vol. 351(C).
    16. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    17. Kacperski, Celina & Ulloa, Roberto & Klingert, Sonja & Kirpes, Benedikt & Kutzner, Florian, 2022. "Impact of incentives for greener battery electric vehicle charging – A field experiment," Energy Policy, Elsevier, vol. 161(C).
    18. Helmus, Jurjen R. & Lees, Michael H. & van den Hoed, Robert, 2022. "A validated agent-based model for stress testing charging infrastructure utilization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 237-262.
    19. Steinbach, Sarah A. & Blaschke, Maximilian J., 2024. "Enabling electric mobility: Can photovoltaic and home battery systems significantly reduce grid reinforcement costs?," Applied Energy, Elsevier, vol. 375(C).
    20. Maciej Neugebauer & Adam Żebrowski & Ogulcan Esmer, 2022. "Cumulative Emissions of CO 2 for Electric and Combustion Cars: A Case Study on Specific Models," Energies, MDPI, vol. 15(7), pages 1-17, 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:15:y:2022:i:13:p:4748-:d:850863. 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.