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Modeling the Impact of Electric Vehicle Charging Infrastructure on Regional Energy Systems: Fields of Action for an Improved e-Mobility Integration

Author

Listed:
  • Dominik Husarek

    (Technology, Research in Energy and Electronics, Siemens AG, 81739 Munich, Germany
    Technology and Economics of Multimodal Energy Systems, Technical University of Darmstadt, 64289 Darmstadt, Germany)

  • Vjekoslav Salapic

    (Technology, Research in Energy and Electronics, Siemens AG, 81739 Munich, Germany)

  • Simon Paulus

    (Technology, Research in Energy and Electronics, Siemens AG, 81739 Munich, Germany)

  • Michael Metzger

    (Technology, Research in Energy and Electronics, Siemens AG, 81739 Munich, Germany)

  • Stefan Niessen

    (Technology, Research in Energy and Electronics, Siemens AG, 81739 Munich, Germany
    Technology and Economics of Multimodal Energy Systems, Technical University of Darmstadt, 64289 Darmstadt, Germany)

Abstract

Since e-Mobility is on the rise worldwide, large charging infrastructure networks are required to satisfy the upcoming charging demand. Planning these networks not only involves different objectives from grid operators, drivers and Charging Station (CS) operators alike but it also underlies spatial and temporal uncertainties of the upcoming charging demand. Here, we aim at showing these uncertainties and assess different levers to enable the integration of e-Mobility. Therefore, we introduce an Agent-based model assessing regional charging demand and infrastructure networks with the interactions between charging infrastructure and electric vehicles. A global sensitivity analysis is applied to derive general guidelines for integrating e-Mobility effectively within a region by considering the grid impact, the economic viability and the Service Quality of the deployed Charging Infrastructure (SQCI). We show that an improved macro-economic framework should enable infrastructure investments across different types of locations such as public, highway and work to utilize cross-locational charging peak reduction effects. Since the height of the residential charging peak depends up to 18% on public charger availability, supporting public charging infrastructure investments especially in highly utilized power grid regions is recommended.

Suggested Citation

  • Dominik Husarek & Vjekoslav Salapic & Simon Paulus & Michael Metzger & Stefan Niessen, 2021. "Modeling the Impact of Electric Vehicle Charging Infrastructure on Regional Energy Systems: Fields of Action for an Improved e-Mobility Integration," Energies, MDPI, vol. 14(23), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7992-:d:691617
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    References listed on IDEAS

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    1. Azhar Ul-Haq & Marium Azhar & Yousef Mahmoud & Aqib Perwaiz & Essam A. Al-Ammar, 2017. "Probabilistic Modeling of Electric Vehicle Charging Pattern Associated with Residential Load for Voltage Unbalance Assessment," Energies, MDPI, vol. 10(9), pages 1-18, September.
    2. Guus ten Broeke & George van Voorn & Arend Ligtenberg, 2016. "Which Sensitivity Analysis Method Should I Use for My Agent-Based Model?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-5.
    3. Eckard Helmers & Johannes Dietz & Martin Weiss, 2020. "Sensitivity Analysis in the Life-Cycle Assessment of Electric vs. Combustion Engine Cars under Approximate Real-World Conditions," Sustainability, MDPI, vol. 12(3), pages 1-31, February.
    4. Semen Uimonen & Matti Lehtonen, 2020. "Simulation of Electric Vehicle Charging Stations Load Profiles in Office Buildings Based on Occupancy Data," Energies, MDPI, vol. 13(21), pages 1-16, October.
    5. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    6. 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.
    7. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    8. Harbrecht, Alexander & McKenna, Russell & Fischer, David & Fichtner, Wolf, 2018. "Behavior-oriented modeling of electric vehicle load profiles: A stochastic simulation model considering different household characteristics, charging decisions and locations," Working Paper Series in Production and Energy 29, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    9. Jan C. Thiele & Winfried Kurth & Volker Grimm, 2014. "Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and 'R'," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-11.
    10. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeswar Mahanta, 2018. "Review of recent trends in charging infrastructure planning for electric vehicles," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    11. Liu, Jian, 2012. "Electric vehicle charging infrastructure assignment and power grid impacts assessment in Beijing," Energy Policy, Elsevier, vol. 51(C), pages 544-557.
    12. Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gareth & Sun, Zhanli & Parker, Dawn C., 2015. "The complexities of agent-based modeling output analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(4).
    13. Kong, Weiwei & Luo, Yugong & Feng, Guixuan & Li, Keqiang & Peng, Huei, 2019. "Optimal location planning method of fast charging station for electric vehicles considering operators, drivers, vehicles, traffic flow and power grid," Energy, Elsevier, vol. 186(C).
    14. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
    15. 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)

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    1. Yuchao Cai & Jie Zhang & Quan Gu & Chenlu Wang, 2024. "An Analytical Framework for Assessing Equity of Access to Public Electric Vehicle Charging Stations: The Case of Shanghai," Sustainability, MDPI, vol. 16(14), pages 1-38, July.
    2. Domenico Tomaselli & Dieter Most & Enkel Sinani & Paul Stursberg & Hans Joerg Heger & Stefan Niessen, 2024. "Leveraging Prosumer Flexibility to Mitigate Grid Congestion in Future Power Distribution Grids," Energies, MDPI, vol. 17(17), pages 1-17, August.

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