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Agent-Based Modelling of Charging Behaviour of Electric Vehicle Drivers

Author

Listed:
  • Mart van der Kam
  • Annemijn Peters
  • Wilfried van Sark
  • Floor Alkemade

Abstract

The combination of electric vehicles (EVs) and intermittent renewable energy sources has received increasing attention over the last few years. Not only does charging electric vehicles with renewable energy realize their true potential as a clean mode of transport, charging electric vehicles at times of peaks in renewable energy production can help large scale integration of renewable energy in the existing energy infrastructure. We present an agent-based model that investigates the potential contribution of this combination. More specifically, we investigate the potential effects of different kinds of policy interventions on aggregate EV charging patterns. The policy interventions include financial incentives, automated smart charging, information campaigns and social charging. We investigate how well the resulting charging patterns are aligned with renewable energy production and how much they affect user satisfaction of EV drivers. Where possible, we integrate empirical data in our model, to ensure realistic scenarios. We use recent theory from environmental psychology to determine agent behaviour, contrary to earlier simulation models, which have focused only on technical and financial considerations. Based on our simulation results, we articulate some policy recommendations. Furthermore, we point to future research directions for environmental psychology scholars and modelers who want to use theory to inform simulation models of energy systems.

Suggested Citation

  • 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.
    • Handle: RePEc:jas:jasssj:2018-123-3
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    References listed on IDEAS

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    1. Claessen, F.N. & Claessens, B. & Hommelberg, M.P.F. & Molderink, A. & Bakker, V. & Toersche, H.A. & van den Broek, M.A., 2014. "Comparative analysis of tertiary control systems for smart grids using the Flex Street model," Renewable Energy, Elsevier, vol. 69(C), pages 260-270.
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    4. Marc Dijk & René Kemp & Pieter Valkering, 2013. "Incorporating social context and co-evolution in an innovation diffusion model—with an application to cleaner vehicles," Journal of Evolutionary Economics, Springer, vol. 23(2), pages 295-329, April.
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    Citations

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    Cited by:

    1. Sahat Hutajulu & Wawan Dhewanto & Eko Agus Prasetio, 2021. "An Agent-Based Model for 5G Technology Diffusion in Urban Societies: Simulating Two Development Scenarios," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    2. 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).
    3. 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.
    4. Lagomarsino, Maria & van der Kam, Mart & Parra, David & Hahnel, Ulf J.J., 2022. "Do I need to charge right now? Tailored choice architecture design can increase preferences for electric vehicle smart charging," Energy Policy, Elsevier, vol. 162(C).
    5. 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.
    6. Mehdizadeh, Milad & Nordfjaern, Trond & Klöckner, Christian A., 2022. "A systematic review of the agent-based modelling/simulation paradigm in mobility transition," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. 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.
    8. Williams, B. & Bishop, D. & Hooper, G. & Chase, J.G., 2024. "Driving change: Electric vehicle charging behavior and peak loading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    9. Vandet, Christian Anker & Rich, Jeppe, 2023. "Optimal placement and sizing of charging infrastructure for EVs under information-sharing," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    10. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.

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