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Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study

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  • Wolbertus, Rick
  • van den Hoed, Robert
  • Kroesen, Maarten
  • Chorus, Caspar

Abstract

On the eve of the large-scale introduction of electric vehicles, policy makers have to decide on how to organise a significant growth in charging infrastructure to meet demand. There is uncertainty about which charging deployment tactic to follow. The main issue is how many of charging stations, of which type, should be installed and where. Early roll-out has been successful in many places, but knowledge on how to plan a large-scale charging network in urban areas is missing. Little is known about return to scale effects, reciprocal effects of charger availability on sales, and the impact of fast charging or more clustered charging hubs on charging preferences of EV owners. This paper explores the effects of various roll-out strategies for charging infrastructure that facilitate the large-scale introduction of EVs, using agent-based simulation. In contrast to previously proposed models, our model is rooted in empirically observed charging patterns from EVs instead of travel patterns of fossil fuelled cars. In addition, the simulation incorporates different user types (inhabitants, visitors, taxis and shared vehicles) to model the diversity of charging behaviours in an urban environment. Different scenarios are explored along the lines of the type of charging infrastructure (level 2, clustered level 2, fast charging) and the intensity of rollout (EV to charging point ratio). The simulation predicts both the success rate of charging attempts and the additional discomfort when searching for a charging station. Results suggest that return to scale and reciprocal effects in charging infrastructure are considerable, resulting in a lower EV to charging station ratio on the longer term.

Suggested Citation

  • Wolbertus, Rick & van den Hoed, Robert & Kroesen, Maarten & Chorus, Caspar, 2021. "Charging infrastructure roll-out strategies for large scale introduction of electric vehicles in urban areas: An agent-based simulation study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 262-285.
    • Handle: RePEc:eee:transa:v:148:y:2021:i:c:p:262-285
    DOI: 10.1016/j.tra.2021.04.010
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    References listed on IDEAS

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    Cited by:

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    3. Jayalakshmi N. Sabhahit & Sanjana Satish Solanke & Vinay Kumar Jadoun & Hasmat Malik & Fausto Pedro García Márquez & Jesús María Pinar-Pérez, 2022. "Contingency Analysis of a Grid Connected EV's for Primary Frequency Control of an Industrial Microgrid Using Efficient Control Scheme," Energies, MDPI, vol. 15(9), pages 1-24, April.
    4. 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.
    5. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    6. Michéle Weisbach & Tobias Schneider & Dominik Maune & Heiko Fechtner & Utz Spaeth & Ralf Wegener & Stefan Soter & Benedikt Schmuelling, 2021. "Intelligent Multi-Vehicle DC/DC Charging Station Powered by a Trolley Bus Catenary Grid," Energies, MDPI, vol. 14(24), pages 1-21, December.
    7. Sinigaglia, Tiago & Eduardo Santos Martins, Mario & Cezar Mairesse Siluk, Julio, 2022. "Technological evolution of internal combustion engine vehicle: A patent data analysis," Applied Energy, Elsevier, vol. 306(PA).
    8. Khaleghikarahrodi, Mehrsa & Macht, Gretchen A., 2023. "Patterns, no patterns, that is the question: Quantifying users’ electric vehicle charging," Transport Policy, Elsevier, vol. 141(C), pages 291-304.

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