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Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings

Author

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  • Jasmine Ramsebner

    (Energy Economics Group (EEG), Technische Universität Wien, Gusshausstraße 25-29, E370-3, 1040 Vienna, Austria)

  • Albert Hiesl

    (Energy Economics Group (EEG), Technische Universität Wien, Gusshausstraße 25-29, E370-3, 1040 Vienna, Austria)

  • Reinhard Haas

    (Energy Economics Group (EEG), Technische Universität Wien, Gusshausstraße 25-29, E370-3, 1040 Vienna, Austria)

Abstract

Interest in and demand for battery electric vehicles (BEVs) is growing strongly due to the increasing awareness of climate change and specific decarbonization goals. One of the largest challenges remains the provision of large-scale, efficient charging infrastructure in multi-apartment buildings. Successful load management (LM) for BEV charging directly influences the technical requirements and the economic and environmental aspects of charging infrastructure and can prevent costly distribution grid expansion. The main objective of this paper is to evaluate potential LM approaches in multi-apartment buildings to avoid an increase in existing electricity demand peaks with BEV diffusion. Using our model parameters, off-peak charging achieved a 40% reduction in the building’s demand peak at 100% BEV diffusion compared to uncontrolled charging and reduced the correlation between BEV charging and the national share of thermal power generation. The most efficient charging capacity in the private network was achieved at 0.44 kW/BEV. A verification of the model results with the demonstration phase of the “Urcharge” project supports our overall findings. Our results outline the advantages of LM across a large-scale BEV charging network to control the impact on the electricity system along with the diffusion of e-mobility.

Suggested Citation

  • Jasmine Ramsebner & Albert Hiesl & Reinhard Haas, 2020. "Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings," Energies, MDPI, vol. 13(22), pages 1-23, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5927-:d:444637
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Thomas Steens & Jan-Simon Telle & Benedikt Hanke & Karsten von Maydell & Carsten Agert & Gian-Luca Di Modica & Bernd Engel & Matthias Grottke, 2021. "A Forecast-Based Load Management Approach for Commercial Buildings Demonstrated on an Integration of BEV," Energies, MDPI, vol. 14(12), pages 1-25, June.

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