Probabilistic Assessment of the Impact of Electric Vehicle Fast Charging Stations Integration into MV Distribution Networks Considering Annual and Seasonal Time-Series Data

Oscar Mauricio Hernández-Gómez () and João Paulo Abreu Vieira
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Oscar Mauricio Hernández-Gómez: Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, PA, Brazil
João Paulo Abreu Vieira: Electrical Engineering Graduate Program, Federal University of Pará, Belém 66075-110, PA, Brazil

Energies, 2024, vol. 17, issue 18, 1-20

Abstract: Electric vehicle (EV) fast charging stations (FCSs) are essential for achieving net-zero carbon emissions. However, their high power demands pose technical hurdles for medium-voltage (MV) distribution networks, resulting in energy losses, equipment performance issues, overheating, and unexpected tripping. Integrating FCSs into the grid requires considering annual and seasonal variations in EV fast-charging energy consumption. Neglecting these variations can lead to either underestimating or overestimating the impacts of FCSs on the networks. This paper introduces a probabilistic method to assess voltage profile violations, overload capacity, and increased power losses due to FCSs. By incorporating annual and seasonal time-series data, the method accounts for uncertainties related to EV fast charging. Applied to an MV feeder in Brazil, our evaluations highlight the impact of annual power consumption seasonality on EV-grid integration studies. Considering seasonal dependency is crucial for precise impact assessments of MV distribution networks. The proposed method aids utility engineers and planners in quantifying and mitigating the effects of EV fast charging, contributing to more reliable MV grid integration strategies.

Keywords: annual and seasonal time-series data; electric vehicles (EVs); fast charging stations (FCSs); medium-voltage (MV) distribution networks; probabilistic assessment (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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