Forecasting electric vehicles recharge impact on distribution network

The expansion of the electric vehicles market has resulted in the major emergence of new models with increasingly efficient charging systems, designed to meet drivers' everyday needs. This emergence will undoubtedly result in the proliferation of electric vehicle charging stations that will be connected to the electrical distribution network for medium voltage power. In this paper, we have demonstrated the impact of fast charging of electric vehicles during peak periods exceeding the power limit of the distribution network compared to random charging. Additionally, two artificial intelligence models, Artificial Neural Network (ANN) and Random Forest (RF), were used to predict potential power surges resulting from electric vehicle fast charging. The prediction results show high performance for the RF model compared to those obtained by ANN model.

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Published
2025-05-15
DOI
https://doi.org/10.1109/iraset64571.2025.11008219
Primary Topic
Power Systems and Technologies
Type
article
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article

Forecasting electric vehicles recharge impact on distribution network

Touria Haidi, Badr Nasiri, Mounir Derri, Boutaina Talbi
Power Systems and Technologies
article

Forecasting electric vehicles recharge impact on distribution network

Touria Haidi, Badr Nasiri, Mounir Derri, Boutaina Talbi
article en

Abstract

The expansion of the electric vehicles market has resulted in the major emergence of new models with increasingly efficient charging systems, designed to meet drivers' everyday needs. This emergence will undoubtedly result in the proliferation of electric vehicle charging stations that will be connected to the electrical distribution network for medium voltage power. In this paper, we have demonstrated the impact of fast charging of electric vehicles during peak periods exceeding the power limit of the distribution network compared to random charging. Additionally, two artificial intelligence models, Artificial Neural Network (ANN) and Random Forest (RF), were used to predict potential power surges resulting from electric vehicle fast charging. The prediction results show high performance for the RF model compared to those obtained by ANN model.

Université Moulay Ismail de Meknes (MA), Hassania School of Public Works (MA)
Openalex Percentile: Top 90%
Power Systems and Technologies
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Forecasting electric vehicles recharge impact on distribution network — Touria Haidi, Badr Nasiri, et al. · (2025) | TGRS Research Map | TGRS