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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Renewable Energy Development
Derri, Mounir
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Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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The impact of electric vehicles and photovoltaic energy integration on distribution network Talbi, Boutaina; Derri, Mounir; Haidi, Touria; Janyene, Aberrahmane
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i3.pp1788-1798

Abstract

The transition towards an eco-friendly and lasting energy system is enabled by the presence of electric vehicles (EVs) and the utilization of renewable energy resources. Despite its intermittent occurrence, solar energy empowers sunny areas to capture renewable energy from sunlight and produce electricity continuously all day long. This energy will be able to smooth the consumption peaks during peak hours and ensure the electrical network flexibility by being injected at various voltage levels. Electric vehicles are supplied by low voltage recharging stations and are seen as power loads that, if rapidly and simultaneously recharged, could potentially affect the stability of the electrical grid. This paper introduces an intelligent method for electric vehicle charging designed to mitigate the impact of simultaneous charging, specifically addressing voltage drop issues. Furthermore, this study demonstrates the positive impact of integrating photovoltaic energy into the distribution network, serving as support, and alleviating the afore cited impact. The simulation results obtained using MATLAB/Simulink illustrate the effectiveness of the proposed strategy in charging electric vehicles, particularly in reducing the observed voltage drop. There is a notable enhancement in voltage drop across all study cases, amounting to a 27 V improvement compared to charging without the proposed method.
Multi-temporal forecasting of wind energy production using artificial intelligence models Bouabdallaoui, Doha; Haidi, Touria; Derri, Mounir; Hbiak, Ishak; El Jaadi, Mariam
International Journal of Renewable Energy Development Vol 14, No 3 (2025): May 2025
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2025.61086

Abstract

In response to changing energy demands, electricity suppliers are increasingly turning to sustainable energy sources, with wind power emerging as a promising solution. This study aims to predict wind energy production over four time horizons: hourly, daily, weekly, and monthly, for a 12,300 kW wind farm located in Northamptonshire, UK. We employed three artificial intelligence (AI) techniques: an ensemble of bagged decision trees, artificial neural networks (ANNs), and support vector machines (SVMs). The paper provides a comparative evaluation of AI-based forecasting techniques for wind energy prediction, highlighting differences in model performance across time horizons while emphasizing the strengths and limitations of each method in addressing the temporal variability of wind energy production. The models were tested over various times using important performance measures, such as the correlation coefficient (R), the coefficient of determination (R²), mean absolute error (MAE), root mean squared error (RMSE), and bias. The results indicate that support vector machines achieve the highest accuracy for medium-term forecasts, with a coefficient of determination of 0.9722 and a mean absolute error of 44.91 kW. Artificial neural networks perform best in short-term forecasting, particularly at the daily level, with a coefficient of determination of 0.948 and a mean absolute error of 36.04 kW. In contrast, long-term predictions exhibit greater variability across models, with the coefficient of determination decreasing to 0.778, reflecting the increased complexity of extended forecasting. The ensemble of bagged decision trees demonstrates strong predictive capability but with slightly higher error margins compared to support vector machines. The obtained results could serve as a reference for selecting the most suitable models based on forecasting objectives and time constraints. Future improvements in forecasting accuracy could happen by combining these models with optimization algorithms, especially for medium- and long-term predictions, where making accurate forecasts is still very difficult.
Co-Authors Bouabdallaoui, Doha El Jaadi, Mariam Haidi, Touria Hbiak, Ishak Janyene, Aberrahmane Talbi, Boutaina