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All Journal International Journal of Renewable Energy Development
Sani Moussa Kadri
Laboratoire Energies Renouvelables et Efficacité Energétique, Institut International d’Ingénierie de l’Eau et de l’Environnement
Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering

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Methodology for the Selection and Optimal Sizing of Standalone PV / Wind Energy Systems with Battery Storage under Resource Availability Constraints Guétinsom Jean Kafando; Daniel YAMEGUEU; Sani Moussa Kadri
International Journal of Renewable Energy Development Accepted Articles
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.61740

Abstract

Access to electricity remains a major challenge in sub-Saharan Africa, particularly in rural areas where grid extension is often costly and unprofitable. Standalone photovoltaic (PV) and/or wind power systems with battery storage represent a promising solution, yet they still face technical and economic barriers, especially related to sizing and storage costs. This article proposes an innovative methodology for the selection and optimal sizing of such systems, integrating a predictive battery aging model based on the analysis of real charge/discharge cycles using the Rainflow algorithm and Miner’s rule. The methodology relies on four main techno-economic performance indicators: the Loss of Power Supply Probability (LPSP), the Levelized Cost of Energy (LCOE), the Capacity Factor (CF) of a wind turbine, and the Weighted Index of Complementarity and Productivity (WICP). It accounts for available resources, the user’s hourly consumption profile, and local climatic conditions. The approach is applied to the case of Nagréongo, a rural area in Burkina Faso. Only the PV/battery system proves to be a viable option. In contrast, the site is unsuitable for wind energy system installation, even in a hybrid configuration, as both the capacity factor (CF) and the wind complementarity index (WICP) remain below acceptable thresholds. The study also reveals that optimal configurations depend heavily on the hourly consumption profile, despite identical daily energy needs. Finally, a comparison with the conventional intuitive method and the HOMER software shows that the proposed methodology can reduce the LCOE by more than 50% and around 20%, respectively, thanks to a better consideration of real battery aging, hourly demand variability, and system idle periods.
Co-Authors Daniel YAMEGUEU Guétinsom Jean Kafando