ArticleScientific reports2025
Wind energy assessment and hybrid micro-grid optimization for selected regions of Saudi Arabia.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.74 W/m² to 149.56 W/m², thereby rendering them suitable for small-scale hybrid applications. A multi-strategy serial cuckoo search algorithm was employed to evaluate three distinct configurations, and the results indicated that the integration of PV, WT, and BS yielded the most cost-effective solution for the majority of regions, achieving a levelized cost of energy of 0.148$/kWh and a loss of power supply probability below 0.05%. Notably, alternative configurations demonstrated superior reliability in locations such as Al-Baha, Taif, and Tabuk. The rseults of this study provide valuable insights into the design of scalable, sustainable, and cost-efficient hybrid MGs tailored to regions with low wind potential, thereby contributing to enhanced energy access and economic development in remote locations.
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