(untitled) (Comprehensive Journal of Science, 2025)
The increasing penetration of renewable energy sources such as solar and wind into power systems has created new challenges for grid stability and reliability, especially in countries facing frequent power outages like Libya. This study proposes the use of Artificial Intelligence (AI) techniques to optimize the operation and management of renewable energy systems. Machine learning algorithms are applied for short-term forecasting of solar irradiance and wind speed, while optimization models are developed for intelligent scheduling of hybrid renewable sources and storage systems. The proposed approach aims to minimize fluctuations, reduce reliance on fossil fuels, and improve overall grid stability. Simulation results demonstrate that AI-based optimization can enhance the efficiency and reliability of renewable energy integration, making it a promising solution for sustainable energy development in Libya.