Renewable Energy Hybrid Systems: Optimization of Solar–Wind Microgrids Using AI Algorithms

Abstract

The increasing demand for sustainable energy solutions has accelerated the adoption of renewable energy systems, particularly in decentralized and off-grid environments. Hybrid renewable energy systems combining solar and wind resources offer significant potential for improving energy reliability and efficiency. However, the intermittent and stochastic nature of these energy sources presents challenges in system optimization and energy management. This study proposes an artificial intelligence– based optimization framework for solar–wind hybrid microgrids, integrating machine learning and evolutionary algorithms to enhance system performance. The framework incorporates real-time data on solar irradiance, wind speed, and load demand to optimize energy generation, storage, and distribution. Algorithms such as genetic algorithms, particle swarm optimization, and artificial neural networks are employed to achieve optimal system configuration and operational efficiency. The results demonstrate improved energy utilization, reduced operational costs, and enhanced system reliability compared to conventional optimization methods. The study highlights the potential of AI-driven approaches in advancing renewable energy systems and supporting the transition toward sustainable energy infrastructures.