Journal of Advanced Multidisciplinary Research

As power grids transition toward sustainable sources, integrating intermittent renewable energy systems poses significant stability and efficiency challenges. This paper presents an AI-driven framework designed to optimize energy distribution across hybrid renewable microgrids. Utilizing a combination of deep reinforcement learning and predictive forecasting models, our approach anticipates dynamic demand fluctuations while maintaining grid balance. Experimental results demonstrate a 14.2% reduction in overall transmission losses and a 22% improvement in peak-load response efficiency compared to traditional management strategies. These findings underscore the potential of scalable machine learning algorithms in modernizing clean energy infrastructure.