Machine learning enhanced hybrid energy storage management system for renewable integration and grid stability optimization in smart microgrids

The increasing share of variable renewable energy sources in the power grid has brought about tremendous challenges in the context of stability and reliability. An active energy stor-age management system is designed and presented in this paper to cater to the intermitten-cy of renewable resources while keeping the grid stable. The study develops and validates a novel hybrid energy storage management system that combines battery and supercapacitor technologies with machine learning optimization algorithms. The research methodology em-ploys a dual-layer control architecture integrating reinforcement learning for strategic energy dispatch and model predictive control for real-time operation. System performance was eval-uated using a comprehensive testbed comprising a 500kW solar installation, 250kWh battery storage, and 50kW supercapacitor array across varying weather and load conditions over six months. The system proposed, yielded results that were 27% better in overall energy perfor-mance than traditional storage management approaches while reducing voltage fluctuations by 43%. The machine learning algorithm successfully predicted renewable generation patterns with 92% accuracy, enabling proactive storage management strategies that reduced peak de-mand charges by 31%. The system maintained consistent performance across seasonal varia-tions, with high availability (99.97%) and significant reductions in maintenance requirements (62.5% fewer events). The successful integration of hybrid storage technologies with advanced machine learning algorithms establishes a viable framework for enhancing grid stability and economic performance in renewable-rich microgrids. The results reveal meaningful aspects for developing next-gen smart grid storage solutions for applications, particularly where com-paratively high reliability is needed to integrate renewables efficiently.