In order to transform the energy infrastructure into including more renewable energy sources that often depend on weather conditions, energy storage management systems are essential to provide the right amount of energy at the right time. This thesis explores the integration of Artificial Intelligence (AI) in energy storage management systems. AI and Machine Learning (ML) are explored as potential tools for improving energy storage systems' (ESS) performance and efficiency, thereby ensuring steady energy supply and grid reliability.

Through the collection of data and analysis of existing systems and the evaluation of development of algorithms tailored to AI and ML, this study, aims to investigate how AI are strategically integrated into IT frameworks governing ESS that are based on renewable energy sources to enhance operational efficiency, energy efficiency, and system reliability. This research insists on investigating on optimal energy distribution, and increased system longevity that can be achieved through AI. In comparison with conventional systems, according to key findings when implementing AI and ML, through predictive analytics, AI/ML optimizes energy storage management and battery lifespan, reduces costs, enables real-time decision-making for a more efficient, reliable, and sustainable energy system by facilitating renewable energy integration, stabilizing the grid, reducing costs, and improving battery lifespan with real-time monitoring. Through qualitative research methods, the study addresses the limitations, such as generalizability, data quality, and computational demands.

Data privacy and risk management are examined in depth using these methods. Additionally, it enhances the understanding of sustainable energy management's societal implications, highlighting the critical role of qualitative analysis in exploring the complex interplay between technology, ethics, and society. In addition to offering significant insights for energy companies, policymakers, and researchers, the study's originality lies in its specific application of AI and ML to the renewable energy storage sector. Underscoring the crucial role of technological innovation in contemporary energy challenges, this thesis evaluates methods for developing and advancing energy storage management systems.