The rapid expansion of cloud computing, artificial intelligence applications, and hyperscale digital services has significantly increased the energy demand of modern data centers, raising concerns about sustainability and operational efficiency. Energy storage systems have emerged as a promising solution for stabilizing power supply, integrating renewable energy sources, and improving overall energy utilization in data center infrastructures. However, conventional energy management strategies often lack the intelligence required to dynamically optimize energy storage and distribution under varying workloads and fluctuating energy availability. This study explores the concept of intelligent energy storage management for sustainable data centers by integrating advanced analytics, machine learning techniques, and real-time monitoring systems to optimize energy storage operations. The proposed framework enables predictive energy demand forecasting, intelligent charging and discharging of storage systems, and efficient integration of renewable energy sources such as solar and wind power. Through intelligent decision-making mechanisms, the system aims to reduce energy waste, lower operational costs, and minimize carbon emissions while maintaining high reliability and performance of data center operations. The findings highlight the potential of intelligent energy storage management systems to significantly enhance energy efficiency and support the transition toward greener and more sustainable data center infrastructures.
Dr. Deepak Tomar (Mon,) studied this question.