AI-Driven Energy Storage Management for Smart Grids in Sustainable Urban Ecosystems

The increased energy consumption in urban ecosystems necessitates the use of advanced energy storage management systems. Although static algorithms are essential to the operation of conventional smart grid systems, these are inherently inefficient and have significant running costs per second because of their intractability. These systems' inability to incorporate renewable energy sources will ultimately threaten the grid's reliability. By using Machine Learning (ML) algorithms for real-time dynamic control, predictive analysis, and optimum management of localized energy storage solutions, the study introduces a revolutionary AI-driven system that improves on traditional techniques. By using data-driven models, reinforcement learning (RL), and time-series forecasting, it enhances energy use, grid stability, and the integration of renewable energy. According to experiments, the AI system satisfies demand in peak and renewable shortage situations with 90.5% greater energy efficiency, 26.5% lower cost, and 92% better stability than a traditional system. A step toward an urban energy solution that is resilient and sustainable diversification of markets. It demonstrates how much better the system works at maximizing energy storage, expenses, and global grid operation.