AI-Optimized Energy Storage Systems for HighEfficiency Renewable Energy Integration

Abstract

The integration of artificial intelligence (AI) into energy storage systems (ESS) is revolutionizing the management and utilization of renewable energy sources. AI algorithms enhance the efficiency, reliability, and sustainability of hybrid renewable energy systems (HRES) by enabling real-time decision-making and adaptive control. Predictive analytics, powered by AI, accurately forecasts energy demand and generation, allowing for optimized charging and discharging of energy storage, ensuring energy availability during peak demand68. AI facilitates smart grids that automatically adjust energy flow based on real-time supply and demand, improving grid efficiency and reducing outages. Furthermore, AI-driven predictive maintenance monitors system health, predicts potential failures, and optimizes maintenance schedules, reducing downtime and extending the lifespan of ESS. User-centric optimization models incorporate consumer preferences and behaviors, fostering greater user engagement and promoting demand-side participation in HRES. AI techniques, such as reinforcement learning and genetic algorithms, optimize energy dispatch and storage strategies, reducing reliance on fossil fuels, lowering operational costs, and decreasing carbon emissions. This leads to cost savings and improves the efficiency of energy storage systems, which is essential for a future powered by renewable energy. AI ensures that clean energy can be harnessed and utilized effectively, creating a more sustainable and reliable energy grid