AI-DRIVEN PREDICTIVE CONTROL FOR HYBRID RENEWABLE ENERGY SYSTEMS (HRES) IN SMART GRIDS

Abstract

Imagine a future where renewable energy sources like solar and wind power are seamlessly integrated into the grid, providing clean and reliable electricity to communities world-wide. The integration of multi-technology energy storage solutions plays a crucial role in mitigating the intermittent nature of renewable energy sources, enhancing grid stability, and enabling real-time energy management. This research explores the role Ai- driven Prediction control for hybrid renewable energy systems in smart grids convergence of artificial intelligence (AI), blockchain, and multi-objective optimization techniques facilitates adaptive decision-making, efficient power distribution, and enhanced energy security. Decentralized energy storage networks and AI-driven demand-side optimization strategies improve grid resilience while minimizing transmission losses. This vision is becoming a reality with the development of hybrid renewable energy system (HRES), which combine multiple energy sources to optimize efficiency and reduce carbon emissions. However, the unstable nature of these sources poses impactful challenges for grid stability and reliability. To figure out this challenge, researchers now focus on artificial intelligence Ai- driven predictive control systems by employing advance machine Learning algorithm and real- time data analytics, these frameworks can predict energy demand and generation, streamline energy storage and distribution, and ensure grid stability.  By harnessing the power AI can open the full potential of renewable energy and create a cleaner, and greener world for generations to come.