Authors: Prof. Ankita Fouzdar, Prof. Mayanka Roy Mandal, Prof. Shraddha Tiwari
Abstract: The rapid growth of renewable energy sources (RES) such as solar and wind has created new opportunities for sustainable power generation, while also posing significant challenges due to their intermittent and unpredictable nature. Smart grids, equipped with advanced communication and control technologies, offer a promising platform for efficiently integrating these variable energy resources. This study explores the optimal integration of renewable energy into smart grids using artificial intelligence (AI)-based forecasting and optimization techniques. Machine learning and deep learning models are employed to accurately predict renewable generation and demand patterns, reducing uncertainty and enabling proactive grid management. Furthermore, advanced optimization algorithms such as genetic algorithms, particle swarm optimization, and reinforcement learning are applied to achieve optimal scheduling, load balancing, and energy storage utilization. The proposed framework enhances grid stability, minimizes energy losses, reduces reliance on fossil fuels, and ensures cost-effective and reliable power delivery. Simulation results validate the effectiveness of the AI-driven approach in improving renewable energy penetration and overall smart grid performance. This work highlights the potential of AI-enabled forecasting and optimization as key enablers for achieving sustainable, resilient, and intelligent energy systems
