AI-ENHANCED INTEGRATION OF SOLAR AND WIND ENERGY IN SMART GRIDS USING HYBRID OPTIMIZATION TECHNIQUES

Abstract

The increasing penetration of renewable energy sources such as solar and wind power presents both opportunities and challenges for smart grid operation. Efficient integration of these intermittent energy sources is crucial for maintaining grid stability, minimizing operational costs, and enhancing energy reliability. This paper proposes an AIenhanced hybrid optimization framework for integrating solar and wind energy into smart grids. The approach combines machine learning-based forecasting with multiobjective optimization algorithms to schedule energy generation, storage, and consumption dynamically. The proposed system predicts renewable generation patterns, optimizes dispatch strategies, and minimizes energy losses while ensuring supply-demand balance. Simulation results demonstrate improved energy efficiency, reduced cost, and enhanced stability compared to conventional optimization methods. The framework supports scalable, real-time decision-making, making it suitable for large-scale smart grid deployments.