AI-DRIVEN ADAPTIVE ENERGY OPTIMIZATION AND MANAGEMENT IN SMART MICROGRIDS USING GENERATIVE ADVERSARIAL NETWORKS (GANS)

Abstract

The increasing penetration of renewable energy sources in smart microgrids introduces challenges related to demand-supply balance, energy forecasting accuracy, and optimal energy distribution. This paper proposes an AI-driven energy optimization framework based on Generative Adversarial Networks (GANs) to enhance the efficiency and reliability of smart microgrids. Unlike traditional optimization methods, GANs leverage adversarial learning to generate highly accurate energy demand forecasts and improve real-time energy dispatch. The proposed method was tested on a real-world smart microgrid dataset, integrating solar PV, wind turbines, and battery storage systems. Results demonstrate that the GAN-based approach improves energy demand prediction accuracy by 24.7% compared to conventional Long Short-Term Memory (LSTM) networks and reduces energy storage inefficiencies by 14.8%. Furthermore, the optimization model enhances grid stability, achieving a 9.6% reduction in voltage fluctuations and a 12.3% decrease in peak load stress, thereby increasing overall microgrid efficiency. These improvements contribute to better energy sustainability, reduced operational costs, and enhanced grid resilience. The findings highlight the potential of GANs as a powerful tool for next generation AIdriven energy management in smart microgrids