AI-Driven Smart Grid Optimization for Renewable Energy Integration

Abstract

The increasing penetration of renewable energy sources into power grids introduces significant challenges related to intermittency, voltage fluctuations, and supply-demand balancing. This paper presents a hybrid artificial intelligence framework combining Transformer-based time series forecasting with deep reinforcement learning (DRL) for real-time smart grid optimization. The proposed system integrates solar and wind generation forecasting (MAE of 1.94%), battery energy storage scheduling, and dynamic load balancing across a simulated 500-node distribution network. Experimental results demonstrate that the AI-driven approach reduces frequency deviations by 66.7%, voltage variations by 68.4%, and achieves a 12.8% reduction in transmission losses compared to conventional automatic generation control methods. The framework's response time of 180 ms enables near-real-time grid management, while the system reliability index improved from 0.9945 to 0.9992. These findings confirm the efficacy of integrated AI methodologies for managing the complexity and variability inherent in renewable-dominant power systems.