Hybrid ML-RL Approach for Smart Grid Stability Prediction and Optimized Control Strategy

Electrical grids are now much more complex due to the rapid integration of distributed generation and alternative energy sources, which makes forecasting grid stability with optimized control a crucial task for operators. Traditional statistical, physics-based, and ML models can learn the pattern of the grid features, but have limitations in optimal strategy control with instability prediction. This work proposes a hybrid ML-RL framework that leverages ML for rapid stability prediction and RL for dynamic control and optimization. The first stage of this study created a baseline that explored the potential of various ML models for stability prediction. Out of them, the stacking classifiers of several fundamental models show a significant performance in classifying the instability, leading to the second stage, where reinforcement learning algorithms (PPO, A2C, and DQN) optimize power control actions. Experimental results demonstrate that the hybrid ML-RL model effectively stabilizes the grid, achieves rapid convergence, and significantly reduces training time. The integration of ML-based stability classification with RL-based dynamic control enhances decision-making efficiency while lowering computational complexity, making it well-suited for real-time smart grid applications.