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

TL;DR

This paper introduces a hybrid ML-RL framework that combines machine learning for fast stability prediction with reinforcement learning for dynamic control, improving real-time smart grid stability management.

Contribution

It presents a novel hybrid approach integrating ML and RL to enhance grid stability prediction and control, reducing training time and computational complexity.

Findings

Hybrid ML-RL model stabilizes the grid effectively.

Achieves rapid convergence and reduces training time.

Enhances decision-making efficiency for real-time applications.

Abstract

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.