Grid Stability Enhancement through Machine Learning-driven Control Strategies in Renewable Energy Integration

The integration of renewable energy sources into the power grid poses significant challenges for grid stability and reliability. In this study, we investigate the efficacy of machine learning-driven control strategies in enhancing grid stability during renewable energy integration. Specifically, we explore the performance of reinforcement learning (RL) and support vector regression (SVR) techniques in regulating grid frequency, voltage, and line loading metrics. Through extensive numerical analysis and simulation results, we demonstrate that RL-based and SVR-based control strategies outperform traditional baseline methods in mitigating frequency and voltage deviations, optimizing line loading characteristics, and managing grid congestion. These findings underscore the potential of machine learning-driven control strategies to facilitate the integration of renewable energy sources into the grid while ensuring grid stability and reliability. Further research is warranted to explore the scalability, robustness, and real-world applicability of these strategies in diverse grid environments, paving the way for a more sustainable and resilient energy future.