Control System Approaches for the Seamless Integration of Renewable Energy Sources into Power Grids

Abstract

The integration of renewable energy sources (RES) into power grids presents significant challenges due to their inherent intermittency. This study proposes a novel hybrid control system combining artificial intelligence (AI) with conventional droop control to mitigate these stability issues. The methodology integrates LSTM and CNN neural networks for accurate grid fluctuation forecasting, STATCOM devices for dynamic reactive power compensation, and reinforcement learning algorithms for optimal energy storage dispatch. Simulation results demonstrate the system's effectiveness, showing a 40% reduction in frequency deviations and 91% prediction accuracy for power fluctuations. These findings indicate that the proposed framework significantly enhances grid stability and scalability while accommodating high penetration of renewable energy sources. The research contributes to the development of more resilient smart grids capable of handling the variable nature of clean energy generation. Keywords: Renewable energy, droop control, AI forecasting, grid resilience, power system stability, smart grid integration.