Adaptive Control Strategies for Enhancing the Integration and Stability of Renewable Energy Sources in Smart Microgrids

The growing use of renewable energy sources (RES) in small-scale power systems brings about major obstacles in keeping the power grid steady and dependable because RES naturally fluctuates and is not always available. This study suggests a new approach to control that uses sophisticated machine learning techniques and instant data analysis to better manage RES in intelligent power systems. The goal of this approach is to boost the stability of the system, cut down on running expenses, and increase the efficiency of energy use. This study is different from previous research because it introduces a mixed control system that combines Model Predictive Control (MPC) with Reinforcement Learning (RL) to adjust control settings in response to current conditions and past data trends.