<![CDATA[The rapid integration of renewable energy into microgrids introduces complex challenges for maintaining dynamic voltage stability and frequency regulation due to the stochastic and intermittent nature of solar and wind generation. Traditional control methods, including PID and model predictive controllers, often fail to adapt effectively to rapid fluctuations and nonlinear system dynamics, highlighting the need for intelligent, adaptive control strategies.This study aims to investigate the effectiveness of deep reinforcement learning (DRL) for real-time voltage stability and frequency regulation in microgrids with high renewable energy penetration. The research seeks to evaluate DRL’s ability to optimize control actions, improve system resilience, and enhance renewable energy utilization compared to conventional methods. A simulation-based approach was employed, modeling microgrid dynamics with integrated solar and wind sources, energy storage systems, and variable loads. DRL controllers were developed using actor-critic architectures and trained to learn optimal control policies through iterative interaction with the simulated environment. System performance was assessed using voltage deviation, frequency deviation, control effort, renewable utilization, and resilience metrics. DRL-based control significantly reduced voltage and frequency deviations to 0.022 p.u. and 0.037 Hz, respectively, while minimizing control effort to 37% and increasing renewable utilization to 92%. System resilience improved to 0.91, outperforming conventional PID and MPC strategies under varying load and generation scenarios. Deep reinforcement learning provides a robust, adaptive approach for microgrid stability management, enabling enhanced reliability, efficiency, and sustainable integration of high-penetration renewable energy. The study demonstrates DRL’s potential for scalable deployment in complex renewable-rich microgrids.]]>
