Autonomous Voltage Stabilization in Hybrid Energy Grids with Reinforcement Learning

The growing incorporation of renewable energy sources into hybrid energy networks requires creative ways to maintain voltage stability. This work investigates using reinforcement learning (RL) algorithms to autonomously regulate voltage levels in hybrid energy systems that include solar, wind, and traditional power sources. It presents an innovative RL-based controller that dynamically modulates the output of distributed energy resources (DERs) in response to variable load demands and generation fluctuations. This research addresses voltage fluctuations in hybrid energy networks using RL. The proposed RL-based system improves voltage stability, flexibility, and efficiency, outperforming traditional techniques in both simulations and practical applications. The RL agent uses real-time data to learn optimum control techniques to reduce voltage variances successfully. The efficacy of the proposed system is assessed by simulations on a benchmark microgrid model, revealing substantial improvements in voltage regulation and system resilience relative to traditional control approaches. The results demonstrate that the RL-based method improves voltage stability and optimizes overall energy utilization efficiency within the grid. It emphasizes the possibility of incorporating AI-driven solutions in managing hybrid energy systems, facilitating the development of more sustainable and dependable energy infrastructures.