Predictive control of coordinated SVGs for voltage stability with reinforcement learning

The increasing integration of renewable energy sources into power grids brings challenges due to their unpredictable and fluctuating nature, which often leads to voltage instability. Static Var Generators (SVGs) are commonly used to stabilize voltage, but frequent switching causes higher energy losses and reduces their efficiency. This study proposes a solution that combines reinforcement learning (RL) and model predictive control (MPC) to improve SVG performance. The RL model forecasts voltage trends based on different levels of renewable energy integration, allowing for timely adjustments to SVG operations. A coordination strategy for multiple SVGs is also introduced, enabling a balanced distribution of workload across devices. This reduces the frequency of switching actions, thereby lowering energy losses and operational wear. By dynamically managing reactive power among SVGs, the approach enhances overall grid stability.