Multiagent Deep Reinforcement Learning for Reactive Power Control in Distribution Grids

Maintaining voltage stability in distribution grids becomes increasingly challenging with the growing penetration of renewable energy sources like solar and wind. These resources inject variable amounts of reactive power, impacting voltage profiles. Conventional control methods struggle to adapt to these dynamics. This paper proposes a Multiagent Deep Reinforcement Learning (MARL) approach for real-time reactive power control in distribution grids. The MARL framework enables distributed energy resources to cooperatively learn optimal reactive power injection/absorption strategies, ensuring voltage stability and efficient grid operation. We developed a detailed simulation model incorporating solar power and mini-hydro integration to validate the effectiveness of the proposed approach. The MARL system significantly outperformed conventional methods, maintaining voltage within the desired range (0.95 pu - 1.05 pu) 98% of the time, compared to 85% with traditional methods. It also reduced reactive power losses by 15% and enhanced grid resilience, effectively handling disturbances like sudden load changes. Our results demonstrate the potential of MARL to revolutionize reactive power control, providing a robust solution for modern, renewable-rich power grids.