Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
This work proposes a two-step framework to manage congestion in low-voltage grids using reinforcement learning under uncertainty. It separates congestion detection using a random forest classifier from control using an actor-critic controller. The method is tested against measurement noise and grid model mismatches, showing high performance even when the grid model is imperfect.