Comparison of Feedforward and Feedback Controlfor Power Grid Congestion Management

This report compares two control architectures for managing congestion inpower grids. The first is a state-of-the-art feedforward controller that relies on amodel of the grid and forecasts of possible disturbances to adjust the generation.The other controller is based on Online Feedback Optimization (OFO), and usesmeasurements to adjust the generation in real-time. This is done by using aprojected gradient descent control algorithm with input and output constraints. Forthe comparison of the two control architectures, a Python library called Pandapoweris used, with a 9-bus grid that has 2 PQ generators. The results show thatfeedforward control is not robust to forecast errors. Meanwhile, feedback control canadjust in real-time and adapt to disturbances. This suggests that feedback control isbetter suited for power grid congestion management with a large share of renewablegeneration.