Closing the Loop: Dynamic State Estimation and Feedback Optimization of Power Grids

This paper considers the problem of online feedback optimization to solve the AC Optimal Power Flow in real-time in power grids. This consists in continuously driving the controllable power injections and loads towards the optimal set-points in time-varying conditions based on real-time measurements performed on the grid. However, instead of assuming noise-free full state measurement like recent feedback optimization approaches, we connect a dynamic State Estimation using available measurements, and study its dynamic interaction with the optimization scheme. We certify stability of this interconnection and the convergence in expectation of the state estimate and the control inputs towards the true state values and optimal set-points respectively. Additionally, we bound the resulting stochastic error. Finally, we show the effectiveness of the approach on a test case using high resolution consumption data.