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

TL;DR

This paper presents a real-time feedback optimization method for power grids that integrates dynamic state estimation to handle noisy measurements, ensuring stability and convergence to optimal power flow set-points.

Contribution

It introduces a novel integration of dynamic state estimation with feedback optimization for power grids, providing stability guarantees and error bounds in a stochastic setting.

Findings

The method achieves convergence of state estimates and control inputs to true values.

Stability of the interconnected system is theoretically certified.

Effectiveness demonstrated on a high-resolution consumption data test case.

Abstract

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.