  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.
