  In this paper, we propose an optimal control-estimation architecture for
distribution networks, which jointly solves the optimal power flow (OPF)
problem and static state estimation (SE) problem through an online
gradient-based feedback algorithm. The main objective is to enable a fast and
timely interaction between the optimal controllers and state estimators with
limited sensor measurements. First, convergence and optimality of the proposed
algorithm are analytically established. Then, the proposed gradient-based
algorithm is modified by introducing statistical information of the inherent
estimation and linearization errors for an improved and robust performance of
the online control decisions. Overall, the proposed method eliminates the
traditional separation of control and operation, where control and estimation
usually operate at distinct layers and different time-scales. Hence, it enables
a computationally affordable, efficient and robust online operational framework
for distribution networks under time-varying settings.
