An Online Joint Optimization-Estimation Architecture for Distribution Networks

TL;DR

This paper introduces an online joint control-estimation framework for distribution networks that integrates optimal power flow and state estimation, enhancing real-time operation under limited measurements.

Contribution

It presents a novel gradient-based algorithm that jointly solves OPF and SE problems with proven convergence and robustness, unifying control and estimation layers.

Findings

Algorithm converges and is optimal under theoretical analysis.

Enhanced robustness by incorporating statistical error information.

Enables real-time, efficient distribution network management.

Abstract

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.