An Online Joint Optimization-Estimation Architecture for Distribution Networks
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.
Cite
@article{arxiv.2203.11327,
title = {An Online Joint Optimization-Estimation Architecture for Distribution Networks},
author = {Yi Guo and Xinyang Zhou and Changhong Zhao and Lijun Chen and Gabriela Hug and Tyler H. Summers},
journal= {arXiv preprint arXiv:2203.11327},
year = {2022}
}