An Online Joint Optimization–Estimation Architecture for Distribution Networks

Guo, Yi; Zhou, Xinyang; Zhao, Changhong; Chen, Lijun; Hug, Gabriela; Summers, Tyler Holt

doi:10.1109/tcst.2023.3254137

An Online Joint Optimization–Estimation Architecture for Distribution Networks

Journal Article · Mon Mar 27 00:00:00 EDT 2023 · IEEE Transactions on Control Systems Technology

DOI:https://doi.org/10.1109/tcst.2023.3254137· OSTI ID:1971894

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Eidgenoessische Technische Hochschule (ETH), Zurich (Switzerland)
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Chinese University of Hong Kong (China)
University of Colorado, Boulder, CO (United States)
University of Texas at Dallas, Richardson, TX (United States)

Here in this article, we propose an optimal joint optimization-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 OPF decisions 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 OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization 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.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; ETH Zurich Postdoctoral Fellowship; National Science Foundation (NSF); Hong Kong Research Grants Council (RGC)

Grant/Contract Number:: AC36-08GO28308; EE-0007998; CMMI-1728605; 24210220

OSTI ID:: 1971894

Report Number(s):: NREL/JA-5D00-84187; MainId:84960; UUID:01595805-6c82-4f67-bd77-c0c5649fc20f; MainAdminID:69363

Journal Information:: IEEE Transactions on Control Systems Technology, Vol. 31, Issue 6; ISSN 1063-6536

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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