An Online Joint Optimization-Estimation Architecture for Distribution Networks

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

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations


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.

Original languageAmerican English
Pages (from-to)2303-2318
Number of pages16
JournalIEEE Transactions on Control Systems Technology
Issue number6
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 1993-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-84187


  • Convergence and optimality analysis
  • distribution networks
  • online optimization algorithms
  • operational architecture
  • optimal power flow (OPF)
  • power systems
  • state estimation (SE)


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