Optimal Power Flow With State Estimation in the Loop for Distribution Networks

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

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations


In this article, we propose a framework for running optimal control-estimation synthesis in distribution networks. Our approach combines a primal-dual gradient-based optimal power flow solver with a state estimation feedback loop based on a limited set of sensors for system monitoring, instead of assuming exact knowledge of all states. The estimation algorithm reduces uncertainty on unmeasured grid states based on certain online state measurements and noisy 'pseudomeasurements.' We analyze the convergence of the proposed algorithm and quantify the statistical estimation errors based on a weighted least-squares estimator. The numerical results on a 4521-node network demonstrate that this approach can scale to extremely large networks and provide robustness to both large pseudomeasurement variability and inherent sensor measurement noise.

Original languageAmerican English
Pages (from-to)3694-3705
Number of pages12
JournalIEEE Systems Journal
Issue number3
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2007-2012 IEEE.

NREL Publication Number

  • NREL/JA-5D00-86083


  • Distribution networks and power systems
  • feedback control
  • large-scale networks
  • optimal power flow (OPF)
  • state estimation (SE)
  • voltage regulation


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