Solving Optimal Power Flow for Distribution Networks with State Estimation Feedback

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

Research output: Contribution to conferencePaperpeer-review

9 Scopus Citations


Conventional optimal power flow (OPF) solvers assume full observability of the involved system states. However in practice, there is a lack of reliable system monitoring devices in the distribution networks. To close the gap between the theoretic algorithm design and practical implementation, this work proposes to solve the OPF problems based on the state estimation (SE) feedback for the distribution networks where only a part of the involved system states are physically measured. The SE feedback increases the observability of the under-measured system and provides more accurate system states monitoring when the measurements are noisy. We analytically investigate the convergence of the proposed algorithm. The numerical results demonstrate that the proposed approach is more robust to large pseudo measurement variability and inherent sensor noise in comparison to the other frameworks without SE feedback.

Original languageAmerican English
Number of pages8
StatePublished - Jul 2020
Event2020 American Control Conference, ACC 2020 - Denver, United States
Duration: 1 Jul 20203 Jul 2020


Conference2020 American Control Conference, ACC 2020
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2020 AACC.

NREL Publication Number

  • NREL/CP-5D00-75019


  • distirbution systems
  • optimal power flow
  • state estimations


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