Decomposed iterative optimal power flow with automatic regionalization

Xinhu Zheng, Dongliang Duan, Liuqing Yang, Haonan Wang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


The optimal power flow (OPF) problem plays an important role in power system operation and control. The problem is nonconvex and NP-hard, hence global optimality is not guaranteed and the complexity grows exponentially with the size of the system. Therefore, centralized optimization techniques are not suitable for large-scale systems and an efficient decomposed implementation of OPF is highly demanded. In this paper, we propose a novel and efficient method to decompose the entire system into multiple sub-systems based on automatic regionalization and acquire the OPF solution across sub-systems via a modified MATPOWER solver. The proposed method is implemented in a modified solver and tested on several IEEE Power System Test Cases. The performance is shown to be more appealing compared with the original solver.

Original languageEnglish (US)
Article number4987
Issue number18
StatePublished - Sep 2020

Bibliographical note

Funding Information:
Funding: This research was supported in part by the National Science Foundation under Grants DMS-1923142, CNS-1932413, ECCS-1828066, OAC-1923983, and CNS-1932139.

Publisher Copyright:
© 2020 by the authors.


  • Automatic regionalization
  • Decomposed iterative algorithm
  • Optimal power flow


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