Decomposed Iterative Optimal Power Flow with Automatic Regionalization

Xinhu Zheng, Dongliang Duan, Liuqing Yang and Haonan Wang
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Xinhu Zheng: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Dongliang Duan: Department of Electrical and Computer Engineering, University of Wyoming, Laramie, WY 82071, USA
Liuqing Yang: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Haonan Wang: Department of Statistics, Colorado State University, Fort Collins, CO 80523, USA

Energies, 2020, vol. 13, issue 18, 1-22

Abstract: 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.

Keywords: optimal power flow; automatic regionalization; decomposed iterative algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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