Data Based Linear Power Flow Model: Investigation of a Least-Squares Based Approximation

Zhentong Shao, Qiaozhu Zhai, Jiang Wu, Xiaohong Guan

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Linearization of power flow is an important topic in power system analysis. The computational burden can be greatly reduced under the linear power flow (LPF) model while the model error is the main concern. Therefore, various linear power flow models have been proposed in literature and dedicated to seek better linear models. Many linear power flow models are based on some kind of transformation/simplification/Taylor expansion of AC power flow equations. In this paper, numerical performance and theoretical explanation of the data-based linear power flow model are investigated. The direct least-squares method with complete orthogonal decomposition is designed for addressing collinear data and big data. The resulted linear power flow model is named as least-squares distribution factors (LSDF) and its performance is investigated in cold-start applications. It is found that LSDF is in fact an approximation of the optimal LPF with minimum mean square error. It is also proved that the LSDF can give an accurate estimation of total system losses. Comprehensive numerical testing show that the LSDF can work very well for system with large load variations. The average error of LSDF is only about 1% of the average error of power transfer distribution factor (PTDF) in numerical testing.

Original languageEnglish
Article number9363590
Pages (from-to)4246-4258
Number of pages13
JournalIEEE Transactions on Power Systems
Volume36
Issue number5
DOIs
StatePublished - Sep 2021

Keywords

  • DC power flow
  • least-squares
  • linear power flow
  • power transfer distribution factor

Fingerprint

Dive into the research topics of 'Data Based Linear Power Flow Model: Investigation of a Least-Squares Based Approximation'. Together they form a unique fingerprint.

Cite this