LAPSE:2023.23911
Published Article
LAPSE:2023.23911
Fast Heuristic AC Power Flow Analysis with Data-Driven Enhanced Linearized Model
Xingpeng Li
March 27, 2023
Abstract
Though the full AC power flow model can accurately represent the physical power system, the use of this model is limited in practice due to the computational complexity associated with its non-linear and non-convexity characteristics. For instance, the AC power flow model is not incorporated in the unit commitment model for practical power systems. Instead, an alternative linearized DC power flow model is widely used in today’s power system operational and planning tools. However, DC power flow model will be useless when reactive power and voltage magnitude are of concern. Therefore, a linearized AC (LAC) power flow model is needed to address this issue. This paper first introduces a traditional LAC model and then proposes an enhanced data-driven linearized AC (DLAC) model using the regression analysis technique. Numerical simulations conducted on the Tennessee Valley Authority (TVA) system demonstrate the performance and effectiveness of the proposed DLAC model.
Keywords
Data-driven, linearization, power flow, power system operations, regression analysis
Suggested Citation
Li X. Fast Heuristic AC Power Flow Analysis with Data-Driven Enhanced Linearized Model. (2023). LAPSE:2023.23911
Author Affiliations
Li X: Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204-4005, USA
Journal Name
Energies
Volume
13
Issue
13
Article Number
E3308
Year
2020
Publication Date
2020-06-28
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en13133308, Publication Type: Journal Article
Record Map
Published Article

LAPSE:2023.23911
This Record
External Link

https://doi.org/10.3390/en13133308
Publisher Version
Download
Files
Mar 27, 2023
Main Article
License
CC BY 4.0
Meta
Record Statistics
Record Views
260
Version History
[v1] (Original Submission)
Mar 27, 2023
 
Verified by curator on
Mar 27, 2023
This Version Number
v1
Citations
Most Recent
This Version
URL Here
https://psecommunity.org/LAPSE:2023.23911
 
Record Owner
Auto Uploader for LAPSE
Links to Related Works
Directly Related to This Work
Publisher Version
(0.1 seconds)

[0.1 s]