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Title: Graph Laplacian Spectrum and Primary Frequency Regulation

Abstract

We present a framework based on spectral graph theory that captures the interplay among network topology, system inertia, and generator and load damping in determining the overall grid behavior and performance. Specifically, we show that the impact of network topology on a power system can be quantified through the network Laplacian eigenvalues, and such eigenvalues determine the grid robustness against low frequency disturbances. Moreover, we can explicitly decompose the frequency signal along scaled Laplacian eigenvectors when damping-inertia ratios are uniform across buses. The insight revealed by this framework partially explains why load-side participation in frequency regulation not only makes the system respond faster, but also helps lower the system nadir after a disturbance. Finally, by presenting a new controller specifically tailored to suppress high frequency disturbances, we demonstrate that our results can provide useful guidelines in the controller design for load-side primary frequency regulation. This improved controller is simulated on the IEEE 39-bus New England interconnection system to illustrate its robustness against high frequency oscillations compared to both the conventional droop control and a recent controller design.

Authors:
 [1]; ORCiD logo [2];  [1]
  1. California Institute of Technology
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1501653
Report Number(s):
NREL/CP-5D00-73482
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2018 IEEE Conference on Decision and Control (CDC), 17-19 December 2018, Miami Beach, Florida
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; graph Laplacian spectrum; frequency regulation; electric power grids; performance

Citation Formats

Guo, Linqi, Zhao, Changhong, and Low, Steven H. Graph Laplacian Spectrum and Primary Frequency Regulation. United States: N. p., 2019. Web. doi:10.1109/CDC.2018.8619252.
Guo, Linqi, Zhao, Changhong, & Low, Steven H. Graph Laplacian Spectrum and Primary Frequency Regulation. United States. doi:10.1109/CDC.2018.8619252.
Guo, Linqi, Zhao, Changhong, and Low, Steven H. Mon . "Graph Laplacian Spectrum and Primary Frequency Regulation". United States. doi:10.1109/CDC.2018.8619252.
@article{osti_1501653,
title = {Graph Laplacian Spectrum and Primary Frequency Regulation},
author = {Guo, Linqi and Zhao, Changhong and Low, Steven H.},
abstractNote = {We present a framework based on spectral graph theory that captures the interplay among network topology, system inertia, and generator and load damping in determining the overall grid behavior and performance. Specifically, we show that the impact of network topology on a power system can be quantified through the network Laplacian eigenvalues, and such eigenvalues determine the grid robustness against low frequency disturbances. Moreover, we can explicitly decompose the frequency signal along scaled Laplacian eigenvectors when damping-inertia ratios are uniform across buses. The insight revealed by this framework partially explains why load-side participation in frequency regulation not only makes the system respond faster, but also helps lower the system nadir after a disturbance. Finally, by presenting a new controller specifically tailored to suppress high frequency disturbances, we demonstrate that our results can provide useful guidelines in the controller design for load-side primary frequency regulation. This improved controller is simulated on the IEEE 39-bus New England interconnection system to illustrate its robustness against high frequency oscillations compared to both the conventional droop control and a recent controller design.},
doi = {10.1109/CDC.2018.8619252},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
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