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Title: Grid Frequency Extreme Event Analysis and Modeling: Preprint

Abstract

Sudden losses of generation or load can lead to instantaneous changes in electric grid frequency and voltage. Extreme frequency events pose a major threat to grid stability. As renewable energy sources supply power to grids in increasing proportions, it becomes increasingly important to examine when and why extreme events occur to prevent destabilization of the grid. To better understand frequency events, including extrema, historic data were analyzed to fit probability distribution functions to various frequency metrics. Results showed that a standard Cauchy distribution fit the difference between the frequency nadir and prefault frequency (f_(C-A)) metric well, a standard Cauchy distribution fit the settling frequency (f_B) metric well, and a standard normal distribution fit the difference between the settling frequency and frequency nadir (f_(B-C)) metric very well. Results were inconclusive for the frequency nadir (f_C) metric, meaning it likely has a more complex distribution than those tested. This probabilistic modeling should facilitate more realistic modeling of grid faults.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [1]
  1. 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 Grid Modernization Laboratory Consortium
OSTI Identifier:
1407845
Report Number(s):
NREL/CP-5D00-70029
DOE Contract Number:
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Power Plants (Wind Integration Workshop), 25-27 October 2017, Berlin, Germany
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; electric grid frequency; voltage; renewable energy; probabilitic modeling

Citation Formats

Florita, Anthony R, Clark, Kara, Gevorgian, Vahan, Folgueras, Maria, and Wenger, Erin. Grid Frequency Extreme Event Analysis and Modeling: Preprint. United States: N. p., 2017. Web.
Florita, Anthony R, Clark, Kara, Gevorgian, Vahan, Folgueras, Maria, & Wenger, Erin. Grid Frequency Extreme Event Analysis and Modeling: Preprint. United States.
Florita, Anthony R, Clark, Kara, Gevorgian, Vahan, Folgueras, Maria, and Wenger, Erin. Wed . "Grid Frequency Extreme Event Analysis and Modeling: Preprint". United States. doi:. https://www.osti.gov/servlets/purl/1407845.
@article{osti_1407845,
title = {Grid Frequency Extreme Event Analysis and Modeling: Preprint},
author = {Florita, Anthony R and Clark, Kara and Gevorgian, Vahan and Folgueras, Maria and Wenger, Erin},
abstractNote = {Sudden losses of generation or load can lead to instantaneous changes in electric grid frequency and voltage. Extreme frequency events pose a major threat to grid stability. As renewable energy sources supply power to grids in increasing proportions, it becomes increasingly important to examine when and why extreme events occur to prevent destabilization of the grid. To better understand frequency events, including extrema, historic data were analyzed to fit probability distribution functions to various frequency metrics. Results showed that a standard Cauchy distribution fit the difference between the frequency nadir and prefault frequency (f_(C-A)) metric well, a standard Cauchy distribution fit the settling frequency (f_B) metric well, and a standard normal distribution fit the difference between the settling frequency and frequency nadir (f_(B-C)) metric very well. Results were inconclusive for the frequency nadir (f_C) metric, meaning it likely has a more complex distribution than those tested. This probabilistic modeling should facilitate more realistic modeling of grid faults.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Nov 01 00:00:00 EDT 2017},
month = {Wed Nov 01 00:00:00 EDT 2017}
}

Conference:
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