# 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:

- 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}

}