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Title: Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

Abstract

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems, e.g., emergency asset management. Included is an example case study: Hurricane Dolly damaging a synthetic 2000-bus test system during its landing in Southern Texas. We perform statistical analysis of damages and discuss topological effects on the example synthetic grid. Also, we include a cursory evaluation of impacts using simplified operational models. Finally, we discuss how our method can be extended to use even higher-fidelity meteorological data sets and suggest directions for future work.

Authors:
; ; ; ; ;
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Science (SC); USDOE National Nuclear Security Administration (NNSA); Exascale Computing Project (ECP)
OSTI Identifier:
1884800
Report Number(s):
NREL/CP-2C00-83849
MainId:84622;UUID:e364bfca-ca22-4a91-832e-c892c30e287e;MainAdminID:65236
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the 2022 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), 25-28 April 2022, New Orleans, Louisiana; Related Information: 80639
Country of Publication:
United States
Language:
English
Subject:
POWER TRANSMISSION AND DISTRIBUTION,WIND ENERGY; contingencies; data-driven forecasting; fragility curve; hurricane damages; risk quantification; scenario-based optimization; transmission grid planning

Citation Formats

Satkauskas, Ignas, Maack, Jonathan, Reynolds, Matthew, Sigler, Devon, Panda, Kinshuk, and Jones, Wesley. Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources. United States: N. p., 2022. Web. doi:10.1109/TD43745.2022.9816884.
Satkauskas, Ignas, Maack, Jonathan, Reynolds, Matthew, Sigler, Devon, Panda, Kinshuk, & Jones, Wesley. Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources. United States. https://doi.org/10.1109/TD43745.2022.9816884
Satkauskas, Ignas, Maack, Jonathan, Reynolds, Matthew, Sigler, Devon, Panda, Kinshuk, and Jones, Wesley. 2022. "Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources". United States. https://doi.org/10.1109/TD43745.2022.9816884.
@article{osti_1884800,
title = {Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources},
author = {Satkauskas, Ignas and Maack, Jonathan and Reynolds, Matthew and Sigler, Devon and Panda, Kinshuk and Jones, Wesley},
abstractNote = {As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems, e.g., emergency asset management. Included is an example case study: Hurricane Dolly damaging a synthetic 2000-bus test system during its landing in Southern Texas. We perform statistical analysis of damages and discuss topological effects on the example synthetic grid. Also, we include a cursory evaluation of impacts using simplified operational models. Finally, we discuss how our method can be extended to use even higher-fidelity meteorological data sets and suggest directions for future work.},
doi = {10.1109/TD43745.2022.9816884},
url = {https://www.osti.gov/biblio/1884800}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2022},
month = {7}
}

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Works referenced in this record:

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