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Title: Contingency Analysis Post-Processing With Advanced Computing and Visualization

Abstract

Contingency analysis is a critical function widely used in energy management systems to assess the impact of power system component failures. Its outputs are important for power system operation for improved situational awareness, power system planning studies, and power market operations. With the increased complexity of power system modeling and simulation caused by increased energy production and demand, the penetration of renewable energy and fast deployment of smart grid devices, and the trend of operating grids closer to their capacity for better efficiency, more and more contingencies must be executed and analyzed quickly in order to ensure grid reliability and accuracy for the power market. Currently, many researchers have proposed different techniques to accelerate the computational speed of contingency analysis, but not much work has been published on how to post-process the large amount of contingency outputs quickly. This paper proposes a parallel post-processing function that can analyze contingency analysis outputs faster and display them in a web-based visualization tool to help power engineers improve their work efficiency by fast information digestion. Case studies using an ESCA-60 bus system and a WECC planning system are presented to demonstrate the functionality of the parallel post-processing technique and the web-based visualization tool.

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1406676
Report Number(s):
PNNL-SA-122202
Journal ID: ISSN 2405-8963; TE1103000
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Resource Relation:
Journal Name: IFAC-PapersOnLine; Journal Volume: 50; Journal Issue: 1
Country of Publication:
United States
Language:
English

Citation Formats

Chen, Yousu, Glaesemann, Kurt, and Fitzhenry, Erin. Contingency Analysis Post-Processing With Advanced Computing and Visualization. United States: N. p., 2017. Web. doi:10.1016/j.ifacol.2017.08.010.
Chen, Yousu, Glaesemann, Kurt, & Fitzhenry, Erin. Contingency Analysis Post-Processing With Advanced Computing and Visualization. United States. doi:10.1016/j.ifacol.2017.08.010.
Chen, Yousu, Glaesemann, Kurt, and Fitzhenry, Erin. Sat . "Contingency Analysis Post-Processing With Advanced Computing and Visualization". United States. doi:10.1016/j.ifacol.2017.08.010.
@article{osti_1406676,
title = {Contingency Analysis Post-Processing With Advanced Computing and Visualization},
author = {Chen, Yousu and Glaesemann, Kurt and Fitzhenry, Erin},
abstractNote = {Contingency analysis is a critical function widely used in energy management systems to assess the impact of power system component failures. Its outputs are important for power system operation for improved situational awareness, power system planning studies, and power market operations. With the increased complexity of power system modeling and simulation caused by increased energy production and demand, the penetration of renewable energy and fast deployment of smart grid devices, and the trend of operating grids closer to their capacity for better efficiency, more and more contingencies must be executed and analyzed quickly in order to ensure grid reliability and accuracy for the power market. Currently, many researchers have proposed different techniques to accelerate the computational speed of contingency analysis, but not much work has been published on how to post-process the large amount of contingency outputs quickly. This paper proposes a parallel post-processing function that can analyze contingency analysis outputs faster and display them in a web-based visualization tool to help power engineers improve their work efficiency by fast information digestion. Case studies using an ESCA-60 bus system and a WECC planning system are presented to demonstrate the functionality of the parallel post-processing technique and the web-based visualization tool.},
doi = {10.1016/j.ifacol.2017.08.010},
journal = {IFAC-PapersOnLine},
number = 1,
volume = 50,
place = {United States},
year = {Sat Jul 01 00:00:00 EDT 2017},
month = {Sat Jul 01 00:00:00 EDT 2017}
}