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Title: A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty

Abstract

Dynamic security assessment (DSA) is a critical function to evaluate power grids’ capability to survive the transition caused by a set of disturbances to an acceptable steady-state condition. Its computational burden is heavy. With the challenges brought by renewable energy and new smart grid technologies, DSA under uncertainty has to be considered to study the impact of forecast errors on DSA simulation, which further increases the computational burden. To address this challenge, this paper presents a high performance computational framework to support DSA simulation user uncertainty. The computational framework aims to provide a seamless workflow that links data from high performance computing, statistical analysis, to visualization so that a problem can be easily expressed in a way compatible with different functions. It also enables software compatibility such that application development can be more efficient. Case study results of the ESCA60 system and a western U.S. size system show the advantages and efficiency of the computational framework.

Authors:
 [1]; ORCiD logo [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1532496
Report Number(s):
PNNL-SA-138783
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE Electronic Power Grid (eGrid 2018), November 12-14, 2018, Charleston, SC
Country of Publication:
United States
Language:
English
Subject:
dynamic security assessment, high performance comptuing, uncertainty, smart sampling, middleware, visualization

Citation Formats

Chen, Yousu, Palmer, Bruce J., Sharma, Poorva, Yuan, Yong, Mathew, Bibi, and Huang, Zhenyu. A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty. United States: N. p., 2018. Web. doi:10.1109/eGRID.2018.8598684.
Chen, Yousu, Palmer, Bruce J., Sharma, Poorva, Yuan, Yong, Mathew, Bibi, & Huang, Zhenyu. A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty. United States. doi:10.1109/eGRID.2018.8598684.
Chen, Yousu, Palmer, Bruce J., Sharma, Poorva, Yuan, Yong, Mathew, Bibi, and Huang, Zhenyu. Mon . "A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty". United States. doi:10.1109/eGRID.2018.8598684.
@article{osti_1532496,
title = {A High Performance Computational Framework for Dynamic Security Assessment under Uncertainty},
author = {Chen, Yousu and Palmer, Bruce J. and Sharma, Poorva and Yuan, Yong and Mathew, Bibi and Huang, Zhenyu},
abstractNote = {Dynamic security assessment (DSA) is a critical function to evaluate power grids’ capability to survive the transition caused by a set of disturbances to an acceptable steady-state condition. Its computational burden is heavy. With the challenges brought by renewable energy and new smart grid technologies, DSA under uncertainty has to be considered to study the impact of forecast errors on DSA simulation, which further increases the computational burden. To address this challenge, this paper presents a high performance computational framework to support DSA simulation user uncertainty. The computational framework aims to provide a seamless workflow that links data from high performance computing, statistical analysis, to visualization so that a problem can be easily expressed in a way compatible with different functions. It also enables software compatibility such that application development can be more efficient. Case study results of the ESCA60 system and a western U.S. size system show the advantages and efficiency of the computational framework.},
doi = {10.1109/eGRID.2018.8598684},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
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