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Title: Quantifying Power Distribution System Resiliency Using Code-Based Metric

Abstract

It is essential to improve the resiliency of power distribution systems (PDS) with the increase in extreme weather events, number of malicious threats and consumers’ need for higher reliability. Here, this paper provides a formal approach to evaluate the operational resiliency of PDS, and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help with analyzing impact of short-term events, which might affect operational resiliency in long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple power distribution system model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected CERTS microgrids.

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]
  1. Washington State Univ., Pullman, WA (United States). School of Electrical Engineering and Computer Science
  2. Idaho National Lab. (INL), Idaho Falls, ID (United States). Power and Energy Systems Dept.
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE); USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1474076
Report Number(s):
INL/JOU-17-42049
Journal ID: ISSN 0093-9994
Grant/Contract Number:  
AC07-05ID14517
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Industry Applications
Additional Journal Information:
Journal Volume: 54; Journal Issue: 4; Journal ID: ISSN 0093-9994
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 42 ENGINEERING; Resilience; Power grids; Microgrids

Citation Formats

Chanda, Sayonsom, Srivastava, Anurag K., Mohanpurkar, Manish U., and Hovsapian, Rob. Quantifying Power Distribution System Resiliency Using Code-Based Metric. United States: N. p., 2018. Web. doi:10.1109/TIA.2018.2808483.
Chanda, Sayonsom, Srivastava, Anurag K., Mohanpurkar, Manish U., & Hovsapian, Rob. Quantifying Power Distribution System Resiliency Using Code-Based Metric. United States. doi:10.1109/TIA.2018.2808483.
Chanda, Sayonsom, Srivastava, Anurag K., Mohanpurkar, Manish U., and Hovsapian, Rob. Sun . "Quantifying Power Distribution System Resiliency Using Code-Based Metric". United States. doi:10.1109/TIA.2018.2808483. https://www.osti.gov/servlets/purl/1474076.
@article{osti_1474076,
title = {Quantifying Power Distribution System Resiliency Using Code-Based Metric},
author = {Chanda, Sayonsom and Srivastava, Anurag K. and Mohanpurkar, Manish U. and Hovsapian, Rob},
abstractNote = {It is essential to improve the resiliency of power distribution systems (PDS) with the increase in extreme weather events, number of malicious threats and consumers’ need for higher reliability. Here, this paper provides a formal approach to evaluate the operational resiliency of PDS, and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help with analyzing impact of short-term events, which might affect operational resiliency in long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple power distribution system model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected CERTS microgrids.},
doi = {10.1109/TIA.2018.2808483},
journal = {IEEE Transactions on Industry Applications},
number = 4,
volume = 54,
place = {United States},
year = {2018},
month = {7}
}

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