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:
-
- Washington State Univ., Pullman, WA (United States). School of Electrical Engineering and Computer Science
- 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:
- Journal Article: 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. https://doi.org/10.1109/TIA.2018.2808483
Chanda, Sayonsom, Srivastava, Anurag K., Mohanpurkar, Manish U., and Hovsapian, Rob. 2018.
"Quantifying Power Distribution System Resiliency Using Code-Based Metric". United States. https://doi.org/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},
url = {https://www.osti.gov/biblio/1474076},
journal = {IEEE Transactions on Industry Applications},
issn = {0093-9994},
number = 4,
volume = 54,
place = {United States},
year = {Sun Jul 01 00:00:00 EDT 2018},
month = {Sun Jul 01 00:00:00 EDT 2018}
}
Web of Science