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Title: A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding

Abstract

Resilience is an overarching concept that requires combined efforts from interdependent critical infrastructures to achieve. As the interdependence between the power system and the natural gas system grows, the roles of coordination in post-disaster repair will be unneglectable to improve the resilience of the two systems. In this paper, a combined repair crew dispatch problem for the interdependent power and natural gas systems is proposed. The repair schedule of the two systems is coordinated and co-optimized. Both power system topology reconfiguration and intentional DG islanding are modeled as operational measures to further improve the resilience of the interdependent systems. Case studies validate the effectiveness of the proposed method in reducing load shedding and repair duration, and prove that the interdependence has a significant impact on the repair sequence and crew coordination.

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [1]
  1. Xi'an Jiaotong Univ. (China)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Southern Methodist Univ., Dallas, TX (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); National Natural Science Foundation of China (NSFC)
OSTI Identifier:
1737394
Alternate Identifier(s):
OSTI ID: 1737393
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 34; Journal Issue: 4; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; Critical infrastructure; extreme event; natural gas system; power system; resilience

Citation Formats

Lin, Yanling, Chen, Bo, Wang, Jianhui, and Bie, Zhaohong. A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding. United States: N. p., 2019. Web. doi:10.1109/tpwrs.2019.2895198.
Lin, Yanling, Chen, Bo, Wang, Jianhui, & Bie, Zhaohong. A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding. United States. https://doi.org/10.1109/tpwrs.2019.2895198
Lin, Yanling, Chen, Bo, Wang, Jianhui, and Bie, Zhaohong. Thu . "A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding". United States. https://doi.org/10.1109/tpwrs.2019.2895198. https://www.osti.gov/servlets/purl/1737394.
@article{osti_1737394,
title = {A Combined Repair Crew Dispatch Problem for Resilient Electric and Natural Gas System Considering Reconfiguration and DG Islanding},
author = {Lin, Yanling and Chen, Bo and Wang, Jianhui and Bie, Zhaohong},
abstractNote = {Resilience is an overarching concept that requires combined efforts from interdependent critical infrastructures to achieve. As the interdependence between the power system and the natural gas system grows, the roles of coordination in post-disaster repair will be unneglectable to improve the resilience of the two systems. In this paper, a combined repair crew dispatch problem for the interdependent power and natural gas systems is proposed. The repair schedule of the two systems is coordinated and co-optimized. Both power system topology reconfiguration and intentional DG islanding are modeled as operational measures to further improve the resilience of the interdependent systems. Case studies validate the effectiveness of the proposed method in reducing load shedding and repair duration, and prove that the interdependence has a significant impact on the repair sequence and crew coordination.},
doi = {10.1109/tpwrs.2019.2895198},
journal = {IEEE Transactions on Power Systems},
number = 4,
volume = 34,
place = {United States},
year = {Thu Jan 24 00:00:00 EST 2019},
month = {Thu Jan 24 00:00:00 EST 2019}
}