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Title: Resilience Enhancement of Distribution Grids Against Extreme Weather Events

This paper proposes a resilience-oriented design (ROD) technique to protect distribution grids against high-impact but low-probability extreme weather events. The problem is formulated as a two-stage stochastic mixed integer problem. The first stage is to make ROD decisions, i.e., hardening existing distribution lines and deploying ROD resources such as back-up distributed generators and automatic switches. The second stage evaluates the system operation cost during a realized extreme weather event and repair cost after the event. A novel modeling strategy is proposed to deal with the decision-dependent uncertainty of distribution line damage status, which is affected by the first-stage hardening decisions. As both stages have binary variables, a modified and computationally efficient progressive hedging algorithm with scenario bundling is introduced. The algorithm performance is evaluated by calculating lower bounds of solutions. In conclusion, the proposed model and algorithms are demonstrated on 34-bus and 123-bus test feeders.
Authors:
 [1] ;  [2] ; ORCiD logo [1] ; ORCiD logo [3] ; ORCiD logo [1]
  1. Iowa State Univ., Ames, IA (United States)
  2. Univ. of South Florida, Tampa, FL (United States)
  3. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 33; Journal Issue: 5; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Electricity Delivery and Energy Reliability (OE), Advanced Grid Research and Development
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; 97 MATHEMATICS AND COMPUTING; decision-dependent uncertainty; distribution systems; failure probability; progressive hedging; resilience-oriented design; scenario bundling; stochastic programming
OSTI Identifier:
1471588

Ma, Shanshan, Su, Liu, Wang, Zhaoyu, Qiu, Feng, and Guo, Ge. Resilience Enhancement of Distribution Grids Against Extreme Weather Events. United States: N. p., Web. doi:10.1109/TPWRS.2018.2822295.
Ma, Shanshan, Su, Liu, Wang, Zhaoyu, Qiu, Feng, & Guo, Ge. Resilience Enhancement of Distribution Grids Against Extreme Weather Events. United States. doi:10.1109/TPWRS.2018.2822295.
Ma, Shanshan, Su, Liu, Wang, Zhaoyu, Qiu, Feng, and Guo, Ge. 2018. "Resilience Enhancement of Distribution Grids Against Extreme Weather Events". United States. doi:10.1109/TPWRS.2018.2822295. https://www.osti.gov/servlets/purl/1471588.
@article{osti_1471588,
title = {Resilience Enhancement of Distribution Grids Against Extreme Weather Events},
author = {Ma, Shanshan and Su, Liu and Wang, Zhaoyu and Qiu, Feng and Guo, Ge},
abstractNote = {This paper proposes a resilience-oriented design (ROD) technique to protect distribution grids against high-impact but low-probability extreme weather events. The problem is formulated as a two-stage stochastic mixed integer problem. The first stage is to make ROD decisions, i.e., hardening existing distribution lines and deploying ROD resources such as back-up distributed generators and automatic switches. The second stage evaluates the system operation cost during a realized extreme weather event and repair cost after the event. A novel modeling strategy is proposed to deal with the decision-dependent uncertainty of distribution line damage status, which is affected by the first-stage hardening decisions. As both stages have binary variables, a modified and computationally efficient progressive hedging algorithm with scenario bundling is introduced. The algorithm performance is evaluated by calculating lower bounds of solutions. In conclusion, the proposed model and algorithms are demonstrated on 34-bus and 123-bus test feeders.},
doi = {10.1109/TPWRS.2018.2822295},
journal = {IEEE Transactions on Power Systems},
number = 5,
volume = 33,
place = {United States},
year = {2018},
month = {4}
}