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Title: Toward a MILP Modeling Framework for Distribution System Restoration

Abstract

Large-scale blackouts and extreme weather events in recent decades raise the concern for improving the resilience of electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems, allows restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR framework is critical to perform optimally coordinated restoration that can achieve maximal restoration performance. This work introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable time step model and a fixed time step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model, and their effectiveness is evaluated via the IEEE 123 node test feeder.

Authors:
 [1];  [2]; ORCiD logo [1]; ORCiD logo [3]
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Xi’an Jiaotong Univ., Xi’an (China); 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), Solar Energy Technologies Office (EE-4S)
OSTI Identifier:
1507794
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Power Systems
Additional Journal Information:
Journal Volume: 34; Journal Issue: 3; Journal ID: ISSN 0885-8950
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; Load modeling; Mathematical model; Adaptation models; Regulators; Power system reliability; Distribution system; distributed generation; mixed-integer linear programming; service restoration; switching sequence management

Citation Formats

Chen, Bo, Ye, Zhigang, Chen, Chen, and Wang, Jianhui. Toward a MILP Modeling Framework for Distribution System Restoration. United States: N. p., 2018. Web. doi:10.1109/TPWRS.2018.2885322.
Chen, Bo, Ye, Zhigang, Chen, Chen, & Wang, Jianhui. Toward a MILP Modeling Framework for Distribution System Restoration. United States. doi:10.1109/TPWRS.2018.2885322.
Chen, Bo, Ye, Zhigang, Chen, Chen, and Wang, Jianhui. Mon . "Toward a MILP Modeling Framework for Distribution System Restoration". United States. doi:10.1109/TPWRS.2018.2885322.
@article{osti_1507794,
title = {Toward a MILP Modeling Framework for Distribution System Restoration},
author = {Chen, Bo and Ye, Zhigang and Chen, Chen and Wang, Jianhui},
abstractNote = {Large-scale blackouts and extreme weather events in recent decades raise the concern for improving the resilience of electric power infrastructures. Distribution service restoration (DSR), a fundamental application in outage management systems, allows restoration solutions for system operators when power outages happen. As distribution generators (DGs) and remotely controllable devices are increasingly installed in distribution systems, an advanced DSR framework is critical to perform optimally coordinated restoration that can achieve maximal restoration performance. This work introduces a DSR modeling framework, which can generate optimal switching sequences and estimated time of restoration in the presence of remotely controllable switches, manually operated switches, and dispatchable DGs. Two mathematical models, a variable time step model and a fixed time step model, are presented and compared. The proposed models are formulated as a mixed-integer linear programming model, and their effectiveness is evaluated via the IEEE 123 node test feeder.},
doi = {10.1109/TPWRS.2018.2885322},
journal = {IEEE Transactions on Power Systems},
number = 3,
volume = 34,
place = {United States},
year = {2018},
month = {12}
}

Journal Article:
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