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Title: Resilient Distribution System by Microgrids Formation After Natural Disasters

Abstract

Microgrids with distributed generation provide a resilient solution in the case of major faults in a distribution system due to natural disasters. This paper proposes a novel distribution system operational approach by forming multiple microgrids energized by distributed generation from the radial distribution system in real-time operations, to restore critical loads from the power outage. Specifically, a mixed-integer linear program (MILP) is formulated to maximize the critical loads to be picked up while satisfying the self-adequacy and operation constraints for the microgrids formation problem, by controlling the ON/OFF status of the remotely controlled switch devices and distributed generation. A distributed multi-agent coordination scheme is designed via local communications for the global information discovery as inputs of the optimization, which is suitable for autonomous communication requirements after the disastrous event. The formed microgrids can be further utilized for power quality control and can be connected to a larger microgrid before the restoration of the main grids is complete. Numerical results based on modified IEEE distribution test systems validate the effectiveness of our proposed scheme.

Authors:
; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability
OSTI Identifier:
1391963
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Smart Grid
Additional Journal Information:
Journal Volume: 7; Journal Issue: 2; Journal ID: ISSN 1949-3053
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; Microgrids; distribution system; mixed-integer linear program; multi-agent coordination; resilience

Citation Formats

Chen, Chen, Wang, Jianhui, Qiu, Feng, and Zhao, Dongbo. Resilient Distribution System by Microgrids Formation After Natural Disasters. United States: N. p., 2016. Web. doi:10.1109/TSG.2015.2429653.
Chen, Chen, Wang, Jianhui, Qiu, Feng, & Zhao, Dongbo. Resilient Distribution System by Microgrids Formation After Natural Disasters. United States. doi:10.1109/TSG.2015.2429653.
Chen, Chen, Wang, Jianhui, Qiu, Feng, and Zhao, Dongbo. Tue . "Resilient Distribution System by Microgrids Formation After Natural Disasters". United States. doi:10.1109/TSG.2015.2429653.
@article{osti_1391963,
title = {Resilient Distribution System by Microgrids Formation After Natural Disasters},
author = {Chen, Chen and Wang, Jianhui and Qiu, Feng and Zhao, Dongbo},
abstractNote = {Microgrids with distributed generation provide a resilient solution in the case of major faults in a distribution system due to natural disasters. This paper proposes a novel distribution system operational approach by forming multiple microgrids energized by distributed generation from the radial distribution system in real-time operations, to restore critical loads from the power outage. Specifically, a mixed-integer linear program (MILP) is formulated to maximize the critical loads to be picked up while satisfying the self-adequacy and operation constraints for the microgrids formation problem, by controlling the ON/OFF status of the remotely controlled switch devices and distributed generation. A distributed multi-agent coordination scheme is designed via local communications for the global information discovery as inputs of the optimization, which is suitable for autonomous communication requirements after the disastrous event. The formed microgrids can be further utilized for power quality control and can be connected to a larger microgrid before the restoration of the main grids is complete. Numerical results based on modified IEEE distribution test systems validate the effectiveness of our proposed scheme.},
doi = {10.1109/TSG.2015.2429653},
journal = {IEEE Transactions on Smart Grid},
issn = {1949-3053},
number = 2,
volume = 7,
place = {United States},
year = {2016},
month = {3}
}