Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters

Lin Yi; Li Meng; Wu Wei; Xue Jingwei; Sun Jiawei; Wang Zekai; Ding Tao

doi:10.1109/ICPST56889.2023.10165500

Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters

Lin Yi
, Li Meng
, Wu Wei
, Xue Jingwei
, Sun Jiawei
, Wang Zekai
, Ding Tao

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Natural disasters with high risk and lower occurrence probability have attracted much more concern in recent years. In this paper, we proposed a distributionally robust resilience enhancement model for the distribution power system, in which the uncertainties of natural disasters are also taken into consideration. The ambiguity of the DRRM is constructed based on the branch outage probability, and the nested CCG algorithm is applied to solve the proposed model. The DRRM has been verified in the IEEE 33-bus distribution system. Case studies showed that the proposed model can reach a more effective and economic reinforcement strategy for the power distribution system.

Original language	English
Title of host publication	2023 IEEE International Conference on Power Science and Technology, ICPST 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	289-293
Number of pages	5
ISBN (Electronic)	9798350311358
DOIs	https://doi.org/10.1109/ICPST56889.2023.10165500
State	Published - 2023
Event	2023 IEEE International Conference on Power Science and Technology, ICPST 2023 - Kunming, China Duration: 5 May 2023 → 7 May 2023

Publication series

Name	2023 IEEE International Conference on Power Science and Technology, ICPST 2023

Conference

Conference	2023 IEEE International Conference on Power Science and Technology, ICPST 2023
Country/Territory	China
City	Kunming
Period	5/05/23 → 7/05/23

Keywords

column-and-constraint generation algorithm
distributionally robust approach
natural disasters
power distribution system resilience
reinforcement strategy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICPST56889.2023.10165500

Cite this

Yi, L., Meng, L., Wei, W., Jingwei, X., Jiawei, S., Zekai, W., & Tao, D. (2023). Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters. In 2023 IEEE International Conference on Power Science and Technology, ICPST 2023 (pp. 289-293). (2023 IEEE International Conference on Power Science and Technology, ICPST 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPST56889.2023.10165500

Yi, Lin ; Meng, Li ; Wei, Wu et al. / Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters. 2023 IEEE International Conference on Power Science and Technology, ICPST 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 289-293 (2023 IEEE International Conference on Power Science and Technology, ICPST 2023).

@inproceedings{37880656e24e4aaab8fbca7086100e49,

title = "Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters",

abstract = "Natural disasters with high risk and lower occurrence probability have attracted much more concern in recent years. In this paper, we proposed a distributionally robust resilience enhancement model for the distribution power system, in which the uncertainties of natural disasters are also taken into consideration. The ambiguity of the DRRM is constructed based on the branch outage probability, and the nested CCG algorithm is applied to solve the proposed model. The DRRM has been verified in the IEEE 33-bus distribution system. Case studies showed that the proposed model can reach a more effective and economic reinforcement strategy for the power distribution system.",

keywords = "column-and-constraint generation algorithm, distributionally robust approach, natural disasters, power distribution system resilience, reinforcement strategy",

author = "Lin Yi and Li Meng and Wu Wei and Xue Jingwei and Sun Jiawei and Wang Zekai and Ding Tao",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Power Science and Technology, ICPST 2023 ; Conference date: 05-05-2023 Through 07-05-2023",

year = "2023",

doi = "10.1109/ICPST56889.2023.10165500",

language = "英语",

series = "2023 IEEE International Conference on Power Science and Technology, ICPST 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "289--293",

booktitle = "2023 IEEE International Conference on Power Science and Technology, ICPST 2023",

}

Yi, L, Meng, L, Wei, W, Jingwei, X, Jiawei, S, Zekai, W & Tao, D 2023, Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters. in 2023 IEEE International Conference on Power Science and Technology, ICPST 2023. 2023 IEEE International Conference on Power Science and Technology, ICPST 2023, Institute of Electrical and Electronics Engineers Inc., pp. 289-293, 2023 IEEE International Conference on Power Science and Technology, ICPST 2023, Kunming, China, 5/05/23. https://doi.org/10.1109/ICPST56889.2023.10165500

Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters. / Yi, Lin; Meng, Li; Wei, Wu et al.
2023 IEEE International Conference on Power Science and Technology, ICPST 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 289-293 (2023 IEEE International Conference on Power Science and Technology, ICPST 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters

AU - Yi, Lin

AU - Meng, Li

AU - Wei, Wu

AU - Jingwei, Xue

AU - Jiawei, Sun

AU - Zekai, Wang

AU - Tao, Ding

PY - 2023

Y1 - 2023

N2 - Natural disasters with high risk and lower occurrence probability have attracted much more concern in recent years. In this paper, we proposed a distributionally robust resilience enhancement model for the distribution power system, in which the uncertainties of natural disasters are also taken into consideration. The ambiguity of the DRRM is constructed based on the branch outage probability, and the nested CCG algorithm is applied to solve the proposed model. The DRRM has been verified in the IEEE 33-bus distribution system. Case studies showed that the proposed model can reach a more effective and economic reinforcement strategy for the power distribution system.

AB - Natural disasters with high risk and lower occurrence probability have attracted much more concern in recent years. In this paper, we proposed a distributionally robust resilience enhancement model for the distribution power system, in which the uncertainties of natural disasters are also taken into consideration. The ambiguity of the DRRM is constructed based on the branch outage probability, and the nested CCG algorithm is applied to solve the proposed model. The DRRM has been verified in the IEEE 33-bus distribution system. Case studies showed that the proposed model can reach a more effective and economic reinforcement strategy for the power distribution system.

KW - column-and-constraint generation algorithm

KW - distributionally robust approach

KW - natural disasters

KW - power distribution system resilience

KW - reinforcement strategy

UR - https://www.scopus.com/pages/publications/85165641922

U2 - 10.1109/ICPST56889.2023.10165500

DO - 10.1109/ICPST56889.2023.10165500

M3 - 会议稿件

AN - SCOPUS:85165641922

T3 - 2023 IEEE International Conference on Power Science and Technology, ICPST 2023

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EP - 293

BT - 2023 IEEE International Conference on Power Science and Technology, ICPST 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE International Conference on Power Science and Technology, ICPST 2023

Y2 - 5 May 2023 through 7 May 2023

ER -

Yi L, Meng L, Wei W, Jingwei X, Jiawei S, Zekai W et al. Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters. In 2023 IEEE International Conference on Power Science and Technology, ICPST 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 289-293. (2023 IEEE International Conference on Power Science and Technology, ICPST 2023). doi: 10.1109/ICPST56889.2023.10165500

Distributionally Robust Resilience Enhancement Model for the Power Distribution System Considering the Uncertainty of Natural Disasters

Abstract

Publication series

Conference

Keywords

UN SDGs

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