Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

Zhaoyuan Yin; Chao Fang; Haoxiang Yang; Yiping Fang; Min Xie

doi:10.1111/risa.13995

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

Zhaoyuan Yin, Chao Fang^*, Haoxiang Yang, Yiping Fang, Min Xie

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

8 Citations (Scopus)

63 Downloads (CityUHK Scholars)

Abstract

In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.

Original language	English
Pages (from-to)	979-993
Number of pages	15
Journal	Risk Analysis
Volume	43
Issue number	5
Online published	8 Jul 2022
DOIs	https://doi.org/10.1111/risa.13995
Publication status	Published - May 2023

Funding

This work is supported by National Natural Science Foundation of China (72171191, 71971181 and 72032005) and by Research Grant Council of Hong Kong (11203519, 11200621). It is also funded by the Natural Science Basic Research Program of Shaanxi under Grant 2021JM-026, Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).

Research Keywords

distributed energy resources
distributionally robust optimization
power grids
resilience analytics
typhoon hazards

Publisher's Copyright Statement

COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: This is the peer reviewed version of the following article: Yin, Z., Fang, C., Yang, H., Fang, Y., & Xie, M. (2023). Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization. Risk Analysis, 43(5), 979-993
which has been published in final form at https://doi.org/10.1111/risa.13995. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

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title = "Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization",

abstract = "In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.",

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author = "Zhaoyuan Yin and Chao Fang and Haoxiang Yang and Yiping Fang and Min Xie",

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AU - Yin, Zhaoyuan

AU - Fang, Chao

AU - Yang, Haoxiang

AU - Fang, Yiping

AU - Xie, Min

PY - 2023/5

Y1 - 2023/5

N2 - In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.

AB - In recent years, the increased frequency of natural hazards has led to more disruptions in power grids, potentially causing severe infrastructural damages and cascading failures. Therefore, it is important that the power system resilience be improved by implementing new technology and utilizing optimization methods. This paper proposes a data-driven spatial distributionally robust optimization (DS-DRO) model to provide an optimal plan to install and dispatch distributed energy resources (DERs) against the uncertain impact of natural hazards such as typhoons. We adopt an accurate spatial model to evaluate the failure probability with regard to system components based on wind speed. We construct a moment-based ambiguity set of the failure distribution based on historical typhoon data. A two-stage DS-DRO model is then formulated to obtain an optimal resilience enhancement strategy. We employ the combination of dual reformulation and a column-and-constraints generation algorithm, and showcase the effectiveness of the proposed approach with a modified IEEE 13-node reliability test system projected in the Hong Kong region.

KW - distributed energy resources

KW - distributionally robust optimization

KW - power grids

KW - resilience analytics

KW - typhoon hazards

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Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

Abstract

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Research Keywords

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GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation

GRF: Importance Analysis and Maintenance Decisions of Complex Systems with Dependent Components

Cite this

Improving the resilience of power grids against typhoons with data-driven spatial distributionally robust optimization

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

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Permanent Link

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GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation

GRF: Importance Analysis and Maintenance Decisions of Complex Systems with Dependent Components

Cite this