LAPSE:2023.34115
Published Article
LAPSE:2023.34115
Topological Modeling Research on the Functional Vulnerability of Power Grid under Extreme Weather
Banghua Xie, Changfan Li, Zili Wu, Weiming Chen
April 24, 2023
The large-scale interconnection of the power grid has brought great benefits to social development, but simultaneously, the frequency of large-scale fault accidents caused by extreme weather is also rocketing. The power grid is regarded as a representative complex network in this paper to analyze its functional vulnerability. First, the actual power grid topology is modeled on the basis of the complex network theory, which is transformed into a directed-weighted topology model after introducing the node voltage together with line reactance. Then, the algorithm of weighted reactance betweenness is proposed by analyzing the characteristic parameters of the power grid topology model. The product of unit reliability and topology model’s characteristic parameters under extreme weather is used as the index to measure the functional vulnerability of the power grid, which considers the extreme weather of freezing and gale and quantifies the functional vulnerability of lines under wind load, ice load, and their synergistic effects. Finally, a simulation using the IEEE-30 node system is implemented. The result shows that the proposed method can effectively measure the short-term vulnerability of power grid units under extreme weather. Meanwhile, the example analysis verifies the different effects of normal and extreme weather on the power grid and identifies the nodes and lines with high vulnerability under extreme weather, which provides theoretical support for preventing and reducing the impact of extreme weather on the power grid.
Keywords
complex network, extreme weather, functional vulnerability, reliability, topology modeling
Suggested Citation
Xie B, Li C, Wu Z, Chen W. Topological Modeling Research on the Functional Vulnerability of Power Grid under Extreme Weather. (2023). LAPSE:2023.34115
Author Affiliations
Xie B: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Li C: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Wu Z: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
Chen W: Faculty of Engineering, China University of Geosciences, Wuhan 430074, China [ORCID]
Journal Name
Energies
Volume
14
Issue
16
First Page
5183
Year
2021
Publication Date
2021-08-22
Published Version
ISSN
1996-1073
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PII: en14165183, Publication Type: Journal Article
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doi:10.3390/en14165183
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