Two-Stage Distributionally Robust Optimization for Infrastructure Resilience Enhancement: A Case Study of 220 kV Power Substations Under Earthquake Disasters

Power substations are widely used as crucial components of power grids but also exposed to a high risk of earthquakes. Enhancing the resilience of power substations plays an important role for power grids to resist earthquakes. However, the uncertainty of equipment failure has posed a significant obstacle to enhancing resilience. In this article, considering the equipment hardening strategy for the power substation, a two-stage distributionally robust optimization model is put forth for earthquake resilience enhancement of the power substations. The optimal equipment hardening strategy is determined prior to an earthquake, and the recovery sequence of the damaged equipment is optimized after the earthquake to enhance the resilience of the power substation. A decision-dependent moment-based ambiguity set is constructed to model the impact of the hardening strategy on the uncertain failure probability of equipment. A customized nested column-and-constraint generation algorithm (NC&CG) is put forth to solve the optimization model. To improve the computational efficiency, we propose a novel heuristic algorithm to solve the main problem of the inner NC&CG according to the power substation structure. The proposed method is demonstrated in the case of a simplified 220 kV power substation. The results show that the distributionally robust approach can effectively enhance the earthquake resilience of power infrastructure under uncertainty of equipment failure. The value of implementing an effective algorithm to quickly obtain the optimal solution in large-scale problems is also highlighted.

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Publication Details

Journal
IEEE Transactions on Reliability
Published
2024-09-17
DOI
https://doi.org/10.1109/tr.2024.3450107
Citations
6
Primary Topic
Power System Reliability and Maintenance
Type
article
Field-Weighted Citation Impact
2.19

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article

Two-Stage Distributionally Robust Optimization for Infrastructure Resilience Enhancement: A Case Study of 220 kV Power Substations Under Earthquake Disasters

Changjie Zou, Yu Liu, Tangfan Xiahou, Di Cao et al.
6 citations
IEEE Transactions on Reliability
Power System Reliability and Maintenance
2.19
article

Two-Stage Distributionally Robust Optimization for Infrastructure Resilience Enhancement: A Case Study of 220 kV Power Substations Under Earthquake Disasters

Changjie Zou, Yu Liu, Tangfan Xiahou, Di Cao, Kai Wang
article en
6 citations

Abstract

Power substations are widely used as crucial components of power grids but also exposed to a high risk of earthquakes. Enhancing the resilience of power substations plays an important role for power grids to resist earthquakes. However, the uncertainty of equipment failure has posed a significant obstacle to enhancing resilience. In this article, considering the equipment hardening strategy for the power substation, a two-stage distributionally robust optimization model is put forth for earthquake resilience enhancement of the power substations. The optimal equipment hardening strategy is determined prior to an earthquake, and the recovery sequence of the damaged equipment is optimized after the earthquake to enhance the resilience of the power substation. A decision-dependent moment-based ambiguity set is constructed to model the impact of the hardening strategy on the uncertain failure probability of equipment. A customized nested column-and-constraint generation algorithm (NC&CG) is put forth to solve the optimization model. To improve the computational efficiency, we propose a novel heuristic algorithm to solve the main problem of the inner NC&CG according to the power substation structure. The proposed method is demonstrated in the case of a simplified 220 kV power substation. The results show that the distributionally robust approach can effectively enhance the earthquake resilience of power infrastructure under uncertainty of equipment failure. The value of implementing an effective algorithm to quickly obtain the optimal solution in large-scale problems is also highlighted.

IEEE Transactions on ReliabilityVol. 74(2)
University of Electronic Science and Technology of China (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 13%
Power System Reliability and Maintenance
2.19
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