The Non-Simulation Resilience Assessment for Electric–Gas Distribution Networks

Unlike traditional power systems, the heterogeneous energy support of electric–gas regional distribution networks brings new challenges to resilience assessment. On the basis of identifying N-k fault uncertainty risks, establishing a resilience assessment methodology is one of the important issues in resilience research. Existing reliability assessment methods cannot accurately quantify resilience under N-k extreme fault scenarios. To address this limitation, we propose a non-simulation resilience assessment method. The approach can simultaneously quantify the dynamic interactions of heterogeneous energy flows and the impact of repair process time uncertainty on system resilience under extreme fault scenarios. Specifically, the resilience indexes are established by combining the load outage and mathematical expectation during/after the extreme fault and applying probabilistic knowledge to express the N-k load outage event, so as to effectively offset the data scarcity due to the limited N-k fault data samples. The internal consistency and parametric responsiveness of the proposed non-simulation method are demonstrated through systematic case comparisons under varying failure rates, repair times, and coupling conditions.

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Publication Details

Journal
Algorithms
Published
2026-03-05
DOI
https://doi.org/10.3390/a19030196
Primary Topic
Optimal Power Flow Distribution
Type
article
Field-Weighted Citation Impact
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article

The Non-Simulation Resilience Assessment for Electric–Gas Distribution Networks

Tingjun Li, Chun Xiao, Xiaoqing Han
Algorithms
Optimal Power Flow Distribution
article

The Non-Simulation Resilience Assessment for Electric–Gas Distribution Networks

Tingjun Li, Chun Xiao, Xiaoqing Han
article en

Abstract

Unlike traditional power systems, the heterogeneous energy support of electric–gas regional distribution networks brings new challenges to resilience assessment. On the basis of identifying N-k fault uncertainty risks, establishing a resilience assessment methodology is one of the important issues in resilience research. Existing reliability assessment methods cannot accurately quantify resilience under N-k extreme fault scenarios. To address this limitation, we propose a non-simulation resilience assessment method. The approach can simultaneously quantify the dynamic interactions of heterogeneous energy flows and the impact of repair process time uncertainty on system resilience under extreme fault scenarios. Specifically, the resilience indexes are established by combining the load outage and mathematical expectation during/after the extreme fault and applying probabilistic knowledge to express the N-k load outage event, so as to effectively offset the data scarcity due to the limited N-k fault data samples. The internal consistency and parametric responsiveness of the proposed non-simulation method are demonstrated through systematic case comparisons under varying failure rates, repair times, and coupling conditions.

AlgorithmsVol. 19(3)
State Grid Corporation of China (China) (CN), Taiyuan University of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 34%
Optimal Power Flow Distribution
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