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A tri-level Typhoon-DAD robust optimization framework to enhance distribution network resilience.
- Source :
-
Reliability Engineering & System Safety . May2024, Vol. 245, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- • A novel T-DAD model is proposed for typhoon disaster scenario. • Besides line hardening, we incorporate DG UC into T-DAD's 1st level. • UC and reconfiguration strategies are extended to 24 h instead of load peak periods. • We simulate typhoon "Mangkhut" (2018) with considering more resilience elements. Extreme natural disasters such as typhoon often cause failures in distribution networks within a short time, and even lead to large area blackouts. We propose a tri-level robust optimization framework that combines pre-disaster, in-disaster and post-disaster strategy comprehensively to enhance distribution network resilience. The typhoons are regarded as attackers, and a tri-level Defender-Attacker-Defender model for typhoon disaster is used to integrate multiple resilience resources. In the first level, line hardening and distributed generation unit commitment are used to improve resilience in pre-disaster. The second level contains attack budget, attack time, hardening budget, repair time and load loss. It couples with the third level iteratively to generate the worst failure scenario under typhoon. In the third level, the Nested Column-and-Constraint Generation algorithm is used to solve distribution network reconfiguration and intentional islanding. Simulation time is spanned to the entire 24-hour disaster day. In the end, the proposed framework is tested through a case study using real data from super typhoon "Mangkhut" (2018) in Yangjiang, China. The result shows that it can effectively reduce load loss under the worst typhoon scenario and enhance distribution network resilience with limited resources. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09518320
- Volume :
- 245
- Database :
- Academic Search Index
- Journal :
- Reliability Engineering & System Safety
- Publication Type :
- Academic Journal
- Accession number :
- 175873247
- Full Text :
- https://doi.org/10.1016/j.ress.2024.110004