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https://hdl.handle.net/10356/180264
Title: | A tri-level typhoon-DAD robust optimization framework to enhance distribution network resilience | Authors: | Hou, Hui Wu, Wenjie Zhang, Zhiwei Wei, Ruizeng Wang, Lei He, Huan Dong, Zhao Yang |
Keywords: | Engineering | Issue Date: | 2024 | Source: | Hou, H., Wu, W., Zhang, Z., Wei, R., Wang, L., He, H. & Dong, Z. Y. (2024). A tri-level typhoon-DAD robust optimization framework to enhance distribution network resilience. Reliability Engineering and System Safety, 245, 110004-. https://dx.doi.org/10.1016/j.ress.2024.110004 | Project: | RG59/22 RT9/22 |
Journal: | Reliability Engineering and System Safety | Abstract: | 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. | URI: | https://hdl.handle.net/10356/180264 | ISSN: | 0951-8320 | DOI: | 10.1016/j.ress.2024.110004 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2024 Elsevier Ltd. All rights reserved. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Journal Articles |
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