Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141192
Title: Optimal skeleton-network restoration considering generator start-up sequence and load pickup
Authors: Sun, Lei
Lin, Zhenzhi
Xu, Yan
Wen, Fushuan
Zhang, Can
Xue, Yusheng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Sun, L., Lin, Z., Xu, Y., Wen, F., Zhang, C., & Xue, Y. (2019). Optimal skeleton-network restoration considering generator start-up sequence and load pickup. IEEE Transactions on Smart Grid, 10(3), 3174-3185. doi:10.1109/TSG.2018.2820012
Journal: IEEE Transactions on Smart Grid
Abstract: Power system restoration comprises of three stages, i.e., the preparation stage, system restoration stage, and load restoration stage. This paper addresses the first two stages by a new skeleton-network restoration strategy considering the generator start-up sequence (GSUS) and load pickup. The proposed restoration strategy consists of three mathematical models: 1) GSUS model; 2) transmission line restoration (TLR) model; and 3) load pickup (LDP) model. The GSUS model aims to maximize the overall system generated energy considering the actual power system topology. The TLR model is to attain an optimal skeleton-network by identifying the restoration sequence of transmission lines. Critical loads are restored to keep the voltage within a specified respective threshold, and the load amounts are optimized in the LDP model. The proposed GSUS model and TLR model are both solved by the branch-and-bound method, while the LDR model is solved by the interior point method as well as the branch-and-cut method. Finally, the performance of the proposed method is demonstrated by two case studies on the New England 10-unit 39-bus system and an actual power system in Guangdong, China, respectively.
URI: https://hdl.handle.net/10356/141192
ISSN: 1949-3053
DOI: 10.1109/TSG.2018.2820012
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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