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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Front. Eng    2021, Vol. 8 Issue (4) : 545-556    https://doi.org/10.1007/s42524-021-0161-5
RESEARCH ARTICLE
Improved resilience measure for component recovery priority in power grids
Guanghan BAI1, Han WANG2, Xiaoqian ZHENG3, Hongyan DUI3(), Min XIE4
1. Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
2. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China
3. School of Management Engineering, Zhengzhou University, Zhengzhou 450001, China
4. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong, China
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Abstract

Given the complexity of power grids, the failure of any component may cause large-scale economic losses. Consequently, the quick recovery of power grids after disasters has become a new research direction. Considering the severity of power grid disasters, an improved power grid resilience measure and its corresponding importance measures are proposed. The recovery priority of failed components after a disaster is determined according to the influence of the failed components on the power grid resilience. Finally, based on the data from the 2019 Power Yearbook of each city in Shandong Province, China, the power grid resilience after a disaster is analyzed for two situations, namely, partial components failure and failure of all components. Result shows that the recovery priorities of components with different importance measures vary. The resilience evaluations under different repair conditions prove the feasibility of the proposed method.

Keywords resilience measure      power grid      importance measure      component recovery     
Corresponding Author(s): Hongyan DUI   
Just Accepted Date: 08 May 2021   Online First Date: 08 June 2021    Issue Date: 01 November 2021
 Cite this article:   
Guanghan BAI,Han WANG,Xiaoqian ZHENG, et al. Improved resilience measure for component recovery priority in power grids[J]. Front. Eng, 2021, 8(4): 545-556.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-021-0161-5
https://academic.hep.com.cn/fem/EN/Y2021/V8/I4/545
Fig.1  Resilience process of power grid.
Fig.2  Simplified diagram of Shandong Power Grid.
Cities Electrical stations Capacities (kWh) Transmission lines
Heze S1 208.99 S1, S2 S1, D5
Jining S2 354.10 S2, S3 S2, D4 S2, D5 S2, D15
Rizhao S3 213.00 S3, D4 S3, D15
Linyi D4 484.40
Tai’an D5 198.38 D5, D4
Dezhou S6 177.70 S6, D5 S6, D11 S6, D12 S6, S13
Qingdao D7 431.82 D7, D4 D7, D9
Yantai S8 506.20 S8, D7
Weifang D9 536.24 D9, D4 D9, S10 D9, D14
Zibo S10 294.01 S10, D11
Liaocheng D11 301.71
Jinan D12 284.36 D12, S13
Binzhou S13 1210.90 S13, D14
Dongying D14 294.53
Zaozhuang D15 153.87 D15, D4
Weihai S16 158.87 S16, S8 S16, D7
Tab.1  Symbols and data for components in Shandong Power Grid
Fig.3   ICCRP, ICRRW, and ICRAW of failed components in Case 1.
Fig.4  Copeland score of failed components in Case 1.
Fig.5  Residual resilience with time in Case 1.
Fig.6   ICCRP and ICRRW of failed components in Case 2.
Fig.7  Copeland score of failed components in Case 2.
Fig.8  Residual resilience with time in Case 2.
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[1] Yan-Fu LI, Chuanzhou JIA. An overview of the reliability metrics for power grids and telecommunication networks[J]. Front. Eng, 2021, 8(4): 531-544.
[2] Shubin SI, Jiangbin ZHAO, Zhiqiang CAI, Hongyan DUI. Recent advances in system reliability optimization driven by importance measures[J]. Front. Eng, 2020, 7(3): 335-358.
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