Dui, Hongyan, Li, Heyuan, Dong, Xinghui, Wu, Shaomin (2025) An energy IoT-driven multi-dimension resilience methodology of smart microgrids. Reliability Engineering and System Safety, 253 . Article Number 110533. ISSN 0951-8320. E-ISSN 1879-0836. (doi:10.1016/j.ress.2024.110533) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:107487)
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Official URL: https://doi.org/10.1016/j.ress.2024.110533 |
Abstract
Smart microgrids are significant in promoting clean energy development and improving microgrid security and reliability. However, harsh environments make them exposed to various hazards, including natural hazards such as hail and wildfire and digital hazards such as cyberattacks. Due to these complex challenges, performing performance evaluation and resilience analysis for smart microgrids in different periods (e.g., before, during, and after the hazards) and different layers (e.g., a data layer and a physical layer) is difficult. To reduce this research gap, this paper develops a new multi-layer failure and multi-dimension resilience methodology in the energy Internet of Things (IoT). This paper analyses a multi-layer failure mechanism of smart microgrids in energy IoT with the synergy of the “physical layer, perception layer, communication layer, and application layer”, establishes a multi-stage performance model for smart microgrids based on operation loops, and develops a multi-dimension resilience methodology for smart microgrids with consideration of four performance evolution processes (i.e., prevention, degradation, restoration, and reconstitution). A case adopted from the Shandong province in China is used to demonstrate the proposed method under normal operating conditions and different types of disasters.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.ress.2024.110533 |
Uncontrolled keywords: | multi-dimension resilience; multi-layer failure; multi-stage performance; energy IoT; smart microgrids |
Subjects: | H Social Sciences > HA Statistics |
Divisions: | Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems |
Depositing User: | Shaomin Wu |
Date Deposited: | 10 Oct 2024 08:06 UTC |
Last Modified: | 13 Nov 2024 13:37 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/107487 (The current URI for this page, for reference purposes) |
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