A novel clustering-based method for reliability assessment of cyber-physical microgrids considering cyber interdependencies and information transmission errors
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43965024" target="_blank" >RIV/49777513:23220/22:43965024 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0306261922004378" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0306261922004378</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.apenergy.2022.119032" target="_blank" >10.1016/j.apenergy.2022.119032</a>
Alternative languages
Result language
angličtina
Original language name
A novel clustering-based method for reliability assessment of cyber-physical microgrids considering cyber interdependencies and information transmission errors
Original language description
In recent years, the evolution of microgrids (MGs) toward a complex interacted cyber-physical system is significant, which has received much attention. Cyber-physical MGs (CPMGs) are vulnerable to various cyberphysical interdependencies mainly. Therefore, the reliability evaluation of such systems is a crucial issue. Several research works have focused on the reliability assessment of CPMGs in the literature. However, there is a research gap in developing fast and accurate models to assess the reliability of CPMGs, simultaneously considering cyber and physical failures, cyber-power interdependencies, and information transmission errors. This article aims to fill such a knowledge gap by developing a new clustering-based method to evaluate the reliability of CPMGs. The scenarios for information transmission errors are generated by the Monte Carlo simulation (MCS), besides uncertain physical parameters, e.g., the output power of renewable distributed generations (DGs). Afterward, the clustering algorithms are used to reduce the number of scenarios. In this paper, the k-means clustering algorithm has been selected to cluster the scenarios. The introduced clustering-based method is applied to a CPMG, and test results are compared to available MCS-based studies. The inaccuracy of the proposed clustering-based method is less than 3%, while its execution time is about 22 times faster than MCS-based ones. The comparative test results under various scenarios like available methods, neglecting the cyber failures and information errors illustrate the advantages of this research. The comparative test results illustrate the accuracy of the reliability calculations, while the execution time is desired.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
APPLIED ENERGY
ISSN
0306-2619
e-ISSN
1872-9118
Volume of the periodical
315
Issue of the periodical within the volume
June 2022
Country of publishing house
GB - UNITED KINGDOM
Number of pages
21
Pages from-to
1-21
UT code for WoS article
000793663600003
EID of the result in the Scopus database
2-s2.0-85127122024