Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43962523" target="_blank" >RIV/49777513:23220/21:43962523 - isvavai.cz</a>
Result on the web
<a href="https://onlinelibrary.wiley.com/doi/full/10.1002/2050-7038.12902" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1002/2050-7038.12902</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/2050-7038.12902" target="_blank" >10.1002/2050-7038.12902</a>
Alternative languages
Result language
angličtina
Original language name
Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties
Original language description
The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids' reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method's accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper, the performance and accuracy of various classic (k-means, fuzzy c-means, and k-medoids) and metaheuristic (genetic algorithm, particle swarm optimization, differential evolutionary, harmony search, and artificial bee colony) clustering algorithms are studied. Comparing different scenario reduction algorithms in the proposed reliability evaluation method is one of the most contributions. he proposed method is applied to two realistic test systems. Test results infer that the proposed method is adequately precise, while the required computation time is less than MCS-based approaches. Test results for both test systems imply that the accurate expected energy not supplied (EENS) with less than 2.1% is achievable applying the proposed method. The fuzzy c-means clustering algorithm results in the best accuracy among the studied classic and nonclassic(metaheuristic) algorithms.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Transactions on Electrical Energy Systems
ISSN
2050-7038
e-ISSN
—
Volume of the periodical
31
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
28
Pages from-to
1-28
UT code for WoS article
000639871700001
EID of the result in the Scopus database
2-s2.0-85104251290