Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
International Transactions on Electrical Energy Systems
ISSN
2050-7038
e-ISSN
—
Svazek periodika
31
Číslo periodika v rámci svazku
6
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
28
Strana od-do
1-28
Kód UT WoS článku
000639871700001
EID výsledku v databázi Scopus
2-s2.0-85104251290