A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F22%3A10250917" target="_blank" >RIV/61989100:27230/22:10250917 - isvavai.cz</a>
Result on the web
<a href="https://mdpi-res.com/d_attachment/electronics/electronics-11-03825/article_deploy/electronics-11-03825.pdf?version=1669022174" target="_blank" >https://mdpi-res.com/d_attachment/electronics/electronics-11-03825/article_deploy/electronics-11-03825.pdf?version=1669022174</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/electronics11223825" target="_blank" >10.3390/electronics11223825</a>
Alternative languages
Result language
angličtina
Original language name
A Novel MOGNDO Algorithm for Security-Constrained Optimal Power Flow Problems
Original language description
The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor-driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system's overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs. (C) 2022 by the authors.
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
20301 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
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
Electronics
ISSN
2079-9292
e-ISSN
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Volume of the periodical
11
Issue of the periodical within the volume
22
Country of publishing house
CH - SWITZERLAND
Number of pages
34
Pages from-to
nestrankovano
UT code for WoS article
000887102300001
EID of the result in the Scopus database
2-s2.0-85142418821