Multi-objective Snow Ablation Optimization Algorithm: An Elementary Vision for Security-Constrained Optimal Power Flow Problem Incorporating Wind Energy Source with FACTS Devices
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F24%3A10254335" target="_blank" >RIV/61989100:27230/24:10254335 - isvavai.cz</a>
Result on the web
<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001162488400001" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001162488400001</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s44196-024-00415-w" target="_blank" >10.1007/s44196-024-00415-w</a>
Alternative languages
Result language
angličtina
Original language name
Multi-objective Snow Ablation Optimization Algorithm: An Elementary Vision for Security-Constrained Optimal Power Flow Problem Incorporating Wind Energy Source with FACTS Devices
Original language description
This study delves into the exploration of a novel Multi-objective Snow Ablation Optimizer (MOSAO) algorithm, tailored for addressing expansive Optimal Power Flow (OPF) challenges inherent in intricate power systems. These systems are often complemented with the integration of renewable energy modalities and the state-of-the-art Flexible AC Transmission Systems (FACTS). Building upon the foundational framework of a previously documented single-objective Snow Ablation Optimizer, we have evolved it into the MOSAO paradigm. This transformation is achieved by harnessing the potency of non-dominated sorting coupled with the crowding distance strategy. The task of OPF magnifies in complexity when integrating renewable energy resources due to their inherent unpredictability and intermittent nature. As the modern power landscape evolves, FACTS devices are witnessing an increasing deployment to mitigate network demand and alleviate congestion issues. Within the ambit of this research, we've incorporated a stochastic wind energy source, working synergistically with an array of FACTS instruments. These encompass the static VAR compensator, thyristor-controlled series compensator and thyristor-driven phase shifter, all operating within the confines of an IEEE-30 bus framework. Strategic placement and calibration of these FACTS devices aim to optimize the system by minimizing the cumulative fuel expenditure. The capricious essence of wind as an energy source is elegantly depicted through the lens of Weibull probability density graphs. To distil the optimal middle-ground solutions, we've employed a fuzzy decision-making matrix. When benchmarking our findings against those derived from other esteemed optimization algorithms, we observe a notable distinction. The results from the modified IEEE-30 bus system accentuate the superior convergence, diversity and distribution attributes of MOSAO, especially when scrutinizing power flows. The MOSAO source code is available at: https://github.com/kanak02/MOSAO.
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
20300 - Mechanical engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Computational Intelligence Systems
ISSN
1875-6891
e-ISSN
1875-6883
Volume of the periodical
17
Issue of the periodical within the volume
1
Country of publishing house
FR - FRANCE
Number of pages
30
Pages from-to
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UT code for WoS article
001162488400001
EID of the result in the Scopus database
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