Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021393" target="_blank" >RIV/62690094:18470/24:50021393 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-024-54510-1" target="_blank" >https://www.nature.com/articles/s41598-024-54510-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-54510-1" target="_blank" >10.1038/s41598-024-54510-1</a>

Result language

angličtina

Original language name

Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power

Original language description

This study introduces an enhanced self-adaptive wild goose algorithm (SAWGA) for solving economical-environmental-technical optimal power flow (OPF) problems in traditional and modern energy systems. Leveraging adaptive search strategies and robust diversity capabilities, SAWGA distinguishes itself from classical WGA by incorporating four potent optimizers. The algorithm&apos;s application to optimize an OPF model on the different IEEE 30-bus and 118-bus electrical networks, featuring conventional thermal power units alongside solar photovoltaic (PV) and wind power (WT) units, addresses the rising uncertainties in operating conditions, particularly with the integration of renewable energy sources (RESs). The inherent complexity of OPF problems in electrical networks, exacerbated by the inclusion of RESs like PV and WT units, poses significant challenges. Traditional optimization algorithms struggle due to the problem&apos;s high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study&apos;s simulation results underscore the efficacy of SAWGA in achieving optimal solutions for OPF, notably reducing overall fuel consumption costs in a faster and more efficient convergence. Noteworthy attributes of SAWGA include its remarkable capabilities in optimizing various objective functions, effective management of OPF challenges, and consistent outperformance compared to traditional WGA and other modern algorithms. The method exhibits a robust ability to achieve global or nearly global optimal settings for decision parameters, emphasizing its superiority in total cost reduction and rapid convergence.

Czech name

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Czech description

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Type

J_imp - 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

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

28

Pages from-to

"Article number: 4135"

UT code for WoS article

001167137100054

EID of the result in the Scopus database

2-s2.0-85185476260