Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power
Abstract 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 distinguishe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-54510-1 |
| _version_ | 1797274773978349568 |
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| author | Pavel Trojovský Eva Trojovská Ebrahim Akbari |
| author_facet | Pavel Trojovský Eva Trojovská Ebrahim Akbari |
| author_sort | Pavel Trojovský |
| collection | DOAJ |
| description | Abstract 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'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's high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study'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. |
| first_indexed | 2024-03-07T15:03:31Z |
| format | Article |
| id | doaj.art-9bab3baa54d04419b74e1100b6b9d333 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:03:31Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-9bab3baa54d04419b74e1100b6b9d3332024-03-05T19:01:38ZengNature PortfolioScientific Reports2045-23222024-02-0114112810.1038/s41598-024-54510-1Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar powerPavel Trojovský0Eva Trojovská1Ebrahim Akbari2Department of Mathematics, Faculty of Science, University of Hradec KrálovéDepartment of Mathematics, Faculty of Science, University of Hradec KrálovéDepartment of Mathematics, Faculty of Science, University of Hradec KrálovéAbstract 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'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's high complexity, susceptibility to local optima, and numerous continuous and discrete decision parameters. The study'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.https://doi.org/10.1038/s41598-024-54510-1Economical-environmental-technical dispatch problemElectrical networksRenewable energy sources (RESs)Optimal power flow (OPF)Self-adaptive wild geese algorithm (SAWGA)OPF optimization functions |
| spellingShingle | Pavel Trojovský Eva Trojovská Ebrahim Akbari Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power Scientific Reports Economical-environmental-technical dispatch problem Electrical networks Renewable energy sources (RESs) Optimal power flow (OPF) Self-adaptive wild geese algorithm (SAWGA) OPF optimization functions |
| title | Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power |
| title_full | Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power |
| title_fullStr | Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power |
| title_full_unstemmed | Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power |
| title_short | Economical-environmental-technical optimal power flow solutions using a novel self-adaptive wild geese algorithm with stochastic wind and solar power |
| title_sort | economical environmental technical optimal power flow solutions using a novel self adaptive wild geese algorithm with stochastic wind and solar power |
| topic | Economical-environmental-technical dispatch problem Electrical networks Renewable energy sources (RESs) Optimal power flow (OPF) Self-adaptive wild geese algorithm (SAWGA) OPF optimization functions |
| url | https://doi.org/10.1038/s41598-024-54510-1 |
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