A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources
Abstract This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergenc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53249-z |
| _version_ | 1797275221716107264 |
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| author | Mohamed Farhat Salah Kamel Mohamed A. Elseify Almoataz Y. Abdelaziz |
| author_facet | Mohamed Farhat Salah Kamel Mohamed A. Elseify Almoataz Y. Abdelaziz |
| author_sort | Mohamed Farhat |
| collection | DOAJ |
| description | Abstract This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties. |
| first_indexed | 2024-03-07T15:10:16Z |
| format | Article |
| id | doaj.art-96079201e0794c7f85b589e92af8d3d0 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:10:16Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-96079201e0794c7f85b589e92af8d3d02024-03-05T18:40:46ZengNature PortfolioScientific Reports2045-23222024-02-0114113510.1038/s41598-024-53249-zA modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sourcesMohamed Farhat0Salah Kamel1Mohamed A. Elseify2Almoataz Y. Abdelaziz3Electrical Power and Machines Engineering Department, Faculty of Engineering, Ain Shams UniversityDepartment of Electrical Engineering, Faculty of Engineering, Aswan UniversityDepartment of Electrical Engineering, Faculty of Engineering, Al-Azhar UniversityFaculty of Engineering and Technology, Future University in EgyptAbstract This paper presents a novel approach to solve the optimal power flow (OPF) problem by utilizing a modified white shark optimization (MWSO) algorithm. The MWSO algorithm incorporates the Gaussian barebones (GB) and quasi-oppositional-based learning (QOBL) strategies to improve the convergence rate and accuracy of the original WSO algorithm. To address the uncertainty associated with renewable energy sources, the IEEE 30 bus system, which consists of 30 buses, 6 thermal generators, and 41 branches, is modified by replacing three thermal generators with two wind generators and one solar PV generator. And the IEEE 57-bus system, which consists of 57 buses, 7 thermal generators, and 80 branches, is also modified by the same concept. The variability of wind and solar generation is described using the Weibull and lognormal distributions, and its impact on the OPF problem is considered by incorporating reserve and penalty costs for overestimation and underestimation of power output. The paper also takes into account the unpredictability of power consumption (load demand) by analyzing its influence using standard probability density functions (PDF). Furthermore, practical conditions related to the thermal generators, such as ramp rate limits are examined. The MWSO algorithm is evaluated and analyzed using 23 standard benchmark functions, and a comparative study is conducted against six well-known techniques using various statistical parameters. The results and statistical analysis demonstrate the superiority and effectiveness of the MWSO algorithm compared to the original WSO algorithm for addressing the OPF problem in the presence of generation and demand uncertainties.https://doi.org/10.1038/s41598-024-53249-z |
| spellingShingle | Mohamed Farhat Salah Kamel Mohamed A. Elseify Almoataz Y. Abdelaziz A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources Scientific Reports |
| title | A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources |
| title_full | A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources |
| title_fullStr | A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources |
| title_full_unstemmed | A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources |
| title_short | A modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources |
| title_sort | modified white shark optimizer for optimal power flow considering uncertainty of renewable energy sources |
| url | https://doi.org/10.1038/s41598-024-53249-z |
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