Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods.
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| Title: | Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods. |
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| Authors: | Alrashidi, Musaed1 malrashidi@qu.edu.sa |
| Source: | Computer Systems Science & Engineering. 2023, Vol. 47 Issue 1, p491-513. 23p. |
| Subjects: | Wind power, Algorithms, Weibull distribution, Numerical analysis, Parameter estimation |
| Abstract: | Statistical distributions are used to model wind speed, and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia, namely Aljouf, Rafha, Tabuk, Turaif, and Yanbo. Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data. Also, with six wind turbine technologies rating between 1 and 3MW, the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy ($/kWh) compared to the assessments by IOAs. The energy cost analyses show that Turaif is the windiest site, with an electricity cost of $0.016906/kWh. The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding $0.02739/kWh. Finally, the outcomes of this study exhibit the potential of wind energy in Saudi Arabia, and its environmental goals can be acquired by harvesting wind energy. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 164329047 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Alrashidi%2C+Musaed%22">Alrashidi, Musaed</searchLink><relatesTo>1</relatesTo><i> malrashidi@qu.edu.sa</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Systems+Science+%26+Engineering%22">Computer Systems Science & Engineering</searchLink>. 2023, Vol. 47 Issue 1, p491-513. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Wind+power%22">Wind power</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Weibull+distribution%22">Weibull distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+analysis%22">Numerical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Statistical distributions are used to model wind speed, and the twoparameters Weibull distribution has proven its effectiveness at characterizing wind speed. Accurate estimation of Weibull parameters, the scale (c) and shape (k), is crucial in describing the actual wind speed data and evaluating the wind energy potential. Therefore, this study compares the most common conventional numerical (CN) estimation methods and the recent intelligent optimization algorithms (IOA) to show how precise estimation of c and k affects the wind energy resource assessments. In addition, this study conducts technical and economic feasibility studies for five sites in the northern part of Saudi Arabia, namely Aljouf, Rafha, Tabuk, Turaif, and Yanbo. Results exhibit that IOAs have better performance in attaining optimal Weibull parameters and provided an adequate description of the observed wind speed data. Also, with six wind turbine technologies rating between 1 and 3MW, the technical and economic assessment results reveal that the CN methods tend to overestimate the energy output and underestimate the cost of energy ($/kWh) compared to the assessments by IOAs. The energy cost analyses show that Turaif is the windiest site, with an electricity cost of $0.016906/kWh. The highest wind energy output is obtained with the wind turbine having a rated power of 2.5 MW at all considered sites with electricity costs not exceeding $0.02739/kWh. Finally, the outcomes of this study exhibit the potential of wind energy in Saudi Arabia, and its environmental goals can be acquired by harvesting wind energy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Systems Science & Engineering is the property of Tech Science Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.32604/csse.2023.038628 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 491 Subjects: – SubjectFull: Wind power Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Weibull distribution Type: general – SubjectFull: Numerical analysis Type: general – SubjectFull: Parameter estimation Type: general Titles: – TitleFull: Comparative Analysis for Evaluating Wind Energy Resources Using Intelligent Optimization Algorithms and Numerical Methods. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Alrashidi, Musaed IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 02676192 Numbering: – Type: volume Value: 47 – Type: issue Value: 1 Titles: – TitleFull: Computer Systems Science & Engineering Type: main |
| ResultId | 1 |
