A new genetic algorithm approach for unit commitment
This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. In the proposed algorithm, coding the solution of the unit commitment problem is based on mixing binary and decimal representations. A fitness function is constructed from the total o...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , |
| التنسيق: | article |
| منشور في: |
1997
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://eprints.kfupm.edu.sa/id/eprint/14247/1/14247_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14247/2/14247_2.doc |
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| _version_ | 1864513393654759424 |
|---|---|
| author | Mantawy, A.H. |
| author2 | Abdel-Magid, Y.L. Selim, S.Z. unknown |
| author2_role | author author author |
| author_facet | Mantawy, A.H. Abdel-Magid, Y.L. Selim, S.Z. unknown |
| author_role | author |
| dc.creator.none.fl_str_mv | Mantawy, A.H. Abdel-Magid, Y.L. Selim, S.Z. unknown |
| dc.date.none.fl_str_mv | 1997-09 2020 |
| dc.format.none.fl_str_mv | application/pdf application/msword |
| dc.identifier.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14247/1/14247_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14247/2/14247_2.doc (1997) A new genetic algorithm approach for unit commitment. Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997. GALESIA 97. Second International Conference On (Conf. Publ. No. 446), 1. |
| dc.language.none.fl_str_mv | en en |
| dc.publisher.none.fl_str_mv | IEEE |
| dc.relation.none.fl_str_mv | https://eprints.kfupm.edu.sa/id/eprint/14247/ |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Computer |
| dc.title.none.fl_str_mv | A new genetic algorithm approach for unit commitment |
| dc.type.none.fl_str_mv | Article PeerReviewed info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | This paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. In the proposed algorithm, coding the solution of the unit commitment problem is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units without penalty terms. Genetic operators are implemented to enhance the search speed and to save memory space. The problem under consideration includes two linked subproblems: a combinatorial optimization problem and a nonlinear programming problem. The former is solved using the proposed genetic algorithm while the latter problem is solved via a quadratic programming routine. Numerical results showed an improvement in the solutions costs compared to the results reported in the literature |
| eu_rights_str_mv | openAccess |
| format | article |
| id | KFUPM_ea9334ea0deeb01451ef83bc97ca12c8 |
| identifier_str_mv | (1997) A new genetic algorithm approach for unit commitment. Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997. GALESIA 97. Second International Conference On (Conf. Publ. No. 446), 1. |
| language_invalid_str_mv | en |
| network_acronym_str | KFUPM |
| network_name_str | King Fahd University of Petroleum and Minerals |
| oai_identifier_str | oai::14247 |
| publishDate | 1997 |
| publisher.none.fl_str_mv | IEEE |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | A new genetic algorithm approach for unit commitmentMantawy, A.H.Abdel-Magid, Y.L.Selim, S.Z.unknownComputerThis paper presents a new genetic algorithm approach to solve the unit commitment problem in electric power systems. In the proposed algorithm, coding the solution of the unit commitment problem is based on mixing binary and decimal representations. A fitness function is constructed from the total operating cost of the generating units without penalty terms. Genetic operators are implemented to enhance the search speed and to save memory space. The problem under consideration includes two linked subproblems: a combinatorial optimization problem and a nonlinear programming problem. The former is solved using the proposed genetic algorithm while the latter problem is solved via a quadratic programming routine. Numerical results showed an improvement in the solutions costs compared to the results reported in the literatureIEEE1997-092020ArticlePeerReviewedinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfapplication/mswordhttps://eprints.kfupm.edu.sa/id/eprint/14247/1/14247_1.pdfhttps://eprints.kfupm.edu.sa/id/eprint/14247/2/14247_2.doc (1997) A new genetic algorithm approach for unit commitment. Genetic Algorithms in Engineering Systems: Innovations and Applications, 1997. GALESIA 97. Second International Conference On (Conf. Publ. No. 446), 1. enenhttps://eprints.kfupm.edu.sa/id/eprint/14247/info:eu-repo/semantics/openAccessoai::142472019-11-01T14:04:55Z |
| spellingShingle | A new genetic algorithm approach for unit commitment Mantawy, A.H. Computer |
| status_str | publishedVersion |
| title | A new genetic algorithm approach for unit commitment |
| title_full | A new genetic algorithm approach for unit commitment |
| title_fullStr | A new genetic algorithm approach for unit commitment |
| title_full_unstemmed | A new genetic algorithm approach for unit commitment |
| title_short | A new genetic algorithm approach for unit commitment |
| title_sort | A new genetic algorithm approach for unit commitment |
| topic | Computer |
| url | https://eprints.kfupm.edu.sa/id/eprint/14247/1/14247_1.pdf https://eprints.kfupm.edu.sa/id/eprint/14247/2/14247_2.doc |