Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model.
Resumen:
This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.
| 2021 | |
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Esta investigación ha sido cofinanciada por el programa de investigación CERVERA del CDTI, el Instituto Industrial y Centro de Desarrollo Tecnológico de España, en el marco del Proyecto de investigación HySGrid+ (CER-20191019), así como por el Programa CYTED de la red MICRO-EOLO (Red 718RT0564). Fondo Sectorial de Energía de ANII. Proyecto FSE_1_2018_1_152951 "Desarrollo de modelos computacionales de bajo costo para el monitoreo y la optimización de la operación de parques eólicos" |
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Genetic algorithm Offshore wind farm Wake effect Wind energy Wind farm Wind farm operation |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://www.mdpi.com/1996-1073/14/4/938
https://hdl.handle.net/20.500.12008/41296 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| _version_ | 1872865287774142464 |
|---|---|
| author | Serrano González, Javier |
| author2 | López, Bruno Draper, Martín |
| author2_role | author author |
| author_facet | Serrano González, Javier López, Bruno Draper, Martín |
| author_role | author |
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| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Serrano González Javier, Universidad de Sevilla, España. López Bruno, Universidad de la República (Uruguay). Facultad de Ingeniería. Draper Martín, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | Serrano González, Javier López, Bruno Draper, Martín |
| dc.date.accessioned.none.fl_str_mv | 2023-11-17T12:18:50Z |
| dc.date.available.none.fl_str_mv | 2023-11-17T12:18:50Z |
| dc.date.issued.none.fl_str_mv | 2021 |
| dc.description.abstract.none.fl_txt_mv | This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake. |
| dc.description.es.fl_txt_mv | Este artículo pertenece al Número Especial Nuevas Tendencias en Offshore en Parques Eólicos : Diseño, Operación y Mantenimiento. |
| dc.description.sponsorship.none.fl_txt_mv | Esta investigación ha sido cofinanciada por el programa de investigación CERVERA del CDTI, el Instituto Industrial y Centro de Desarrollo Tecnológico de España, en el marco del Proyecto de investigación HySGrid+ (CER-20191019), así como por el Programa CYTED de la red MICRO-EOLO (Red 718RT0564). Fondo Sectorial de Energía de ANII. Proyecto FSE_1_2018_1_152951 "Desarrollo de modelos computacionales de bajo costo para el monitoreo y la optimización de la operación de parques eólicos" |
| dc.format.extent.es.fl_str_mv | 18 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Serrano González, J., López, B. y Draper, M. "Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model". Energies. [en línea]. 2021, vol. 14, no 4, pp. 1-18. DOI: 10.3390/en14040938. |
| dc.identifier.doi.none.fl_str_mv | 10.3390/en14040938 |
| dc.identifier.issn.none.fl_str_mv | 1996-1073 |
| dc.identifier.uri.none.fl_str_mv | https://www.mdpi.com/1996-1073/14/4/938 https://hdl.handle.net/20.500.12008/41296 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | MDPI |
| dc.relation.none.fl_str_mv | Energies, vol. 14, no 4, feb. 2021, pp. 1-18. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Genetic algorithm Offshore wind farm Wake effect Wind energy Wind farm Wind farm operation |
| dc.title.none.fl_str_mv | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Este artículo pertenece al Número Especial Nuevas Tendencias en Offshore en Parques Eólicos : Diseño, Operación y Mantenimiento. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_fc6d6764530f672b8717c90a9d1e4cf5 |
| identifier_str_mv | Serrano González, J., López, B. y Draper, M. "Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model". Energies. [en línea]. 2021, vol. 14, no 4, pp. 1-18. DOI: 10.3390/en14040938. 1996-1073 10.3390/en14040938 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41296 |
| publishDate | 2021 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | karina.camps@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución (CC - By 4.0) |
| spelling | Serrano González Javier, Universidad de Sevilla, España.López Bruno, Universidad de la República (Uruguay). Facultad de Ingeniería.Draper Martín, Universidad de la República (Uruguay). Facultad de Ingeniería.2023-11-17T12:18:50Z2023-11-17T12:18:50Z2021Serrano González, J., López, B. y Draper, M. "Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model". Energies. [en línea]. 2021, vol. 14, no 4, pp. 1-18. DOI: 10.3390/en14040938.1996-1073https://www.mdpi.com/1996-1073/14/4/938https://hdl.handle.net/20.500.12008/4129610.3390/en14040938Este artículo pertenece al Número Especial Nuevas Tendencias en Offshore en Parques Eólicos : Diseño, Operación y Mantenimiento.This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2023-11-15T20:24:33Z No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) SLD21.pdf: 3709422 bytes, checksum: eddf6002dcdafbbbad6f4c2edeca9558 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2023-11-16T17:25:20Z (GMT) No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) SLD21.pdf: 3709422 bytes, checksum: eddf6002dcdafbbbad6f4c2edeca9558 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-11-17T12:18:50Z (GMT). No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) SLD21.pdf: 3709422 bytes, checksum: eddf6002dcdafbbbad6f4c2edeca9558 (MD5) Previous issue date: 2021Esta investigación ha sido cofinanciada por el programa de investigación CERVERA del CDTI, el Instituto Industrial y Centro de Desarrollo Tecnológico de España, en el marco del Proyecto de investigación HySGrid+ (CER-20191019), así como por el Programa CYTED de la red MICRO-EOLO (Red 718RT0564).Fondo Sectorial de Energía de ANII. Proyecto FSE_1_2018_1_152951 "Desarrollo de modelos computacionales de bajo costo para el monitoreo y la optimización de la operación de parques eólicos"18 p.application/pdfenengMDPIEnergies, vol. 14, no 4, feb. 2021, pp. 1-18.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución (CC - By 4.0)Genetic algorithmOffshore wind farmWake effectWind energyWind farmWind farm operationOptimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model.Artículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaSerrano González, JavierLópez, BrunoDraper, MartínLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/41296/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-844http://localhost:8080/xmlui/bitstream/20.500.12008/41296/2/license_urla0ebbeafb9d2ec7cbb19d7137ebc392cMD52license_textlicense_texttext/html; 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públicahttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestkarina.camps@seciu.edu.uyUruguayopendoar:47712023-11-17T12:18:50COLIBRI - Universidad de la Repúblicafalse |
| spellingShingle | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. Serrano González, Javier Genetic algorithm Offshore wind farm Wake effect Wind energy Wind farm Wind farm operation |
| status_str | publishedVersion |
| title | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| title_full | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| title_fullStr | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| title_full_unstemmed | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| title_short | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| title_sort | Optimal pitch angle strategy for energy maximization in offshore wind farms considering gaussian wake model. |
| topic | Genetic algorithm Offshore wind farm Wake effect Wind energy Wind farm Wind farm operation |
| url | https://www.mdpi.com/1996-1073/14/4/938 https://hdl.handle.net/20.500.12008/41296 |
