Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina
Resumen:
In the continuous fight against Bellman's Curse of Dimensionality, this work presents the first steps towards learning the Optimal Operation Policy of the electricity generation system of Uruguay, Brazil, Paraguay and Argentina with the infrastructures projected for the year 2030. The Operation Policy under consideration involves 76 state variables: one associated to the surface temperature anomaly of the Pacific Ocean in the N34 area, and 75 related to the hydroelectric reservoirs. The proposed methodology includes the design and training of two alternate neural network architectures combined with modern techniques devised for variance reduction and exploration, which were key to the success achieved
| 2022 | |
| Proyecto ANII-FSE_1_2017_1_144926 - "Planificación de inversiones con energías variables, restricciones de red y gestión de demanda" (2018-2020) Fondo Sectorial de Energía ANII | |
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Wind energy generation Training Sea surface Renewable energy sources Neural networks Hydroelectric power generation Reinforcement learning Approximate Stochastic Dynamic Programming Machine Learning Optimal operation of hydrothermal systems |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://ieeexplore.ieee.org/document/10037786
https://hdl.handle.net/20.500.12008/36685 |
|
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522899831029760 |
|---|---|
| author | Chaer, Ruben |
| author2 | Ramírez Paulino, Ignacio Camacho, Vanina Caporale, Ximena Casaravilla, Gonzalo |
| author2_role | author author author author |
| author_facet | Chaer, Ruben Ramírez Paulino, Ignacio Camacho, Vanina Caporale, Ximena Casaravilla, Gonzalo |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Chaer Ruben, Universidad de la República (Uruguay). Facultad de Ingeniería. Ramírez Paulino Ignacio, Universidad de la República (Uruguay). Facultad de Ingeniería. Camacho Vanina, Administración del Mercado Eléctrico Caporale Ximena, Universidad de la República (Uruguay). Facultad de Ingeniería. Casaravilla Gonzalo, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.coverage.spatial.es.fl_str_mv | Argentina Brasil Paraguay Uruguay |
| dc.creator.none.fl_str_mv | Chaer, Ruben Ramírez Paulino, Ignacio Camacho, Vanina Caporale, Ximena Casaravilla, Gonzalo |
| dc.date.accessioned.none.fl_str_mv | 2023-04-12T11:45:43Z |
| dc.date.available.none.fl_str_mv | 2023-04-12T11:45:43Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | In the continuous fight against Bellman's Curse of Dimensionality, this work presents the first steps towards learning the Optimal Operation Policy of the electricity generation system of Uruguay, Brazil, Paraguay and Argentina with the infrastructures projected for the year 2030. The Operation Policy under consideration involves 76 state variables: one associated to the surface temperature anomaly of the Pacific Ocean in the N34 area, and 75 related to the hydroelectric reservoirs. The proposed methodology includes the design and training of two alternate neural network architectures combined with modern techniques devised for variance reduction and exploration, which were key to the success achieved |
| dc.description.es.fl_txt_mv | Presentado y publicado en 2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America), La Paz, Bolivia, 20-22 oct. 2022, pp. 1-6. |
| dc.description.sponsorship.none.fl_txt_mv | Proyecto ANII-FSE_1_2017_1_144926 - "Planificación de inversiones con energías variables, restricciones de red y gestión de demanda" (2018-2020) Fondo Sectorial de Energía ANII |
| dc.format.extent.es.fl_str_mv | 6 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Chaer, R., Ramírez Paulino, I., Camacho, V. y otros. Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina [Preprint]. Publicado en: 2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America), La Paz, Bolivia, 20-22 oct 2022, 6 p. DOI 10.1109/IEEEPESGTDLatinAmeri53482.2022.10037786 |
| dc.identifier.uri.none.fl_str_mv | https://ieeexplore.ieee.org/document/10037786 https://hdl.handle.net/20.500.12008/36685 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 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 | Wind energy generation Training Sea surface Renewable energy sources Neural networks Hydroelectric power generation Reinforcement learning Approximate Stochastic Dynamic Programming Machine Learning Optimal operation of hydrothermal systems |
| dc.title.none.fl_str_mv | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | Presentado y publicado en 2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America), La Paz, Bolivia, 20-22 oct. 2022, pp. 1-6. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_7b506d11cc275d40bf45410f1596713b |
| identifier_str_mv | Chaer, R., Ramírez Paulino, I., Camacho, V. y otros. Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina [Preprint]. Publicado en: 2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America), La Paz, Bolivia, 20-22 oct 2022, 6 p. DOI 10.1109/IEEEPESGTDLatinAmeri53482.2022.10037786 |
| 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:20.500.12008/36685 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@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 - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Chaer Ruben, Universidad de la República (Uruguay). Facultad de Ingeniería.Ramírez Paulino Ignacio, Universidad de la República (Uruguay). Facultad de Ingeniería.Camacho Vanina, Administración del Mercado EléctricoCaporale Ximena, Universidad de la República (Uruguay). Facultad de Ingeniería.Casaravilla Gonzalo, Universidad de la República (Uruguay). Facultad de Ingeniería.ArgentinaBrasilParaguayUruguay2023-04-12T11:45:43Z2023-04-12T11:45:43Z2022Chaer, R., Ramírez Paulino, I., Camacho, V. y otros. Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina [Preprint]. Publicado en: 2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America), La Paz, Bolivia, 20-22 oct 2022, 6 p. DOI 10.1109/IEEEPESGTDLatinAmeri53482.2022.10037786https://ieeexplore.ieee.org/document/10037786https://hdl.handle.net/20.500.12008/36685Presentado y publicado en 2022 IEEE PES Generation, Transmission and Distribution Conference and Exposition – Latin America (IEEE PES GTD Latin America), La Paz, Bolivia, 20-22 oct. 2022, pp. 1-6.In the continuous fight against Bellman's Curse of Dimensionality, this work presents the first steps towards learning the Optimal Operation Policy of the electricity generation system of Uruguay, Brazil, Paraguay and Argentina with the infrastructures projected for the year 2030. The Operation Policy under consideration involves 76 state variables: one associated to the surface temperature anomaly of the Pacific Ocean in the N34 area, and 75 related to the hydroelectric reservoirs. The proposed methodology includes the design and training of two alternate neural network architectures combined with modern techniques devised for variance reduction and exploration, which were key to the success achievedSubmitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2023-04-10T16:47:07Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) CRCCC22.pdf: 245758 bytes, checksum: 249132c0fcef3a0382301d036e25d78b (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2023-04-11T18:05:14Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) CRCCC22.pdf: 245758 bytes, checksum: 249132c0fcef3a0382301d036e25d78b (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-04-12T11:45:43Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) CRCCC22.pdf: 245758 bytes, checksum: 249132c0fcef3a0382301d036e25d78b (MD5) Previous issue date: 2022Proyecto ANII-FSE_1_2017_1_144926 - "Planificación de inversiones con energías variables, restricciones de red y gestión de demanda" (2018-2020) Fondo Sectorial de Energía ANII6 p.application/pdfenengLas 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 - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Wind energy generationTrainingSea surfaceRenewable energy sourcesNeural networksHydroelectric power generationReinforcement learningApproximate Stochastic Dynamic ProgrammingMachine LearningOptimal operation of hydrothermal systemsLearning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and ArgentinaPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaChaer, RubenRamírez Paulino, IgnacioCamacho, VaninaCaporale, XimenaCasaravilla, GonzaloPotenciaPotenciaProcesamiento de SeñalesProcesamiento de SeñalesEnergía EléctricaTratamiento de ImágenesEnergía EléctricaTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina Chaer, Ruben Wind energy generation Training Sea surface Renewable energy sources Neural networks Hydroelectric power generation Reinforcement learning Approximate Stochastic Dynamic Programming Machine Learning Optimal operation of hydrothermal systems |
| status_str | submittedVersion |
| title | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| title_full | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| title_fullStr | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| title_full_unstemmed | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| title_short | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| title_sort | Learning the optimal joint operation of the energy systems of Uruguay, Brazil, Paraguay and Argentina |
| topic | Wind energy generation Training Sea surface Renewable energy sources Neural networks Hydroelectric power generation Reinforcement learning Approximate Stochastic Dynamic Programming Machine Learning Optimal operation of hydrothermal systems |
| url | https://ieeexplore.ieee.org/document/10037786 https://hdl.handle.net/20.500.12008/36685 |
