Análisis de la penetración de energías renovables no convencionales en el suministro de electricidad en Colombia por medio de simulación.

dc.contributor.advisorOlaya Morales, Yris
dc.contributor.advisorArango Aramburo, Santiago
dc.contributor.authorValencia Zapata, Alexandra
dc.contributor.researchgroupCiencias de la Decisionspa
dc.coverage.countryColombia
dc.date.accessioned2021-10-12T20:13:19Z
dc.date.available2021-10-12T20:13:19Z
dc.date.issued2021
dc.descriptiondiagramas, tablasspa
dc.description.abstractLa transición a economías bajas en carbono plantea la necesidad de comprender los efectos que tiene la incorporación de tecnologías renovables no convencionales sobre la seguridad del suministro, es decir sobre un suministro de energía con disponibilidad ininterrumpida de fuentes de energía. En particular, interesa evaluar el impacto de tecnologías de generación con fuentes renovables no convencionales en un sistema con un gran componente de generación hidráulica, como el caso colombiano. Para tal fin, se desarrolló un modelo de simulación del predespacho ideal de electricidad para Colombia. El modelo usa métodos estocásticos para representar las ofertas de generación de acuerdo con su fuente de energía. Las simulaciones del modelo muestran cómo las tecnologías renovables son siempre despachadas, al beneficiarse de la regla de orden de mérito, las tecnologías térmicas convencionales disminuyen su participación en el despacho y con ello se reducen las emisiones de CO2 emitidos por el sector, y de mayor importancia, el precio de bolsa es más bajo. (Texto tomado de la fuente)spa
dc.description.abstractThe transition to low-carbon economies sets out the need to understand the effects of the incorporation of non-conventional renewable technologies in the electricity supply, that is, on an energy supply with uninterrupted availability of energy sources. It is interesting to evaluate the impact of non-conventional renewable source generation technologies in systems with a sizeable hydraulic generation component, such as the Colombian case is. For this purpose, we developed a simulation model of the ideal pre-dispatch of electricity in Colombia. The model uses stochastic methods to represent generation offers according to their energy sources. Model's simulations show that the system operation always dispatches renewable technologies, which benefit from the merit order, conventional thermal technologies reduce their contribution on the dispatch, and thereby reducing the CO2 emissions emitted by the sector, and more importantly, the wholesale electricity price is lower.eng
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingeniería - Ingeniería de Sistemasspa
dc.description.researchareaInvestigación de operacionesspa
dc.format.extentxx, 113 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/80522
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.departmentDepartamento de la Computación y la Decisiónspa
dc.publisher.facultyFacultad de Minasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Minas - Maestría en Ingeniería - Ingeniería de Sistemasspa
dc.relation.referencesAL-Musaylh, M. S., Deo, R. C., Adamowski, J. F., & Li, Y. (2019). Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia. Renewable and Sustainable Energy Reviews, 113(July 2019), 109293. https://doi.org/10.1016/j.rser.2019.109293spa
dc.relation.referencesAL-Musaylh, M. S., Deo, R. C., Li, Y., & Adamowski, J. F. (2018). Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting. Applied Energy, 217(February), 422–439. https://doi.org/10.1016/j.apenergy.2018.02.140spa
dc.relation.referencesAllen, T. T. (2015). Introduction to discrete event simulation and agent- based modeling. Springe. https://doi.org/10.1007/978-0-85729-139-4 Springerspa
dc.relation.referencesAlmonacid, F., Pérez-Higueras, P. J., Fernández, E. F., & Hontoria, L. (2014). A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Conversion and Management, 85, 389–398. https://doi.org/10.1016/j.enconman.2014.05.090spa
dc.relation.referencesAmbec, S., & Crampes, C. (2012). Electricity provision with intermittent sources of energy. Resource and Energy Economics, 34(3), 319–336. https://doi.org/10.1016/j.reseneeco.2012.01.001spa
dc.relation.referencesAntonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., & Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78–111. https://doi.org/10.1016/j.solener.2016.06.069spa
dc.relation.referencesArango-Aramburo, S., Turner, S. W. D., Daenzer, K., Ríos-Ocampo, J. P., Hejazi, M. I., Kober, T., Álvarez-Espinosa, A. C., Romero-Otalora, G. D., & van der Zwaan, B. (2019). Climate impacts on hydropower in Colombia: A multi-model assessment of power sector adaptation pathways. Energy Policy, 128(July 2018), 179–188. https://doi.org/10.1016/j.enpol.2018.12.057spa
dc.relation.referencesArias-Gaviria, J., Carvajal-Quintero, S. X., & Arango-Aramburo, S. (2019). Understanding dynamics and policy for renewable energy diffusion in Colombia. Renewable Energy, 1111–1119. https://doi.org/10.1016/j.renene.2019.02.138spa
dc.relation.referencesAwad, H., Salim, K. M. E., & Gül, M. (2020). Multi-objective design of grid-tied solar photovoltaics for commercial flat rooftops using particle swarm optimization algorithm. Journal of Building Engineering, 28(July 2019), 101080. https://doi.org/10.1016/j.jobe.2019.101080spa
dc.relation.referencesBaldick, R. (1995). The Generalized Unit Commitment Problem. IEEE Transactions on Power Systems, 10(1), 465–475. https://doi.org/10.1109/59.373972spa
dc.relation.referencesBale, C. S. E., Varga, L., & Foxon, T. J. (2015). Energy and complexity: New ways forward. Applied Energy, 138, 150–159. https://doi.org/10.1016/j.apenergy.2014.10.057spa
dc.relation.referencesBanks, J., Carson II, J., Nelson, B., & Nicol, D. (2014). Discrete-evetn system simulation (Quinta, Issue 9). Pearson.http://publications.lib.chalmers.se/records/fulltext/245180/245180.pdf%0Ahttps://hdl.handle.net/20.500.12380/245180%0Ahttp://dx.doi.org/10.1016/j.jsames.2011.03.003%0Ahttps://doi.org/10.1016/j.gr.2017.08.001%0Ahttp://dx.doi.org/10.1016/j.precamres.2014.12spa
dc.relation.referencesBeck, F., & Martinot, E. (2004). Renewable Energy Policies and Barriers. Encyclopedia of Energy, 5, 365–383. https://doi.org/10.1016/b0-12-176480-x/00488-5spa
dc.relation.referencesBenevit, M. G., Silva, J. S., Gewehr, A. G., & Beluco, A. (2016). Subtle Influence of the Weibull Shape Parameter on Homer Optimization Space of a Wind Diesel Hybrid Gen Set for Use in Southern Brazil. Journal of Power and Energy Engineering, 04(08), 38–48. https://doi.org/10.4236/jpee.2016.48004spa
dc.relation.referencesBotero, S. B., & Cano Cano, J. A. (2008). Análisis de series de tiempo para la predicción de los precios de la energía en la bolsa de Colombia. Cuadernos de Economia, 27(48), 173–208.spa
dc.relation.referencesBreceda Lapeyre, M. (1990). Precios de la Electricidad: Un Debate Te6rico Para los Paises en Vias de Desarrollo. 77–113.spa
dc.relation.referencesBrekken, T. K. A., Yokochi, A., Von Jouanne, A., Yen, Z. Z., Hapke, H. M., & Halamay, D. A. (2011). Optimal energy storage sizing and control for wind power applications. IEEE Transactions on Sustainable Energy, 2(1), 69–77. https://doi.org/10.1109/TSTE.2010.2066294spa
dc.relation.referencesBrouwer, A. S., Van Den Broek, M., Seebregts, A., & Faaij, A. (2014). Impacts of large-scale Intermittent Renewable Energy Sources on electricity systems, and how these can be modeled. Renewable and Sustainable Energy Reviews, 33, 443–466. https://doi.org/10.1016/j.rser.2014.01.076spa
dc.relation.referencesBublitz, A., Keles, D., Zimmermann, F., Fraunholz, C., & Fichtner, W. (2019). A survey on electricity market design: Insights from theory and real-world implementations of capacity remuneration mechanisms. Energy Economics, #pagerange#. https://doi.org/10.1016/j.eneco.2019.01.030spa
dc.relation.referencesCárdenas Ardila, L. M. (2015). Plataforma para la evaluación de políticas de mitigación de gases efecto invernadero en el sector eléctrico. Universidad Nacional de Colombia, 245.spa
dc.relation.referencesCarrión, M., & Arroyo, J. M. (2006). A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 21(3), 1371–1378. https://doi.org/10.1109/TPWRS.2006.876672spa
dc.relation.referencesCastaneda, M., Franco, C. J., & Dyner, I. (2017). Evaluating the effect of technology transformation on the electricity utility industry. Renewable and Sustainable Energy Reviews, 80(65), 341–351. https://doi.org/10.1016/j.rser.2017.05.179spa
dc.relation.referencesCebeci, M. E., Tor, O. B., Oprea, S., & Bara, A. (2018). Consecutive market and network simulations to optimize investment and operational decisions under different RES penetration scenarios. IEEE Transactions on Sustainable Energy, PP(c), 1. https://doi.org/10.1109/TSTE.2018.2881036spa
dc.relation.referencesChabouni, N., Belarbi, Y., & Benhassine, W. (2020). Electricity load dynamics, temperature and seasonality Nexus in Algeria. Energy, 200, 117513. https://doi.org/10.1016/j.energy.2020.117513spa
dc.relation.referencesChapagain, K., Kittipiyakul, S., & Kulthanavit, P. (2020). Short-term electricity demand forecasting: Impact analysis of temperature for Thailand. Energies, 13(10), 1–29. https://doi.org/10.3390/en13102498spa
dc.relation.referencesChattopadhyay, D. (2014). Modelling renewable energy impact on the electricity market in India. Renewable and Sustainable Energy Reviews, 31, 9–22. https://doi.org/10.1016/j.rser.2013.11.035spa
dc.relation.referencesCherif, H., & Belhadj, J. (2014). Energy output estimation of hybrid Wind-Photovoltaic power system using statistical distributions. 2, 117–132.spa
dc.relation.referencesCludius, J., Hermann, H., Matthes, F. C., & Graichen, V. (2014). The merit order effect of wind and photovoltaic electricity generation in Germany 2008-2016 estimation and distributional implications. Energy Economics, 44(2014), 302–313. https://doi.org/10.1016/j.eneco.2014.04.020spa
dc.relation.referencesCongreso de Colombia. (2014). Ley 1715 de 2014. In Ministerio de Minas y Energía (No. 1715; p. 25). http://www.fedebiocombustibles.com/files/1715.pdfspa
dc.relation.referencesCorrea Posada, C. M. (2009). Modelo de optimización para las plantas térmicas de generación de ciclo combinado en el despacho económico (Issue September). Universidad Nacional de Colombia Sede Medellín.spa
dc.relation.referencesCREG. (1994). Resolución N° 055 Por la cual se regula la actividad de generación de energía eléctrica en el Sistema Interconectado Nacional. (pp. 1–8). http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resolución-1994-CRG94055spa
dc.relation.referencesCREG. (1995). Resolución No. 025. Por la cual se establece el Código de Redes, como parte del Reglamento de Operación del Sistema Interconectado Nacional. In Comisión de Regulación de Energía y Gas (p. 141). http://apolo.creg.gov.co/Publicac.nsf/2b8fb06f012cc9c245256b7b00789b0c/3a940408d14bf2e80525785a007a653b/$FILE/Cr025-95.pdfspa
dc.relation.referencesCREG. (1996). Resolución No. 086. Por la cual se reglamenta la actividad de generación con plantas menores de 20MW que se encuentra conectado al Sistema Interconectado Nacional (SIN) (p. 4). http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resoluci%25C3%25B3n-1996-CRG86-96spa
dc.relation.referencesCREG. (2001). Resolución No. 026. Por la cual se dictan normas sobre funcionamiento del Mercado mayorista de Energía. (No. 026). http://apolo.creg.gov.co/PUBLICAC.NSF/Indice01/Resolución-2001-CREG026-2001?OpenDocumentspa
dc.relation.referencesCREG. (2009a). Resolución No.141. Por la cual se modifica el artículo 1 de la Resolución CREG-034 de 2001. (No. 141). http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resolucion-2009-Creg141-2009spa
dc.relation.referencesCREG. (2009b). Resolución N° 051. Por la cual se modifica el esquema de ofertas de precios, el Despacho ideal y las reglas para determinar el precio de la Bolsa en el Mercado Energía Mayorista (pp. 1–32). http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/e93298f462402ffd0525785a007a714f/$FILE/Creg051-2009.pdfspa
dc.relation.referencesCREG. (2010). Resolución N° 126. Por la cual se establecen los criterios generales para la remuneración del servicio de transprote de gas natural y esquema general de cargos del Sitema Nacional de Transporte, y se dictan otras disposiciones en materia de tranbsporte de (No. 126; pp. 1–69). http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resolucion-2010-Creg126-2010spa
dc.relation.referencesCREG. (2015). Resolución No.173. Por la cual se ordena hacer público un proyecto de resolución "Por la cual se modifica el despacho económico, el predespacho económico, el predespacho ideal y el despacho programado en el Mercado de Energía Mayorista. (No. 173). http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/37a1244f437cdec905257ee3007cbe8c/$FILE/Creg173-2015.pdfspa
dc.relation.referencesCREG. (2018a). Resolución No. 003. Por la cual se resuleve una actuación administrativa iniciada en virtud de los establecido en el artículo 126 de la Ley 142 de 1994 y se ajustan los cargos regulados del sistema de transporte de TGI S:A: E.S.P. (No. 003; pp. 1–32). http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/e097ea9241a3c13305258258004f0ea0/$FILE/Creg003-2018.pdfspa
dc.relation.referencesCREG. (2018b). Resolución No. 106.Por la cual se resuelve el recurso de reposición interpuesto por la Transportadora de Gas Internacional TGI S.A. E.S.P. contra la Resolución CREG 003 DE 2018. (No. 106; pp. 1–27). http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/7f7970d5a83cfb900525832400779ec5/$FILE/Creg106-2018.pdfspa
dc.relation.referencesCREG. (2019a). Declaración de parámetros para el cálculo de la ENFICC. In Circular 027-2019. http://apolo.creg.gov.co/Publicac.nsf/52188526a7290f8505256eee0072eba7/f2eb1897273b0596052583c4007c71fespa
dc.relation.referencesCREG. (2019b). Circular N° 024. Publicación de los parámetros reportados por los agentes para la determinación de la energía firme para el cargo por confiabilidad - ENFICC. http://apolo.creg.gov.co/Publicac.nsf/52188526a7290f8505256eee0072eba7/2b8f1f53bc87d067052583b7007745d7?OpenDocumentspa
dc.relation.referencesCutler, N. J., Boerema, N. D., MacGill, I. F., & Outhred, H. R. (2011). High penetration wind generation impacts on spot prices in the Australian national electricity market. Energy Policy, 39(10), 5939–5949. https://doi.org/10.1016/j.enpol.2011.06.053spa
dc.relation.referencesDas, D. C., Roy, A. K., & Sinha, N. (2012). GA based frequency controller for solar thermal-diesel-wind hybrid energy generation/energy storage system. International Journal of Electrical Power and Energy Systems, 43(1), 262–279. https://doi.org/10.1016/j.ijepes.2012.05.025spa
dc.relation.referencesDasgupta, D., & McGregor, D. R. (1994). Thermal unit commitment using genetic algorithms. IEE Proceedings: Generation, Transmission and Distribution, 141(5), 459–465. https://doi.org/10.1049/ip-gtd:19941221spa
dc.relation.referencesDe Felice, M., Alessandri, A., & Ruti, P. M. (2013). Electricity demand forecasting over Italy: Potential benefits using numerical weather prediction models. Electric Power Systems Research, 104, 71–79. https://doi.org/10.1016/j.epsr.2013.06.004spa
dc.relation.referencesDeng, J., & Jirutitijaroen, P. (2010). Short-term load forecasting using time series analysis: A case study for Singapore. 2010 IEEE Conference on Cybernetics and Intelligent Systems, CIS 2010, 231–236. https://doi.org/10.1109/ICCIS.2010.5518553spa
dc.relation.referencesDepartamento Nacional de Planeación. (2019). La Agenda 2030 en Colombia - Objetivos de Desarrollo Sostenible. https://www.ods.gov.co/esspa
dc.relation.referencesDíaz López, E., Prieto, A. M., & Lio, D. G. (2016). An implementation for the meta-heuristic “Variable Mesh Optimization” on CUDA architecture. Revista Cubana de Ciencias Informáticas, 10(3), 42–57. http://rcci.uci.xn--cupg-7na.42-56spa
dc.relation.referencesDíez, P. F. (1993). Energía eólica. In Energía eólica (pp. 1–25).spa
dc.relation.referencesDilhani, M. H. M. R. S., & Jeenanunta, C. (2017). Effect of Neural Network structure for daily electricity load forecasting. 3rd International Moratuwa Engineering Research Conference, MERCon 2017, 419–424. https://doi.org/10.1109/MERCon.2017.7980521spa
dc.relation.referencesECMWF. (2021). ERA5 | ECMWF. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5spa
dc.relation.referencesEIA. (2021). Electric Power Monthly - U.S. Energy Information Administration (EIA). https://www.eia.gov/electricity/monthly/epm_table_grapher.php?t=epmt_6_07_b Eltawil, M. A., & Zhao, Z. (2010). Grid-connected photovoltaic power systems: Technical and potential problems-A review. Renewable and Sustainable Energy Reviews, 14(1), 112–129. https://doi.org/10.1016/j.rser.2009.07.015spa
dc.relation.referencesEnergética2030. (2021). Energética 2030. https://www.energetica2030.co/spa
dc.relation.referencesFattaheian-Dehkkordi, S., Fereidunian, A., Gholami-Dehkordi, H., & Lesani, H. (2013). Hour- ahead demand forecasting in smart grid using support vector regression (SVR). International Transactions on Electrical Energy Systems, 20, 1–6. https://doi.org/10.1002/etepspa
dc.relation.referencesGan, L. K., Shek, J. K. H., & Mueller, M. A. (2015). Hybrid wind-photovoltaic-diesel-battery system sizing tool development using empirical approach, life-cycle cost and performance analysis: A case study in Scotland. Energy Conversion and Management, 106, 479–494. https://doi.org/10.1016/j.enconman.2015.09.029spa
dc.relation.referencesGaur, A. S., Das, P., Jain, A., Bhakar, R., & Mathur, J. (2019). Long-term energy system planning considering short-term operational constraints. Energy Strategy Reviews, 26(October 2018), 100383. https://doi.org/10.1016/j.esr.2019.100383spa
dc.relation.referencesGielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24(June 2018), 38–50. https://doi.org/10.1016/j.esr.2019.01.006spa
dc.relation.referencesGómez-Navarro, T., & Ribó-Pérez, D. (2018). Assesing the obstacles to the participaction of renewable energy sources in the electricity market of colombia.pdf. Renewable and Sustainable Energy Reviews, 90(September 2016), 131–141. https://doi.org/https://doi.org/10.1016/j.rser.2018.03.015spa
dc.relation.referencesGöransson, L., & Johnsson, F. (2009). Dispatch modeling of a regional power generation system - Integrating wind power. Renewable Energy, 34(4), 1040–1049. https://doi.org/10.1016/j.renene.2008.08.002spa
dc.relation.referencesGordon, S. I., & Guilfoos, B. (2017). Introduction to modeling and simulation with Matlab and Python. In Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis (Vol. 53, Issue 9). CRC Press is an imprint of Taylor & Francis Group.http://publications.lib.chalmers.se/records/fulltext/245180/245180.pdf%0Ahttps://hdl.handle.net/20.500.12380/245180%0Ahttp://dx.doi.org/10.1016/j.jsames.2011.03.003%0Ahttps://doi.org/10.1016/j.gr.2017.08.001%0Ahttp://dx.doi.org/10.1016/j.precamres.2014.12spa
dc.relation.referencesHartel, R., Fichtner, W., & Keles, D. (2015). Electricity market design options for promoting low carbon technologies. Insight_E, 3(April). http://www.insightenergy.org/ckeditor_assets/attachments/71/rreb3.pdfspa
dc.relation.referencesHashimoto, T., Stedinger, J. R., & Loucks, D. P. (1982). Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resources Research, 18(1), 14–20. https://doi.org/10.1029/WR018i001p00014spa
dc.relation.referencesHelm, C., & Mier, M. (2019). On the efficient market diffusion of intermittent renewable energies. Energy Economics, 80, 812–830. https://doi.org/10.1016/j.eneco.2019.01.017spa
dc.relation.referencesHenao, F., & Dyner, I. (2020). Renewables in the optimal expansion of colombian power considering the Hidroituango crisis. Renewable Energy, 158(2020), 612–627. https://doi.org/10.1016/j.renene.2020.05.055spa
dc.relation.referencesHenao, F., Rodriguez, Y., Viteri, J. P., & Dyner, I. (2019). Optimising the insertion of renewables in the Colombian power sector. Renewable Energy, 132, 81–92. https://doi.org/10.1016/j.renene.2018.07.099spa
dc.relation.referencesHenao, F., Viteri, J. P., Rodríguez, Y., Gómez, J., & Dyner, I. (2020). Annual and interannual complementarities of renewable energy sources in Colombia. Renewable and Sustainable Energy Reviews, 134(September). https://doi.org/10.1016/j.rser.2020.110318spa
dc.relation.referencesHernandez, J. A., Velasco, D., & Trujillo, C. L. (2011). Analysis of the effect of the implementation of photovoltaic systems like option of distributed generation in Colombia. Renewable and Sustainable Energy Reviews, 15(5), 2290–2298. https://doi.org/10.1016/j.rser.2011.02.003spa
dc.relation.referencesHerrera Flórez, H. H. (2016). FACTORES DE EMISION DEL S.I.N. SISTEMA INTERCONECTADO NACIONAL COLOMBIA 2015. https://www1.upme.gov.co/siame/Documents/Calculo-FE-del-SIN/Documento_calculo_del_FE_SIN_2015_dic_2016.pdfspa
dc.relation.referencesHolttinen, H., Meibom, P., Orths, A., Lange, B., O´Malley, M., Tande, J. O., Estanqueiro, A., Gomez, E., Söder, L., Strbac, G., Smith, J. C., & Van Hulle, F. (2011). Impacts of large amounts of wind power on design and operation of power system, results of IEA collaboration. Wind Energy, 14, 179–192. https://doi.org/10.1002/we.410spa
dc.relation.referencesIbargüengoytia González, P. H., Reyes Ballesteros, A., Borunda Pacheco, M., & García López, U. A. (2018). Predicción de potencia eólica utilizando técnicas modernas de Inteligencia Artificial. Ingeniería Investigación y Tecnología, 19(4), 1–11. https://doi.org/10.22201/fi.25940732e.2018.19n4.033spa
dc.relation.referencesIDEAM. (2014). Valores medios multianuales de temperatura media en C° - periodo 1981 - 2010.spa
dc.relation.referencesIEA. (2019). Energy security - Areas of work - IEA. https://www.iea.org/areas-of-work/ensuring-energy-securityspa
dc.relation.referencesIlseven, E., & Gol, M. (2019). A comparative study on feature selection based improvement of medium-term demand forecast accuracy. 2019 IEEE Milan PowerTech, PowerTech 2019, 1–6. https://doi.org/10.1109/PTC.2019.8810598spa
dc.relation.referencesInternational Renewable Energy Agency. (2020). Renewable Power Generation Costs in 2019. In Irena. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdfspa
dc.relation.referencesJalilov, S. M., Keskinen, M., Varis, O., Amer, S., & Ward, F. A. (2016). Managing the water-energy-food nexus: Gains and losses from new water development in Amu Darya River Basin. Journal of Hydrology, 539, 648–661. https://doi.org/10.1016/j.jhydrol.2016.05.071spa
dc.relation.referencesJaramillo, O. A., Borja, M. A., & Huacuz, J. M. (2004). Using hydropower to complement wind energy: A hybrid system to provide firm power. Renewable Energy, 29(11), 1887–1909. https://doi.org/10.1016/j.renene.2004.02.010spa
dc.relation.referencesJensen, S. Ø., Marszal-Pomianowska, A., Lollini, R., Pasut, W., Knotzer, A., Engelmann, P., Stafford, A., & Reynders, G. (2017). IEA EBC Annex 67 Energy Flexible Buildings. Energy and Buildings, 155, 25–34. https://doi.org/10.1016/j.enbuild.2017.08.044spa
dc.relation.referencesJimenez, M., Franco, C. J., & Dyner, I. (2016). Diffusion of renewable energy technologies: The need for policy in Colombia. Energy, 111, 818–829. https://doi.org/10.1016/j.energy.2016.06.051spa
dc.relation.referencesJordán, C., Medina, D., & Zúñiga, A. (2010). Aplicación de un Algoritmo Evolutivo Flexible a la Optimización de la Operación de Sistemas Hidrotérmicos. Revista Tecnológica ESPOL-RTE, 23, 35–45.spa
dc.relation.referencesKaldellis, J. K., Vlachou, D. S., & Korbakis, G. (2005). Techno-economic evaluation of small hydro power plants in Greece: A complete sensitivity analysis. Energy Policy, 33(15), 1969–1985. https://doi.org/10.1016/j.enpol.2004.03.018spa
dc.relation.referencesKirschen, D. S., & Strbac, G. (2019). Fundamentals of power system economics (Segunda). John Wiley & Sons.spa
dc.relation.referencesKleijnen, J. P. C. (1995). Statistical validation of simulation models. European Journal of Operational Research, 87, 21–34. https://ac.els-cdn.com/037722179500132A/1-s2.0-037722179500132A-main.pdf?_tid=469323b8-771b-4f7f-b087-0178fdc02807&acdnat=1550338576_811b5b25697cef2f608e95c78288c11fspa
dc.relation.referencesKondili, E. (2010). Design and performance optimisation of stand-alone and hybrid wind energy systems. In Stand-Alone and Hybrid Wind Energy Systems. Woodhead Publishing Limited. https://doi.org/10.1533/9781845699628.1.81spa
dc.relation.referencesKong, J., Skjelbred, H. I., & Fosso, O. B. (2020). An overview on formulations and optimization methods for the unit-based short-term hydro scheduling problem. Electric Power Systems Research, 178(April 2019), 106027. https://doi.org/10.1016/j.epsr.2019.106027spa
dc.relation.referencesKougias, I., Szabó, S., Monforti-Ferrario, F., Huld, T., & Bódis, K. (2016). A methodology for optimization of the complementarity between small-hydropower plants and solar PV systems. Renewable Energy, 87, 1023–1030. https://doi.org/10.1016/j.renene.2015.09.073spa
dc.relation.referencesKruyt, B., van Vuuren, D. P., de Vries, H. J. M., & Groenenberg, H. (2009). Indicators for energy security. Energy Policy, 37(6), 2166–2181. https://doi.org/10.1016/j.enpol.2009.02.006spa
dc.relation.referencesLannoye, E., Flynn, D., & O’Malley, M. (2015). Transmission, variable generation, and power system flexibility. IEEE Transactions on Power Systems, 30(1), 57–66. https://doi.org/10.1109/TPWRS.2014.2321793spa
dc.relation.referencesLebotsa, M. E., Sigauke, C., Bere, A., Fildes, R., & Boylan, J. E. (2018). Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem. Applied Energy, 222(December 2017), 104–118. https://doi.org/10.1016/j.apenergy.2018.03.155spa
dc.relation.referencesLee, C. W., & Lin, B. Y. (2017). Applications of the chaotic quantum genetic algorithm with support vector regression in load forecasting. Energies, 10(11). https://doi.org/10.3390/en10111832spa
dc.relation.referencesLiang, X. (2017). Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications, 53(2), 855–866. https://doi.org/10.1109/TIA.2016.2626253spa
dc.relation.referencesLiu, H., Chen, C., Lv, X., Wu, X., & Liu, M. (2019). Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 195(May), 328–345. https://doi.org/10.1016/j.enconman.2019.05.020spa
dc.relation.referencesLópez, A. R., Krumm, A., Schattenhofer, L., Burandt, T., Montoya, F. C., Oberländer, N., & Oei, P. Y. (2020). Solar PV generation in Colombia - A qualitative and quantitative approach to analyze the potential of solar energy market. Renewable Energy, 148, 1266–1279. https://doi.org/10.1016/j.renene.2019.10.066spa
dc.relation.referencesLund, H., & Mathiesen, B. V. (2009). Energy system analysis of 100% renewable energy systems-The case of Denmark in years 2030 and 2050. Energy, 34(5), 524–531. https://doi.org/10.1016/j.energy.2008.04.003spa
dc.relation.referencesLund, P. D., Lindgren, J., Mikkola, J., & Salpakari, J. (2015). Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renewable and Sustainable Energy Reviews, 45, 785–807. https://doi.org/10.1016/j.rser.2015.01.057spa
dc.relation.referencesMaldonado, S., González, A., & Crone, S. (2019). Automatic time series analysis for electric load forecasting via support vector regression. Applied Soft Computing Journal, 83, 105616. https://doi.org/10.1016/j.asoc.2019.105616spa
dc.relation.referencesMartín Guareño, J. J. (2016). Support Vector Regression: Propiedades y aplicaciones. In Universidad de Sevilla. Departamento de Estadística e Investigación Operativa. Universidad de Sevilla.spa
dc.relation.referencesMartínez-Márquez, C. I., Twizere-Bakunda, J. D., Lundback-Mompó, D., Orts-Grau, S., Gimeno-Sales, F. J., & Seguí-Chilet, S. (2019). Small wind turbine emulator based on lambda-Cp curves obtained under real operating conditions. Energies, 12(13). https://doi.org/10.3390/en12132456spa
dc.relation.referencesMartínez Moreno, W. A., & Rodríguez Hernández, R. (2019). Proyección de la demanda de energía eléctrica y potencia máxima en Colombia. http://www.siel.gov.co/siel/documentos/documentacion/Demanda/Proyeccion_Demanda_Energia_Jul_2019.pdfspa
dc.relation.referencesMarzband, M., Ghazimirsaeid, S. S., Uppal, H., & Fernando, T. (2017). A real-time evaluation of energy management systems for smart hybrid home Microgrids. Electric Power Systems Research, 143, 624–633. https://doi.org/10.1016/j.epsr.2016.10.054spa
dc.relation.referencesMastropietro, P., Rodilla, P., Rangel, L. E., & Batlle, C. (2020). Reforming the colombian electricity market for an efficient integration of renewables: A proposal. Energy Policy, 139(November 2019), 111346. https://doi.org/10.1016/j.enpol.2020.111346spa
dc.relation.referencesMejía Giraldo, D. A., Franco A, F. F., & Gallego, R. A. (2005). Solución Al Problema Del Despacho De Energía En Sistemas Hidrotérmicos Usando Simulated Annealing. Scientia Et Technica, XI(29), 7–12. https://doi.org/10.22517/23447214.6605spa
dc.relation.referencesMohanty, S., Patra, P. K., & Sahoo, S. S. (2016). Prediction and application of solar radiation with soft computing over traditional and conventional approach - A comprehensive review. Renewable and Sustainable Energy Reviews, 56, 778–796. https://doi.org/10.1016/j.rser.2015.11.078spa
dc.relation.referencesMokilane, P., Debba, P., Yadavalli, V. S. S., & Sigauke, C. (2019). Bayesian structural time-series approach to a long-term electricity demand forecasting. Applied Mathematics and Information Sciences, 13(2), 189–199. https://doi.org/10.18576/AMIS/130206spa
dc.relation.referencesMorales-España, G., Latorre, J. M., & Ramos, A. (2013). Tight and compact MILP formulation for the thermal unit commitment problem. IEEE Transactions on Power Systems, 28(4), 4897–4908. https://doi.org/10.1109/TPWRS.2013.2251373spa
dc.relation.referencesMosquera-López, S., & Nursimulu, A. (2019). Drivers of electricity price dynamics: Comparative analysis of spot and futures markets. Energy Policy, 126(May 2018), 76–87. https://doi.org/10.1016/j.enpol.2018.11.020spa
dc.relation.referencesNakata, T., Silva, D., & Rodionov, M. (2011). Application of energy system models for designing a low-carbon society. Progress in Energy and Combustion Science, 37(4), 462–502. https://doi.org/10.1016/j.pecs.2010.08.001spa
dc.relation.referencesNERC. (2013). Reliability Metrics Specifications Sheet ALR 1-3 Reserve Margin. https://www.nerc.com/comm/PC/Performance Analysis Subcommittee PAS 2013/1-3 July 9.pdfspa
dc.relation.referencesNewbery, D., Pollitt, M. G., Ritz, R. A., & Strielkowski, W. (2018). Market design for a high-renewables European electricity system. Renewable and Sustainable Energy Reviews, 91(April), 695–707. https://doi.org/10.1016/j.rser.2018.04.025spa
dc.relation.referencesOECD, & IEA. (2007). Tackling Investment Challenges in Power Generation. www.iea.org/w/bookshop/pricing.htmlspa
dc.relation.referencesOkumus, I., & Dinler, A. (2016). Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Conversion and Management, 123, 362–371. https://doi.org/10.1016/j.enconman.2016.06.053spa
dc.relation.referencesOrtega Arango, S., Ángel Sanint, E., & Jaramillo Vélez, A. (2020). ESCENARIOS ENERGÉTICOS PARA COLOMBIA EN EL MARCO DEL COVID-19. https://repository.eia.edu.co/bitstream/handle/11190/2530/EscenariosEnerg%E9ticosCovid19-WP.pdf?sequence=1spa
dc.relation.referencesOsorio, A. F., Ortega, S., & Arango-Aramburo, S. (2016). Assessment of the marine power potential in Colombia. Renewable and Sustainable Energy Reviews, 53, 966–977. https://doi.org/10.1016/j.rser.2015.09.057spa
dc.relation.referencesPaz, D. F. (2018). Metodología para la determinación de características del viento y evaluación del potencial de energía eólica en Túquerres - Nariño Methodology for the determination of wind characteristics and assessment of wind energy potential in Túquerres - Nariño Meto. 31(31), 19–31.spa
dc.relation.referencesPeláez Villada, D. C. (2014). Determinación del costo de le nergía hidraúlica en Colombia a partir del análisis del mercado de derivados energéticos [Universidad Nacional de Colombia]. https://doi.org/10.4324/9781315853178spa
dc.relation.referencesPerez, A., & Garcia-Rendon, J. J. (2021). Integration of non-conventional renewable energy and spot price of electricity: A counterfactual analysis for Colombia. Renewable Energy, 167, 146–161. https://doi.org/10.1016/j.renene.2020.11.067spa
dc.relation.referencesPfenninger, S., Hawkes, A., & Keirstead, J. (2014). Energy systems modeling for twenty-first century energy challenges. Renewable and Sustainable Energy Reviews, 33(1), 74–86. https://doi.org/10.1016/j.rser.2014.02.003spa
dc.relation.referencesPidd, M. (2004). Computer Simulation in Management Science (5ta ed.). John Wiley & Sons.spa
dc.relation.referencesPinson, P. (2013). Wind energy: Forecasting challenges for its operational management. Statistical Science, 28(4), 564–585. https://doi.org/10.1214/13-STS445spa
dc.relation.referencesPupo-Roncallo, O., Campillo, J., Ingham, D., Hughes, K., & Pourkashanian, M. (2019). Large scale integration of renewable energy sources (RES) in the future Colombian energy system. Energy, 186, 115805. https://doi.org/10.1016/j.energy.2019.07.135spa
dc.relation.referencesRau, R. (2010). Despacho Economico Optimo De Plantas De Generacion Hidrotermico En Sistemas De Energia Electrica [Universidad Nacional del Centro del Perú]. http://repositorio.uncp.edu.pe/bitstream/handle/UNCP/3604/Rau Vargas.pdf?sequence=1&isAllowed=yspa
dc.relation.referencesResendiz Trejo, J. (2006). Las maquinas de vectores de soporte para identificación en línea. Centro de investigación y de estudios avanzados del Instituto Politécnico Nacional.spa
dc.relation.referencesRhoades, S. A. (1993). The Herfindahl-Hisrchman Index. Federal Reserva Bank of St. Louis, 188–189.https://fraser.stlouisfed.org/files/docs/publications/FRB/pages/1990-1994/33101_1990-1994.pdfspa
dc.relation.referencesRueda-Bayona, J. G., Guzmán, A., Eras, J. J. C., Silva-Casarín, R., Bastidas-Arteaga, E., & Horrillo-Caraballo, J. (2019). Renewables energies in Colombia and the opportunity for the offshore wind technology. Journal of Cleaner Production, 220, 529–543. https://doi.org/10.1016/j.jclepro.2019.02.174spa
dc.relation.referencesSargent, R. G. (2008). Verification and validation of simulation models. Procedings of the 2008 Winter Simulation Conference, 157–169. https://doi.org/10.1109/WSC.2008.4736065spa
dc.relation.referencesSchlesinger, S., Crosble, R. E., Gagné, R. E., Innis, G. S., Lalwani, C. S., Loch, J., Sylvester, R. J., Wright, R. D., Kheir, N., & Bartos, D. (1979). Terminology for model credibility. Simulation, 32(3), 103–104. https://doi.org/10.1177/003754977903200304spa
dc.relation.referencesSchwartz, P. (2012). The Art of the Long View: Planning for the Future in an Uncertain World. Crown. https://books.google.com.co/books?id=T-r36bIZA44Cspa
dc.relation.referencesSen, S., & Kothari, D. . (1998). Optimal thermal generating unit commitment. Electrical Power & Energy Systems, 20, 443–451. https://doi.org/10.1109/TPAS.1971.293167spa
dc.relation.referencesSensfuß, F., Ragwitz, M., & Genoese, M. (2008). The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany. Energy Policy, 36(8), 3086–3094. https://doi.org/10.1016/j.enpol.2008.03.035spa
dc.relation.referencesSheble, G. B., & Fahd, G. N. (1994). Unit commitment literature synopsis. IEEE Transactions on Power Systems, 9(1), 128–135. https://doi.org/10.1109/59.317549spa
dc.relation.referencesSobri, S., Koohi-Kamali, S., & Rahim, N. A. (2018). Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156(May 2017), 459–497. https://doi.org/10.1016/j.enconman.2017.11.019spa
dc.relation.referencesSoroudi, A., & Amraee, T. (2013). Decision making under uncertainty in energy systems: State of the art. Renewable and Sustainable Energy Reviews, 28, 376–384. https://doi.org/10.1016/j.rser.2013.08.039spa
dc.relation.referencesSsekulima, E. B., Anwar, M. B., Al Hinai, A., & El Moursi, M. S. (2016). Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: A review. IET Renewable Power Generation, 10(7), 885–898. https://doi.org/10.1049/iet-rpg.2015.0477spa
dc.relation.referencesSterman, J. D. (2002). All models are wrong: Reflections on becoming a systems scientist. System Dynamics Review, 18(4), 501–531. https://doi.org/10.1002/sdr.261spa
dc.relation.referencesStoft, S. (2002). Power System Economics designing markets for electricity (John Wiley & Sone Ltd (ed.); History).spa
dc.relation.referencesStrbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030spa
dc.relation.referencesSuganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews, 16(2), 1223–1240. https://doi.org/10.1016/j.rser.2011.08.014spa
dc.relation.referencesTan, Q. F., Wen, X., Fang, G. H., Wang, Y. Q., Qin, G. H., & Li, H. M. (2020). Long-term optimal operation of cascade hydropower stations based on the utility function of the carryover potential energy. Journal of Hydrology, 580(November 2019), 124359. https://doi.org/10.1016/j.jhydrol.2019.124359spa
dc.relation.referencesTascikaraoglu, A., & Uzunoglu, M. (2014). A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews, 34, 243–254. https://doi.org/10.1016/j.rser.2014.03.033spa
dc.relation.referencesUPME. (2016). Calculadora Fecoc 2016 . http://www.upme.gov.co/calculadora_emisiones/aplicacion/acercade.htmlspa
dc.relation.referencesUPME. (2019). Proyección de precios de los energéticos para generación eléctrica enero 2019- diciembre 2039. www.upme.gov.cospa
dc.relation.referencesUPME. (2020). Informe estados de avance: Generación y Transmisión (Issue F-DI-01 – V4).spa
dc.relation.referencesUPME. (2021). Resoluciones para Liquidación de regalías. https://www1.upme.gov.co/simco/PromocionSector/Normatividad/Paginas/Resoluciones-de-Liquidacion-de-regalias.aspxspa
dc.relation.referencesUPME, & BID. (2015). Integración de las energías renovables no convencionales en Colombia. https://doi.org/10.1017/CBO9781107415324.004spa
dc.relation.referencesUPME, U. de P. M. E. (2017). Factores de emision del Sistema Interconectado Nacional Colombia - SIN. https://www1.upme.gov.co/ServicioCiudadano/Documents/Proyectos_normativos/Doc_calculo_del_FE_del_SIN_2016.docxspa
dc.relation.referencesvan der Meer, D. W., Widén, J., & Munkhammar, J. (2018). Review on probabilistic forecasting of photovoltaic power production and electricity consumption. Renewable and Sustainable Energy Reviews, 81(May 2017), 1484–1512. https://doi.org/10.1016/j.rser.2017.05.212spa
dc.relation.referencesVargas, J. (2019). Esquemas de fijación de precios para el mercado mayorista de electricidad colombiano bajo penetración de renovables. Universidad Nacional de Colombia Sede Medellín.spa
dc.relation.referencesVelásquez, J. D., Franco, C. J., & García, H. A. (2009). Un modelo no lineal para la predicción de la demanda mensual de electricidad en Colombia. Estudios Gerenciales, 25, 37–54. https://doi.org/10.1016/S0123-5923(09)70079-8spa
dc.relation.referencesXM S.A. E.S.P. (2020a). Indicadores. https://www.xm.com.co/Paginas/Indicadores/Oferta/Indicador-generacion-sin.aspxspa
dc.relation.referencesXM S.A. E.S.P. (2020b, April 25). Histórico de demanda. https://www.xm.com.co/Paginas/Consumo/historico-de-demanda.aspxspa
dc.relation.referencesXM S.A. E.S.P. (2020b, April 25). Histórico de demanda. https://www.xm.com.co/Paginas/Consumo/historico-de-demanda.aspxspa
dc.relation.referencesXM S.A. E.S.P. (2021a). Asignación subastas. https://www.xm.com.co/Paginas/Mercado-de-energia/asignacion-subastas.aspxspa
dc.relation.referencesXM S.A. E.S.P. (2021b). Capacidad efectiva por tipo de generación. http://paratec.xm.com.co/paratec/SitePages/generacion.aspx?q=capacidadspa
dc.relation.referencesXM S.A. E.S.P. (2021c). Portal BI Información inteligente XM. http://portalbissrs.xm.com.co/Paginas/Home.aspxspa
dc.relation.referencesXM S.A.S. (2020, February 6). En Colombia Factor de emisión de CO2 por generación eléctrica del Sistema Interconectado: 164.38 gramos de CO2 por kilovatio hora. https://www.xm.com.co/Paginas/detalle-noticias.aspx?identificador=2383spa
dc.relation.referencesYoo, J. H. (2009). Maximization of hydropower generation through the application of a linear programming model. Journal of Hydrology, 376(1–2), 182–187. https://doi.org/10.1016/j.jhydrol.2009.07.026spa
dc.relation.referencesYuce, B., Mourshed, M., & Rezgui, Y. (2017). A smart forecasting approach to district energy management. Energies, 10(8), 1–22. https://doi.org/10.3390/en10081073spa
dc.relation.referencesZapata, S., Castaneda, M., Jimenez, M., Julian Aristizabal, A., Franco, C. J., & Dyner, I. (2018). Long-term effects of 100% renewable generation on the Colombian power market. Sustainable Energy Technologies and Assessments, 30(July), 183–191. https://doi.org/10.1016/j.seta.2018.10.008spa
dc.relation.referencesZhang, G., & Guo, J. (2020). A Novel Method for Hourly Electricity Demand Forecasting. IEEE Transactions on Power Systems, 35(2), 1351–1363. https://doi.org/10.1109/TPWRS.2019.2941277spa
dc.relation.referencesZhang, Y., Wang, J., & Wang, X. (2014). Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews, 32, 255–270. https://doi.org/10.1016/j.rser.2014.01.033spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.ddc000 - Ciencias de la computación, información y obras generales::003 - Sistemasspa
dc.subject.lembRenewable energy sources
dc.subject.lembRecursos energéticos renovables
dc.subject.lembElectric power
dc.subject.lembEnergía eléctrica
dc.subject.proposalEnergías renovables no convencionalesspa
dc.subject.proposalSuministro de electricidadspa
dc.subject.proposalPredespacho idealspa
dc.subject.proposalNon-conventional renewable energiesspa
dc.subject.proposalElectricity supplyeng
dc.subject.proposalIdeal pre-dispatcheng
dc.titleAnálisis de la penetración de energías renovables no convencionales en el suministro de electricidad en Colombia por medio de simulación.spa
dc.title.translatedAnalysis of the penetration of non-conventional renewable energies in the electricity supply in Colombia through simulation.eng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentInvestigadoresspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.awardtitleEstrategia de Transformación del Sector Energético Colombiano en el horizonte 2030 (ENERGETICA 2030)spa
oaire.fundernameMinCienciasspa
oaire.fundernameENERGÉTICA 2030spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1035433833.2021.pdf
Tamaño:
2.09 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ingeniería - Ingeniería de Sistemas

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
3.87 KB
Formato:
Item-specific license agreed upon to submission
Descripción: