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

dc.contributorOlaya Morales, Yris
dc.contributorArango Aramburo, Santiago
dc.contributorCiencias de la Decision
dc.creatorValencia Zapata, Alexandra
dc.date.accessioned2021-10-12T20:13:19Z
dc.date.accessioned2022-09-21T19:58:37Z
dc.date.available2021-10-12T20:13:19Z
dc.date.available2022-09-21T19:58:37Z
dc.date.created2021-10-12T20:13:19Z
dc.date.issued2021
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/80522
dc.identifierUniversidad Nacional de Colombia
dc.identifierRepositorio Institucional Universidad Nacional de Colombia
dc.identifierhttps://repositorio.unal.edu.co/
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3420063
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)
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.
dc.languagespa
dc.publisherUniversidad Nacional de Colombia
dc.publisherMedellín - Minas - Maestría en Ingeniería - Ingeniería de Sistemas
dc.publisherDepartamento de la Computación y la Decisión
dc.publisherFacultad de Minas
dc.publisherMedellín, Colombia
dc.publisherUniversidad Nacional de Colombia - Sede Medellín
dc.relationAL-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.2019.109293
dc.relationAL-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.2018.02.140
dc.relationAllen, T. T. (2015). Introduction to discrete event simulation and agent- based modeling. Springe. https://doi.org/10.1007/978-0-85729-139-4 Springer
dc.relationAlmonacid, 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.2014.05.090
dc.relationAmbec, 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.2012.01.001
dc.relationAntonanzas, 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.2016.06.069
dc.relationArango-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.2018.12.057
dc.relationArias-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.2019.02.138
dc.relationAwad, 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.2019.101080
dc.relationBaldick, R. (1995). The Generalized Unit Commitment Problem. IEEE Transactions on Power Systems, 10(1), 465–475. https://doi.org/10.1109/59.373972
dc.relationBale, 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.2014.10.057
dc.relationBanks, 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.2014.12
dc.relationBeck, F., & Martinot, E. (2004). Renewable Energy Policies and Barriers. Encyclopedia of Energy, 5, 365–383. https://doi.org/10.1016/b0-12-176480-x/00488-5
dc.relationBenevit, 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.2016.48004
dc.relationBotero, 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.
dc.relationBreceda Lapeyre, M. (1990). Precios de la Electricidad: Un Debate Te6rico Para los Paises en Vias de Desarrollo. 77–113.
dc.relationBrekken, 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.2010.2066294
dc.relationBrouwer, 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.2014.01.076
dc.relationBublitz, 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.2019.01.030
dc.relationCárdenas 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.
dc.relationCarrión, 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.2006.876672
dc.relationCastaneda, 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.2017.05.179
dc.relationCebeci, 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.2018.2881036
dc.relationChabouni, 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.2020.117513
dc.relationChapagain, 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/en13102498
dc.relationChattopadhyay, 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.2013.11.035
dc.relationCherif, H., & Belhadj, J. (2014). Energy output estimation of hybrid Wind-Photovoltaic power system using statistical distributions. 2, 117–132.
dc.relationCludius, 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.2014.04.020
dc.relationCongreso de Colombia. (2014). Ley 1715 de 2014. In Ministerio de Minas y Energía (No. 1715; p. 25). http://www.fedebiocombustibles.com/files/1715.pdf
dc.relationCorrea 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.
dc.relationCREG. (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-CRG94055
dc.relationCREG. (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/$FILE/Cr025-95.pdf
dc.relationCREG. (1996). Resolución No. 086. Por la cual se reglamenta la actividad de generación con plantas menores de 20MW que se encuentra conectado al Sistema Interconectado Nacional (SIN) (p. 4). http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resoluci%25C3%25B3n-1996-CRG86-96
dc.relationCREG. (2001). Resolución No. 026. Por la cual se dictan normas sobre funcionamiento del Mercado mayorista de Energía. (No. 026). http://apolo.creg.gov.co/PUBLICAC.NSF/Indice01/Resolución-2001-CREG026-2001?OpenDocument
dc.relationCREG. (2009a). Resolución No.141. Por la cual se modifica el artículo 1 de la Resolución CREG-034 de 2001. (No. 141). http://apolo.creg.gov.co/Publicac.nsf/Indice01/Resolucion-2009-Creg141-2009
dc.relationCREG. (2009b). Resolución N° 051. Por la cual se modifica el esquema de ofertas de precios, el Despacho ideal y las reglas para determinar el precio de la Bolsa en el Mercado Energía Mayorista (pp. 1–32). http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/e93298f462402ffd0525785a007a714f/$FILE/Creg051-2009.pdf
dc.relationCREG. (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-Creg126-2010
dc.relationCREG. (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/$FILE/Creg173-2015.pdf
dc.relationCREG. (2018a). Resolución No. 003. Por la cual se resuleve una actuación administrativa iniciada en virtud de los establecido en el artículo 126 de la Ley 142 de 1994 y se ajustan los cargos regulados del sistema de transporte de TGI S:A: E.S.P. (No. 003; pp. 1–32). http://apolo.creg.gov.co/Publicac.nsf/1c09d18d2d5ffb5b05256eee00709c02/e097ea9241a3c13305258258004f0ea0/$FILE/Creg003-2018.pdf
dc.relationCREG. (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.pdf
dc.relationCREG. (2019a). Declaración de parámetros para el cálculo de la ENFICC. In Circular 027-2019. http://apolo.creg.gov.co/Publicac.nsf/52188526a7290f8505256eee0072eba7/f2eb1897273b0596052583c4007c71fe
dc.relationCREG. (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?OpenDocument
dc.relationCutler, 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.2011.06.053
dc.relationDas, 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.2012.05.025
dc.relationDasgupta, 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:19941221
dc.relationDe 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.2013.06.004
dc.relationDeng, 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.2010.5518553
dc.relationDepartamento Nacional de Planeación. (2019). La Agenda 2030 en Colombia - Objetivos de Desarrollo Sostenible. https://www.ods.gov.co/es
dc.relationDíaz 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.42-56
dc.relationDíez, P. F. (1993). Energía eólica. In Energía eólica (pp. 1–25).
dc.relationDilhani, 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.2017.7980521
dc.relationECMWF. (2021). ERA5 | ECMWF. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5
dc.relationEIA. (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.2009.07.015
dc.relationEnergética2030. (2021). Energética 2030. https://www.energetica2030.co/
dc.relationFattaheian-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/etep
dc.relationGan, 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.2015.09.029
dc.relationGaur, 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.2019.100383
dc.relationGielen, 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.2019.01.006
dc.relationGómez-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.2018.03.015
dc.relationGöransson, 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.2008.08.002
dc.relationGordon, 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.2014.12
dc.relationHartel, 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.pdf
dc.relationHashimoto, 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/WR018i001p00014
dc.relationHelm, 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.2019.01.017
dc.relationHenao, 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.2020.05.055
dc.relationHenao, 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.2018.07.099
dc.relationHenao, 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.2020.110318
dc.relationHernandez, 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.2011.02.003
dc.relationHerrera 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.pdf
dc.relationHolttinen, 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.410
dc.relationIbargüengoytia 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.2018.19n4.033
dc.relationIDEAM. (2014). Valores medios multianuales de temperatura media en C° - periodo 1981 - 2010.
dc.relationIEA. (2019). Energy security - Areas of work - IEA. https://www.iea.org/areas-of-work/ensuring-energy-security
dc.relationIlseven, 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.2019.8810598
dc.relationInternational 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.pdf
dc.relationJalilov, 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.2016.05.071
dc.relationJaramillo, 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.2004.02.010
dc.relationJensen, 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.2017.08.044
dc.relationJimenez, 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.2016.06.051
dc.relationJordán, 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.
dc.relationKaldellis, 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.2004.03.018
dc.relationKirschen, D. S., & Strbac, G. (2019). Fundamentals of power system economics (Segunda). John Wiley & Sons.
dc.relationKleijnen, 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_811b5b25697cef2f608e95c78288c11f
dc.relationKondili, 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/10.1533/9781845699628.1.81
dc.relationKong, 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.2019.106027
dc.relationKougias, 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.2015.09.073
dc.relationKruyt, 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.2009.02.006
dc.relationLannoye, 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.2014.2321793
dc.relationLebotsa, 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.2018.03.155
dc.relationLee, 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/en10111832
dc.relationLiang, 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.2016.2626253
dc.relationLiu, 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.2019.05.020
dc.relationLópez, 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.2019.10.066
dc.relationLund, 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.2008.04.003
dc.relationLund, 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.2015.01.057
dc.relationMaldonado, 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.2019.105616
dc.relationMartín Guareño, J. J. (2016). Support Vector Regression: Propiedades y aplicaciones. In Universidad de Sevilla. Departamento de Estadística e Investigación Operativa. Universidad de Sevilla.
dc.relationMartínez-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/en12132456
dc.relationMartínez 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.pdf
dc.relationMarzband, 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.2016.10.054
dc.relationMastropietro, 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.2020.111346
dc.relationMejía 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/10.22517/23447214.6605
dc.relationMohanty, 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.2015.11.078
dc.relationMokilane, 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/130206
dc.relationMorales-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.2013.2251373
dc.relationMosquera-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.2018.11.020
dc.relationNakata, 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.2010.08.001
dc.relationNERC. (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.pdf
dc.relationNewbery, 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.2018.04.025
dc.relationOECD, & IEA. (2007). Tackling Investment Challenges in Power Generation. www.iea.org/w/bookshop/pricing.html
dc.relationOkumus, 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.2016.06.053
dc.relationOrtega 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=1
dc.relationOsorio, 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.2015.09.057
dc.relationPaz, 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.
dc.relationPeláez 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/10.4324/9781315853178
dc.relationPerez, 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.2020.11.067
dc.relationPfenninger, 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.2014.02.003
dc.relationPidd, M. (2004). Computer Simulation in Management Science (5ta ed.). John Wiley & Sons.
dc.relationPinson, P. (2013). Wind energy: Forecasting challenges for its operational management. Statistical Science, 28(4), 564–585. https://doi.org/10.1214/13-STS445
dc.relationPupo-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.2019.07.135
dc.relationRau, 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=y
dc.relationResendiz Trejo, J. (2006). Las maquinas de vectores de soporte para identificación en línea. Centro de investigación y de estudios avanzados del Instituto Politécnico Nacional.
dc.relationRhoades, 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.pdf
dc.relationRueda-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.2019.02.174
dc.relationSargent, R. G. (2008). Verification and validation of simulation models. Procedings of the 2008 Winter Simulation Conference, 157–169. https://doi.org/10.1109/WSC.2008.4736065
dc.relationSchlesinger, 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/10.1177/003754977903200304
dc.relationSchwartz, 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-r36bIZA44C
dc.relationSen, S., & Kothari, D. . (1998). Optimal thermal generating unit commitment. Electrical Power & Energy Systems, 20, 443–451. https://doi.org/10.1109/TPAS.1971.293167
dc.relationSensfuß, 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.2008.03.035
dc.relationSheble, G. B., & Fahd, G. N. (1994). Unit commitment literature synopsis. IEEE Transactions on Power Systems, 9(1), 128–135. https://doi.org/10.1109/59.317549
dc.relationSobri, 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.2017.11.019
dc.relationSoroudi, 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.2013.08.039
dc.relationSsekulima, 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.2015.0477
dc.relationSterman, 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.261
dc.relationStoft, S. (2002). Power System Economics designing markets for electricity (John Wiley & Sone Ltd (ed.); History).
dc.relationStrbac, G. (2008). Demand side management: Benefits and challenges. Energy Policy, 36(12), 4419–4426. https://doi.org/10.1016/j.enpol.2008.09.030
dc.relationSuganthi, 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.2011.08.014
dc.relationTan, 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.2019.124359
dc.relationTascikaraoglu, 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.2014.03.033
dc.relationUPME. (2016). Calculadora Fecoc 2016 . http://www.upme.gov.co/calculadora_emisiones/aplicacion/acercade.html
dc.relationUPME. (2019). Proyección de precios de los energéticos para generación eléctrica enero 2019- diciembre 2039. www.upme.gov.co
dc.relationUPME. (2020). Informe estados de avance: Generación y Transmisión (Issue F-DI-01 – V4).
dc.relationUPME. (2021). Resoluciones para Liquidación de regalías. https://www1.upme.gov.co/simco/PromocionSector/Normatividad/Paginas/Resoluciones-de-Liquidacion-de-regalias.aspx
dc.relationUPME, & BID. (2015). Integración de las energías renovables no convencionales en Colombia. https://doi.org/10.1017/CBO9781107415324.004
dc.relationUPME, 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.docx
dc.relationvan 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.2017.05.212
dc.relationVargas, J. (2019). Esquemas de fijación de precios para el mercado mayorista de electricidad colombiano bajo penetración de renovables. Universidad Nacional de Colombia Sede Medellín.
dc.relationVelásquez, 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)70079-8
dc.relationXM S.A. E.S.P. (2020a). Indicadores. https://www.xm.com.co/Paginas/Indicadores/Oferta/Indicador-generacion-sin.aspx
dc.relationXM S.A. E.S.P. (2020b, April 25). Histórico de demanda. https://www.xm.com.co/Paginas/Consumo/historico-de-demanda.aspx
dc.relationXM S.A. E.S.P. (2020b, April 25). Histórico de demanda. https://www.xm.com.co/Paginas/Consumo/historico-de-demanda.aspx
dc.relationXM S.A. E.S.P. (2021a). Asignación subastas. https://www.xm.com.co/Paginas/Mercado-de-energia/asignacion-subastas.aspx
dc.relationXM S.A. E.S.P. (2021b). Capacidad efectiva por tipo de generación. http://paratec.xm.com.co/paratec/SitePages/generacion.aspx?q=capacidad
dc.relationXM S.A. E.S.P. (2021c). Portal BI Información inteligente XM. http://portalbissrs.xm.com.co/Paginas/Home.aspx
dc.relationXM 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=2383
dc.relationYoo, 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.2009.07.026
dc.relationYuce, B., Mourshed, M., & Rezgui, Y. (2017). A smart forecasting approach to district energy management. Energies, 10(8), 1–22. https://doi.org/10.3390/en10081073
dc.relationZapata, 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.2018.10.008
dc.relationZhang, 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.2019.2941277
dc.relationZhang, 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.2014.01.033
dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleAnálisis de la penetración de energías renovables no convencionales en el suministro de electricidad en Colombia por medio de simulación.
dc.typeTesis


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