Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables

En un esfuerzo por cuantificar y dar manejo a las incetidumbres dentro de los sistemas de potencia se han definido los costos de incertidumbre y se han calculado distintas funciones de costo de incertidumbre para diferentes tipos de generadores y vehículos eléctricos. Esta tesis busca emplear la for...

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Autores:: Reyes Moreno, Elkin David

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
dc.title.translated.eng.fl_str_mv	Optimal power flow extended to controllable and renewable systems and controllable loads
title	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
spellingShingle	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Flujo óptimo de potencia Costos de incertidumbre Sistemas renovables controlables Cargas controlables Programación no lineal Técnicas metaheurísticas Optimal power flow Uncertainty costs Renewable and controllable systems Controllable load Metaheuristics Non-linear programming
title_short	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
title_full	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
title_fullStr	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
title_full_unstemmed	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
title_sort	Flujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlables
dc.creator.fl_str_mv	Reyes Moreno, Elkin David
dc.contributor.advisor.none.fl_str_mv	Rivera Rodriguez, Sergio Raúl
dc.contributor.author.none.fl_str_mv	Reyes Moreno, Elkin David
dc.contributor.researchgroup.spa.fl_str_mv	EMC-UN
dc.subject.ddc.spa.fl_str_mv	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Flujo óptimo de potencia Costos de incertidumbre Sistemas renovables controlables Cargas controlables Programación no lineal Técnicas metaheurísticas Optimal power flow Uncertainty costs Renewable and controllable systems Controllable load Metaheuristics Non-linear programming
dc.subject.proposal.spa.fl_str_mv	Flujo óptimo de potencia Costos de incertidumbre Sistemas renovables controlables Cargas controlables Programación no lineal Técnicas metaheurísticas
dc.subject.proposal.eng.fl_str_mv	Optimal power flow Uncertainty costs Renewable and controllable systems Controllable load Metaheuristics Non-linear programming
description	En un esfuerzo por cuantificar y dar manejo a las incetidumbres dentro de los sistemas de potencia se han definido los costos de incertidumbre y se han calculado distintas funciones de costo de incertidumbre para diferentes tipos de generadores y vehículos eléctricos. Esta tesis busca emplear la formulación de los costos de incertidumbre para solucionar el problema del flujo de potencia óptimo extendido a sistemas renovables controlables y cargas controlables. Para lo anterior, se calcularon las primeras y segundas derivadas de las funciones de costo de incertidumbre y se incluyeron en Matpower, así, se encontró una solución analítica del flujo de potencia óptimo. Para corroborar la solución analítica se resolvió el flujo de potencia óptimo por medio de métodos metaheurísticos. Finalmente se encontró que los métodos analíticos tienen un desempeño mucho más alto que los métodos metaheurísticos, especialmente a medida que crece el número de variables de decisión en un problema de optimización. (Texto tomado de la fuente)
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-09-22T21:40:20Z
dc.date.available.none.fl_str_mv	2021-09-22T21:40:20Z
dc.date.issued.none.fl_str_mv	2021
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
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dc.type.content.spa.fl_str_mv	Text
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status_str	acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80267 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	R. Montanari, "Criteria for the economic planning of a low power hydroelectric plant,"Renewable Energy, vol. 28, no. 13, pp. 2129 - 2145, 2003. P. Cabus, "River flow prediction through rainfall-runoff modelling with a probabilitydistributed model (pdm) in flanders, belgium," Agricultural Water Management, vol. 95,no. 7, pp. 859 - 868, 2008. N. Mujere, "Flood frequency analysis using the gumbel distribution," vol. 3, pp. 2774-2778, 7 2011. N. G. Mankiw, Principios de economía (6a. ed.). Cengage Learning, 2012. A. Soroudi and T. Amraee, "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, vol. 28, pp. 376 - 384,2013. A. R. Jordehi, "How to deal with uncertainties in electric power systems? a review,"Renewable and Sustainable Energy Reviews, vol. 96, pp. 145 - 155, 2018. M. Alam, "Particle swarm optimization: Algorithm and its codes in matlab," 03 2016. Y. Song, D. Hill, and T. Liu, "Small-disturbance angle stability analysis of microgrids: A graph theory viewpoint," 09 2015. C. Zhai, H. Zhang, G. Xiao, and T. Pan, "A model predictive approach to protect power systems against cascading blackouts," International Journal of Electrical Power AND Energy Systems, 05 2019. J. Arevalo, F. Santos, and S. Rivera, "Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: mathematical expected value and verification by monte carlo simulation," International Journal of Power and Energy Conversion, vol. 10, no. 2, pp. 171-207, 2019. F. Molina, S. Perez, and S. Rivera, "Formulación de funciones de costo de incertidumbre en pequeñas centrales hidroeléctricas dentro de una microgrid," Revista Ingenierias USBMed, vol. 8, no. 1, pp. 29-36, 2017. J. Arévalo, F. Santos, and S. Rivera, "Application of analytical uncertainty costs of solar, wind and electric vehicles in optimal power dispatch," Ingenieria, vol. 22, pp. 324- 346, Sep. 2017. UPME, "Solicitudes de conexión de proyectos de generación," J. Hetzer, D. C. Yu, and K. Bhattarai, "An economic dispatch model incorporating wind power," IEEE Transactions on Energy Conversion, vol. 23, pp. 603-611, June 2008. J. Bernal, J. Neira, and S. Rivera, "Mathematical uncertainty cost functions for controllable photo-voltaic generators considering uniform distributions," WSEAS Transactions on Mathematics, vol. 18, pp. 137 - 142, 2019. S. Vargas, D. Rodriguez, and S. Rivera, "Mathematical formulation and numerical validation of uncertainty costs for controllable loads," Revista Internacional de Métodos Numéricos para Calculo y Diseño en Ingeniería, vol. 35, 2019. C. Martinez and S. Rivera, "Modelación cuadrática de costos de incertidumbre para generación renovable y su aplicación en el despacho económico," Matua, Revista de Matemática de la Universidad del Atlántico, vol. 5, no. 1, 2018. P. Li, Z. Zhou, and R. Shi, "Probabilistic optimal operation management of microgrid using point estimate method and improved bat algorithm," in 2014 IEEE PES General Meeting \| Conference Exposition, pp. 1-5, July 2014. J. Zhao, F. Wen, Z. Y. Dong, Y. Xue, and K. P. Wong, "Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization," IEEE Transactions on Industrial Informatics, vol. 8, pp. 889-899, Nov 2012. S. Rivera and J. Torres, "Optimal energy dispatch in multiple periods of time considering the variability and uncertainty of generation from renewable sources/despacho de energía óptimo en múltiples periodos de tiempo considerando la variabilidad y la incertidumbre de la generación a partir de fuentes renovables," Revista Prospectiva, vol. 16, no. 2, pp. 75-81, 2018. W. Guzman, S. Osorio, and S. Rivera, "Modelado de cargas controlables en el despacho de sistemas con fuentes renovables y vehículos eléctricos," Ingeniería y Región, no. 17, pp. 49-60, 2017. A. Peña, D. Romero, and S. Rivera, "Generation and demand scheduling in a microgrid with battery-based storage systems, hybrid renewable systems and electric vehicle aggregators," WSEAS TRANSACTIONS on POWER SYSTEMS, vol. 10, pp. 8-23, 2019. J. Torres Riveros and S. Rivera, "Despacho de energía óptimo en múltiples periodos considerando la incertidumbre de la generación a partir de fuentes renovables en un modelo reducido del sistema de potencia colombiano," AVANCES: Investigacion en Ingenieria, vol. 15, pp. 48-58, 12 2018. UPME, "Plan de expansión de generación-transmisión 2017-2031," , Unidad de planeación minero energética, 2017. E. Mojica-Nava, S. Rivera, and N. Quijano, "Distributed dispatch control in microgrids with network losses," in 2016 IEEE Conference on Control Applications (CCA), pp. 285-290, Sep. 2016. E. Mojica-Nava, S. Rivera, and N. Quijano, "Game-theoretic dispatch control in microgrids considering network losses and renewable distributed energy resources integration," IET Generation, Transmission Distribution, vol. 11, no. 6, pp. 1583-1590, 2017. XM, "El mercado de energía mayorista y su administración," Feb. 2007. CREG, "Resolución no. 112 "por la cual se reglamentan los aspectos comerciales aplicables a las transacciones internacionales de energía, que se realizan en el mercado mayorista de electricidad, como parte integrante del reglamento de operación."," Sept. 1998. A. S. Al-Sumaiti, M. H. Ahmed, S. Rivera, M. S. El Moursi, M. M. A. Salama, and T. Alsumaiti, "Stochastic pv model for power system planning applications," IET Renewable Power Generation, vol. 13, no. 16, pp. 3168-3179, 2019. S. Surender Reddy, P. R. Bijwe, and A. R. Abhyankar, "Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period," IEEE Systems Journal, vol. 9, no. 4, pp. 1440-1451, 2015. T. Chang, "Investigation on frequency distribution of global radiation using different probability density functions," International Journal of Applied Science and Engineering, vol. 8, no. 2, pp. 99-107, 2010. N. Zhang, P. K. Behera, and C. Williams, "Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks," in 2013 IEEE International Systems Conference (SysCon), pp. 280-286, 2013. J. Meng, G. Li, and Y. Du, "Economic dispatch for power systems with wind and solar energy integration considering reserve risk," in 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1-5, 2013. OFFICE of ENERGY EFFICIENCY & RENEWABLE ENERGY, "How do wind turbines survive severe storms?," Nov. 2020. Qinglei Guo, Jonghoon Han, Minhan Yoon, and Gilsoo Jang, "A study of economic dispatch with emission constraint in smart grid including wind turbines and electric vehicles," in 2012 IEEE Vehicle Power and Propulsion Conference, pp. 1002-1005, 2012. F. Xie, M. Huang, W. Zhang, and J. Li, "Research on electric vehicle charging station load forecasting," vol. 3, 10 2011. T. Sufen, Z. Youbing, and Q. Jun, "Impact of electric vehicles as interruptible load on economic dispatch incorporating wind power," in International Conference on Sustainable Power Generation and Supply (SUPERGEN 2012), pp. 1-5, 2012. A. Gomez-Exposito, A. Conejo, and C. Canizares, Electric Energy Systems: Analysis and Operation. Electric Power Engineering Series, CRC Press, 2017. R. D. Zimmerman and C. E. Murillo-Sánchez, "Matpower user’s manual," Oct. 2020. J. J. G. . W. D. Stevenson, Análisis de Sistemas de Potencia. McGraw-Hill, 1 ed., 2002. Y. Wen, C. Guo, H. Pandzíc, and D. S. Kirschen, "Enhanced security-constrained unit commitment with emerging utility-scale energy storage," IEEE Transactions on Power Systems, vol. 31, no. 1, pp. 652-662, 2016. D. Pozo, J. Contreras, and E. E. Sauma, "Unit commitment with ideal and generic energy storage units," IEEE Transactions on Power Systems, vol. 29, no. 6, pp. 2974- 2984, 2014. V. Miranda and R. Alves, "Differential evolutionary particle swarm optiization (deepso): A successful hybrid," in 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, pp. 368-374, 2013. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN’95 - International Conference on Neural Networks, vol. 4, pp. 1942-1948 vol.4, 1995. J. Arevalo, D. Medina, J. Rueda, and S. Rivera, "2018 competition on operational planning of sustainable power systems: Testsbeds and results," WSEAS Transactions on Power Systems, vol. 14, pp. 98-106, 08 2019. H. Mostapha Kalami, "Practical genetic algorithms in python and matlab - video tutorial," 2020. D. Goldberg and K. Deb, "A comparative analysis of selection schemes used in genetic algorithms," in FOGA, 1990. B. Miller and D. Goldberg, "Genetic algorithms, tournament selection, and the effects of noise," Complex Syst., vol. 9, 1995.80 Bibliografía H. Wang, C. E. Murillo-Sanchez, R. D. Zimmerman, and R. J. Thomas, "On computational issues of market-based optimal power flow," IEEE Transactions on Power Systems, vol. 22, pp. 1185-1193, Aug 2007. A. Wächter and L. T. Biegler, "On the implementation of an interior-point filter linesearch algorithm for large-scale nonlinear programming," Mathematical Programming, vol. 106, pp. 25-57, Mar 2006. I. The MathWorks, "Constrained nonlinear optimization algorithms," 2021. R. Parkinson A.R.and Balling and J. Hedengren, Optimization Methods for Engineering Design. Brigham Young University, 2 ed., 2018. E. D. Reyes, A. S. Bretas, and S. Rivera, "Marginal uncertainty cost functions for solar photovoltaic, wind energy, hydro generators, and plug-in electric vehicles," Energies, vol. 13, no. 23, 2020
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dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Eléctrica y Electrónica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rivera Rodriguez, Sergio Raúlf33aee4fc6d9088e0f3f254e2bf2d3ccReyes Moreno, Elkin Davida114e611f9c5f3935f60af4f2bd7b85dEMC-UN2021-09-22T21:40:20Z2021-09-22T21:40:20Z2021https://repositorio.unal.edu.co/handle/unal/80267Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/En un esfuerzo por cuantificar y dar manejo a las incetidumbres dentro de los sistemas de potencia se han definido los costos de incertidumbre y se han calculado distintas funciones de costo de incertidumbre para diferentes tipos de generadores y vehículos eléctricos. Esta tesis busca emplear la formulación de los costos de incertidumbre para solucionar el problema del flujo de potencia óptimo extendido a sistemas renovables controlables y cargas controlables. Para lo anterior, se calcularon las primeras y segundas derivadas de las funciones de costo de incertidumbre y se incluyeron en Matpower, así, se encontró una solución analítica del flujo de potencia óptimo. Para corroborar la solución analítica se resolvió el flujo de potencia óptimo por medio de métodos metaheurísticos. Finalmente se encontró que los métodos analíticos tienen un desempeño mucho más alto que los métodos metaheurísticos, especialmente a medida que crece el número de variables de decisión en un problema de optimización. (Texto tomado de la fuente)In order to quantify and handle the uncertainties present in power systems, uncertainty costs have been defined and several uncertainty cost functions have been calculated for different types of generators and electric vehicles. This thesis attempts to use the formulation of the uncertainty costs functions in order to solve the optimal power flow extended to renewable controllable systems and controllable loads. In order to do so, the first and second derivatives of uncertainty cost functions were calculated and included in Matpotuer, thus, an analytical solution was found for the optimal power flow. In order to validate the analytic solutions, optimal power flow was solved through metaheuristic methods. Finally, it was found that analytical methods have better performance than metaheuristic methods, especially when variable numbers become bigger in an optimization problem.MaestríaMagíster en Ingeniería - Ingeniería EléctricaSistemas de potenciaxxi, 80 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería EléctricaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaFlujo óptimo de potenciaCostos de incertidumbreSistemas renovables controlablesCargas controlablesProgramación no linealTécnicas metaheurísticasOptimal power flowUncertainty costsRenewable and controllable systemsControllable loadMetaheuristicsNon-linear programmingFlujo óptimo de potencia extendido a sistemas renovables controlables y cargas controlablesOptimal power flow extended to controllable and renewable systems and controllable loadsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMR. Montanari, "Criteria for the economic planning of a low power hydroelectric plant,"Renewable Energy, vol. 28, no. 13, pp. 2129 - 2145, 2003.P. Cabus, "River flow prediction through rainfall-runoff modelling with a probabilitydistributed model (pdm) in flanders, belgium," Agricultural Water Management, vol. 95,no. 7, pp. 859 - 868, 2008.N. Mujere, "Flood frequency analysis using the gumbel distribution," vol. 3, pp. 2774-2778, 7 2011.N. G. Mankiw, Principios de economía (6a. ed.). Cengage Learning, 2012.A. Soroudi and T. Amraee, "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, vol. 28, pp. 376 - 384,2013.A. R. Jordehi, "How to deal with uncertainties in electric power systems? a review,"Renewable and Sustainable Energy Reviews, vol. 96, pp. 145 - 155, 2018.M. Alam, "Particle swarm optimization: Algorithm and its codes in matlab," 03 2016.Y. Song, D. Hill, and T. Liu, "Small-disturbance angle stability analysis of microgrids: A graph theory viewpoint," 09 2015.C. Zhai, H. Zhang, G. Xiao, and T. Pan, "A model predictive approach to protect power systems against cascading blackouts," International Journal of Electrical Power AND Energy Systems, 05 2019.J. Arevalo, F. Santos, and S. Rivera, "Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: mathematical expected value and verification by monte carlo simulation," International Journal of Power and Energy Conversion, vol. 10, no. 2, pp. 171-207, 2019.F. Molina, S. Perez, and S. Rivera, "Formulación de funciones de costo de incertidumbre en pequeñas centrales hidroeléctricas dentro de una microgrid," Revista Ingenierias USBMed, vol. 8, no. 1, pp. 29-36, 2017.J. Arévalo, F. Santos, and S. Rivera, "Application of analytical uncertainty costs of solar, wind and electric vehicles in optimal power dispatch," Ingenieria, vol. 22, pp. 324- 346, Sep. 2017.UPME, "Solicitudes de conexión de proyectos de generación,"J. Hetzer, D. C. Yu, and K. Bhattarai, "An economic dispatch model incorporating wind power," IEEE Transactions on Energy Conversion, vol. 23, pp. 603-611, June 2008.J. Bernal, J. Neira, and S. Rivera, "Mathematical uncertainty cost functions for controllable photo-voltaic generators considering uniform distributions," WSEAS Transactions on Mathematics, vol. 18, pp. 137 - 142, 2019.S. Vargas, D. Rodriguez, and S. Rivera, "Mathematical formulation and numerical validation of uncertainty costs for controllable loads," Revista Internacional de Métodos Numéricos para Calculo y Diseño en Ingeniería, vol. 35, 2019.C. Martinez and S. Rivera, "Modelación cuadrática de costos de incertidumbre para generación renovable y su aplicación en el despacho económico," Matua, Revista de Matemática de la Universidad del Atlántico, vol. 5, no. 1, 2018.P. Li, Z. Zhou, and R. Shi, "Probabilistic optimal operation management of microgrid using point estimate method and improved bat algorithm," in 2014 IEEE PES General Meeting \| Conference Exposition, pp. 1-5, July 2014.J. Zhao, F. Wen, Z. Y. Dong, Y. Xue, and K. P. Wong, "Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization," IEEE Transactions on Industrial Informatics, vol. 8, pp. 889-899, Nov 2012.S. Rivera and J. Torres, "Optimal energy dispatch in multiple periods of time considering the variability and uncertainty of generation from renewable sources/despacho de energía óptimo en múltiples periodos de tiempo considerando la variabilidad y la incertidumbre de la generación a partir de fuentes renovables," Revista Prospectiva, vol. 16, no. 2, pp. 75-81, 2018.W. Guzman, S. Osorio, and S. Rivera, "Modelado de cargas controlables en el despacho de sistemas con fuentes renovables y vehículos eléctricos," Ingeniería y Región, no. 17, pp. 49-60, 2017.A. Peña, D. Romero, and S. Rivera, "Generation and demand scheduling in a microgrid with battery-based storage systems, hybrid renewable systems and electric vehicle aggregators," WSEAS TRANSACTIONS on POWER SYSTEMS, vol. 10, pp. 8-23, 2019.J. Torres Riveros and S. Rivera, "Despacho de energía óptimo en múltiples periodos considerando la incertidumbre de la generación a partir de fuentes renovables en un modelo reducido del sistema de potencia colombiano," AVANCES: Investigacion en Ingenieria, vol. 15, pp. 48-58, 12 2018.UPME, "Plan de expansión de generación-transmisión 2017-2031," , Unidad de planeación minero energética, 2017.E. Mojica-Nava, S. Rivera, and N. Quijano, "Distributed dispatch control in microgrids with network losses," in 2016 IEEE Conference on Control Applications (CCA), pp. 285-290, Sep. 2016.E. Mojica-Nava, S. Rivera, and N. Quijano, "Game-theoretic dispatch control in microgrids considering network losses and renewable distributed energy resources integration," IET Generation, Transmission Distribution, vol. 11, no. 6, pp. 1583-1590, 2017.XM, "El mercado de energía mayorista y su administración," Feb. 2007.CREG, "Resolución no. 112 "por la cual se reglamentan los aspectos comerciales aplicables a las transacciones internacionales de energía, que se realizan en el mercado mayorista de electricidad, como parte integrante del reglamento de operación."," Sept. 1998.A. S. Al-Sumaiti, M. H. Ahmed, S. Rivera, M. S. El Moursi, M. M. A. Salama, and T. Alsumaiti, "Stochastic pv model for power system planning applications," IET Renewable Power Generation, vol. 13, no. 16, pp. 3168-3179, 2019.S. Surender Reddy, P. R. Bijwe, and A. R. Abhyankar, "Real-time economic dispatch considering renewable power generation variability and uncertainty over scheduling period," IEEE Systems Journal, vol. 9, no. 4, pp. 1440-1451, 2015.T. Chang, "Investigation on frequency distribution of global radiation using different probability density functions," International Journal of Applied Science and Engineering, vol. 8, no. 2, pp. 99-107, 2010.N. Zhang, P. K. Behera, and C. Williams, "Solar radiation prediction based on particle swarm optimization and evolutionary algorithm using recurrent neural networks," in 2013 IEEE International Systems Conference (SysCon), pp. 280-286, 2013.J. Meng, G. Li, and Y. Du, "Economic dispatch for power systems with wind and solar energy integration considering reserve risk," in 2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1-5, 2013.OFFICE of ENERGY EFFICIENCY & RENEWABLE ENERGY, "How do wind turbines survive severe storms?," Nov. 2020.Qinglei Guo, Jonghoon Han, Minhan Yoon, and Gilsoo Jang, "A study of economic dispatch with emission constraint in smart grid including wind turbines and electric vehicles," in 2012 IEEE Vehicle Power and Propulsion Conference, pp. 1002-1005, 2012.F. Xie, M. Huang, W. Zhang, and J. Li, "Research on electric vehicle charging station load forecasting," vol. 3, 10 2011.T. Sufen, Z. Youbing, and Q. Jun, "Impact of electric vehicles as interruptible load on economic dispatch incorporating wind power," in International Conference on Sustainable Power Generation and Supply (SUPERGEN 2012), pp. 1-5, 2012.A. Gomez-Exposito, A. Conejo, and C. 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Rivera, "Marginal uncertainty cost functions for solar photovoltaic, wind energy, hydro generators, and plug-in electric vehicles," Energies, vol. 13, no. 23, 2020Público generalLICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/80267/3/license.txtcccfe52f796b7c63423298c2d3365fc6MD53ORIGINAL1030633993.2021.pdf1030633993.2021.pdfTesis de Maestría en Ingeniería Electrícaapplication/pdf801470https://repositorio.unal.edu.co/bitstream/unal/80267/4/1030633993.2021.pdf269c780a7b49eece853b010d53dfd75aMD54THUMBNAIL1030633993.2021.pdf.jpg1030633993.2021.pdf.jpgGenerated Thumbnailimage/jpeg4283https://repositorio.unal.edu.co/bitstream/unal/80267/5/1030633993.2021.pdf.jpg150a206463f2a63c7bf11d76acd9827fMD55unal/80267oai:repositorio.unal.edu.co:unal/802672024-07-29 23:12:46.835Repositorio Institucional Universidad Nacional de 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GVyZWNob3MgZGUgYXV0b3IgcXVlIGNvbmxsZXZlIGxhIGRpc3RyaWJ1Y2nDs24gZGUgZXN0b3MgYXJjaGl2b3MgeSBtZXRhZGF0b3MuCkFsIGhhY2VyIGNsaWMgZW4gZWwgc2lndWllbnRlIGJvdMOzbiwgdXN0ZWQgaW5kaWNhIHF1ZSBlc3TDoSBkZSBhY3VlcmRvIGNvbiBlc3RvcyB0w6lybWlub3MuCgpVTklWRVJTSURBRCBOQUNJT05BTCBERSBDT0xPTUJJQSAtIMOabHRpbWEgbW9kaWZpY2FjacOzbiAyNy8yMC8yMDIwCg==

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