Revisión general de respuesta a la demanda y sus implicaciones

#RespuestaADemanda

Autores:

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad de Bogotá Jorge Tadeo Lozano

Repositorio:: Expeditio: repositorio UTadeo

Idioma:: spa

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dc.title.spa.fl_str_mv	Revisión general de respuesta a la demanda y sus implicaciones
title	Revisión general de respuesta a la demanda y sus implicaciones
spellingShingle	Revisión general de respuesta a la demanda y sus implicaciones Demanda Energia Demanda de energía Empresas de energía Recursos energéticos Response demand Control Energy management systems Algorithm Power renewable
title_short	Revisión general de respuesta a la demanda y sus implicaciones
title_full	Revisión general de respuesta a la demanda y sus implicaciones
title_fullStr	Revisión general de respuesta a la demanda y sus implicaciones
title_full_unstemmed	Revisión general de respuesta a la demanda y sus implicaciones
title_sort	Revisión general de respuesta a la demanda y sus implicaciones
dc.contributor.advisor.none.fl_str_mv	Aristizábal Cardona, Andrés Julián
dc.subject.spa.fl_str_mv	Demanda Energia
topic	Demanda Energia Demanda de energía Empresas de energía Recursos energéticos Response demand Control Energy management systems Algorithm Power renewable
dc.subject.lemb.spa.fl_str_mv	Demanda de energía Empresas de energía Recursos energéticos
dc.subject.keyword.spa.fl_str_mv	Response demand Control Energy management systems Algorithm Power renewable
description	#RespuestaADemanda
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-02-10T20:53:18Z
dc.date.available.none.fl_str_mv	2021-02-10T20:53:18Z
dc.date.created.none.fl_str_mv	2021
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url	http://hdl.handle.net/20.500.12010/17197
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	A. V. Escobar., “Gestión de la energía eléctrica domiciliaria con base en la Gestión Activa de la Demanda. Tesis Doctoral. Universidad Distrital Francisco José de Caldas.,” Universidad Distrital Francisco Jose de caldas, 2016. V. Orejuela, D. Arias, and A. Aguila, “Response of Residential Electricity Demand Against Price Signals in Ecuador,” Proc. 2015 IEEE Thirty Fifth Cent. Am. Panama Conv., no. Concapan XXXV, pp. 373–378, 2015. M. A. Fotouhi Ghazvini, J. Soares, O. Abrishambaf, R. Castro, and Z. Vale, “Demand response implementation in smart households,” Energy Build., vol. 143, pp. 129–148, 2017. A. Arteconi, D. Patteeuw, K. Bruninx, E. Delarue, W. D’haeseleer, and L. Helsen, “Active demand response with electric heating systems: Impact of market penetration,” Appl. Energy, vol. 177, pp. 636–648, 2016. D. Hurley, P. Peterson, and M. Whited, “Demand Response as a Power System Resource: Program Designs, Performance, and Lessons Learned in the United States,” Regulatory Assistance Project, no. May. p. 76, 2013. F. Lauro, F. Moretti, A. Capozzoli, and S. Panzieri, “Model predictive control for building active demand response systems,” Energy Procedia, vol. 83, pp. 494– 503, 2015. J. A. P. Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, and N. Jenkins, “Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities,” Electr. Power Syst. Res., vol. 77, no. 9, pp. 1189–1203, 2007. [8] R. Schleicher-Tappeser R. Schleicher-Tappeser, “How renewables will change electricity markets in the next five years,” Energy Policy, vol. 48, pp. 64–75, 2012. C. A. Ramírez Escobar, “Los Precios del Mercado Mayorista de Electricidad como Expresión de la Participación Activa de la Demanda: Aplicación de la Economía Experimental,” UNIVERSIDAD POLITÉCNICA DE VALENCIA, 2012. M. Curtis, J. Torriti, and S. T. Smith, “A comparative analysis of building energy estimation methods in the context of demand response,” Energy Build., vol. 174, pp. 13–25, 2018. M. Vallés Rodriguez, P. Frías Marín, J. Reneses Guillén, and L. González Sotres, “Gestión Activa de la Demanda para una Europa más eficiente,” Anales De Mecánica Y Electricidad, no. 2009, pp. 55–61, 2013. M. J. Fell, D. Shipworth, G. M. Huebner, and C. A. Elwell, “Public acceptability of domestic demand-side response in Great Britain: The role of automation and direct load control,” Energy Res. Soc. Sci., vol. 9, pp. 72–84, 2015. G. Prinsloo, R. Dobson, and K. Schreve, “Carbon footprint optimization as PLC control strategy in solar power system automation,” Energy Procedia, vol. 49, pp. 2180–2190, 2013. M. Humayun, A. Safdarian, M. Ali, M. Z. Degefa, and M. Lehtonen, “Optimal capacity planning of substation transformers by demand response combined with network automation,” Electr. Power Syst. Res., vol. 134, pp. 176–185, 2016. C. Girón, F. J. Rodríguez, L. Giménez de Urtasum, and S. Borroy, “Assessing the contribution of automation to the electric distribution network reliability,” Int. J. Electr. Power Energy Syst., vol. 97, no. October 2017, pp. 120–126, 2018. K. Kostková, Ľ. Omelina, P. Kyčina, and P. Jamrich, “An introduction to load management,” Electr. Power Syst. Res., vol. 95, pp. 184–191, 2013. S. Yao, H. Liu, D. Lu, and X. Yuan, “Time-space Characteristic of Interruptible Load on Dispatch Solution,” IFAC-PapersOnLine, vol. 48, no. 28, pp. 1355– 1358, 2015. X. Jian, L. Zhang, X. Miao, Y. Zhang, and X. Han, “Designing interruptible load management scheme based on customer performance using mechanism design theory,” Int. J. Electr. Power Energy Syst., vol. 95, pp. 476–489, 2018. M. T. Angulo and J. X. Velasco-Hernandez, “Robust qualitative estimation of time-varying contact rates in uncertain epidemics,” Epidemics, vol. 24, no. April 2017, pp. 98–104, 2018. C. Águila et al., “Propuesta de mejora en la gestión de empresas del rubro de distribución de energía eléctrica Propuesta de mejora en la Gestión de compras de un grupo de empresas del rubro de distribución de energía eléctrica Para optar por el grado académico de Maestro e,” UNIVERSIDAD PERUANA DE CIENCIAS APLICADAS ESCUELA, 2018. A. Faruqui, S. Sergici, and C. Warner, “Arcturus 2.0: A meta-analysis of timevarying rates for electricity,” Electr. J., vol. 30, no. 10, pp. 64–72, 2017. K. Thirumala, Shantanu, T. Jain, and A. C. Umarikar, “Visualizing time-varying power quality indices using generalized empirical wavelet transform,” Electr. Power Syst. Res., vol. 143, pp. 99–109, 2017 X. Zhao, H. Bai, and J. Hao, “Research on the load shifting potential of on-site power plants with byproduct gasholders in steel enterprises under time-of-use power price,” Energy Procedia, vol. 142, pp. 2704–2710, 2017. J. M. Bright, S. Killinger, D. Lingfors, and N. A. Engerer, “Improved satellitederived PV power nowcasting using real-time power data from reference PV systems,” Sol. Energy, vol. 168, no. November 2017, pp. 118–139, 2018. M. F. Hung and T. H. Huang, “Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing,” Energy Econ., vol. 48, pp. 168–177, 2015. H. T. Haider, O. H. See, and W. Elmenreich, “Residential demand response scheme based on adaptive consumption level pricing,” Energy, vol. 113, pp. 301–308, 2016. M. Fera, R. Macchiaroli, R. Iannone, S. Miranda, and S. Riemma, “Economic evaluation model for the energy Demand Response,” Energy, vol. 112, pp. 457– 468, 2016. J. Ko and D. Kim, “Employer-based travel demand management program: Employer’s choice and effectiveness,” Transp. Policy, vol. 59, no. June, pp. 1–9, 2017. F. Zaeim-Kohan, H. Razmi, and H. Doagou-Mojarrad, “Multi-objective transmission congestion management considering demand response programs and generation rescheduling,” Appl. Soft Comput. J., vol. 70, pp. 169–181, 2018. V. Guti, T. H. Ach, I. Hidr, and L. Habana, “La formación de ingenieros desde el enfoque Ciencia , Tecnología y Sociedad The training of engineers Technology and Society from the approach on Science ,” La Habana, Cuba, pp. 16–28, 2017. F. Wang, H. Xu, T. Xu, K. Li, M. Shafie-khah, and J. P. S. Catalï¿½o, “The values of market-based demand response on improving power system reliability under extreme circumstances,” Appl. Energy, vol. 193, pp. 220–231, 2017. S. S. S. B. Yecid A. Muñoz Maldonado1 , Ronald F. Güiza Pineda2, “ANÁLISIS DE INTEGRACIÓN REGIONAL CON FUENTES DE ENERGÍA RENOVABLES EN AMÉRICA LATINA Y EL CARIBE (ALC),” ENERLAC., Bucaramanga, colombia, pp. 106–125, 2017. D. J. M. Gers1, “AMÉRICA LATINA Y EL CARIBE: ESTADO DEL ARTE DE LAS REDES ELÉCTRICAS INTELIGENTES,” Recibido: 01/nov/2016 y Aceptado: 30/jun/2017 ENERLAC. Volumen I. Número 1. Octubre, 2017 (24- 41), Quito, Ecuador, pp. 24–41, 2017. Y. Jiang et al., “Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system,” Appl. Energy, vol. 190, pp. 1126–1137, 2017. S. Nojavan, M. Majidi, A. Najafi-Ghalelou, M. Ghahramani, and K. Zare, “A cost-emission presence of demand response program: ε-constraint method and fuzzy satisfying approach,” Energy Convers. Manag., vol. 138, pp. 383–392, 2017. model for fuel cell/PV/battery hybrid energy system in the CIDEL, “Congreso INternaciónal de Distribución Eléctrica 22,23 y 24 de Septiembre de 2014,” CIDEL 2014, 2014. [Online]. Available: http://www.cidel2014.com/sesiones.asp?lang=esp. D. J. M. Gers1, “AMÉRICA LATINA Y EL CARIBE: ESTADO DEL ARTE DE LAS REDES ELÉCTRICAS INTELIGENTES,” Recibido 1/ Noviembre/2016 y Aceptado 30/junio/2017, p. 21, 2017. G. R. Aghajani, H. A. Shayanfar, and H. Shayeghi, “Demand side management in a smart micro-grid in the presence of renewable generation and demand response,” Energy, vol. 126, pp. 622–637, 2017. T. H. Pedersen, R. E. Hedegaard, and S. Petersen, “Space heating demand response potential of retrofitted residential apartment blocks,” Energy Build., vol. 141, pp. 158–166, 2017. E. Nyholm, M. Odenberger, and F. Johnsson, “An economic assessment of distributed solar PV generation in Sweden from a consumer perspective – The impact of demand response,” Renewable Energy, vol. 108, Elsevier Ltd, Göteborg, Sweden, pp. 169–178, Jun-2017. M. Qadrdan, M. Cheng, J. Wu, and N. Jenkins, “Benefits of demand-side response in combined gas and electricity networks,” Appl. Energy, vol. 192, pp. 360–369, 2017. A. Chauhan and R. P. Saini, “Size optimization and demand response of a standalone integrated renewable energy system,” Energy, vol. 124, Elsevier B.V., India, pp. 59–73, 2017. J. Wang, H. Zhong, X. Lai, Q. Xia, C. Shu, and C. Kang, Distributed real-time demand response based on Lagrangian multiplier optimal selection approach, vol. 190. Lisbon, Portugal: Elsevier Ltd, 2017. S. Nojavan, M. Majidi, and K. Zare, “Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program,” Int. J. Hydrogen Energy, vol. 42, no. 16, pp. 11857–11867, 2017. E. Reihani, M. Motalleb, M. Thornton, and R. Ghorbani, “A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture,” Appl. Energy, vol. 183, pp. 445–455, 2016. B. Baeten, F. Rogiers, and L. Helsen, “Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response,” Appl. Energy, vol. 195, pp. 184–195, 2017. X. Han, S. You, and H. Bindner, “Critical kick-back mitigation through improved design of demand response,” Appl. Therm. Eng., vol. 114, pp. 1507– 1514, 2017. T. M. Lawrence, R. T. Watson, and M. Boudreau, “Data flow requirements for integrating smart buildings and a smart grid through model predictive control,” Procedia Eng., vol. 180, pp. 1402–1412, 2017. A. M. Kosek, G. T. Costanzo, H. W. Bindner, and O. Gehrke, “An Overview of Demand Side Management Control Schemes for Buildings in Smart Grids.” D. Behrens, T. Schoormann, and R. Knackstedt, “Developing an Algorithm to Consider Mutliple Demand Response Objectives Applying an Algorithm Engineering-Oriented Approach for the Residential Context,” vol. 8, no. 1, pp. 2621–2626, 2017. M. Pipattanasomporn, S. Member, M. Kuzlu, and S. Rahman, “An Algorithm for Intelligent Home Energy Management and Demand Response Analysis,” pp. 1– 8, 2012. R. B. M. P. Author, Residential Demand Response Algorithms: State-of-theArt, Key Issues and Challenges, 1st ed. University, Incheon, South Korea: IEEE Transactions on Smart Grid. T. Yanan, D. Srinivasan, and A. Trivedi, “Multi Objective Optimal Energy Consumption Scheduling in Smart Grids,” 2017. J. M. Lujano-rojas and J. Contreras, “Multi-objective demand response to realtime prices ( RTP ) using a task scheduling methodology s Cort e Tom a,” vol. 138, 2017. A. R. Hevner, S. T. March, J. Park, and S. Ram, “Design Science in Information Systems Research,” vol. 28, no. 1, seul, korea, pp. 75–105, 2013. T. Yang, Y. Zhao, H. Pen, and Z. Wang, “Data center holistic demand response algorithm to smooth microgrid tie-line power fl uctuation,” Appl. Energy, vol. 231, no. December 2017, pp. 277–287, 2018. G. K. Chellamani and P. V. Chandramani, “Demand Response Management System with Discrete Time Window using Supervised Learning Algorithm,” Cogn. Syst. Res., p. 11, 2018. B. Sivaneasan, N. K. Kandasamy, M. L. Lim, and K. P. Goh, “A new demand response algorithm for solar PV intermittency management ☆,” Appl. Energy, vol. 218, no. March, pp. 36–45, 2018. M. Conceptual, J. José, M. Flórez, R. Andres, E. Martinez, and R. Ferreira, “Parte I Antecedentes y Marco Conceptual del Análisis, Evaluación y Recomendaciones para la Implementación de Redes Inteligentes en Colombia,” Bogota, 2016. J. D. Morcillo, C. J. Franco, and F. Angulo, “Simulation of demand growth scenarios in the Colombian electricity market : An integration of system dynamics and dynamic systems,” Appl. Energy, vol. 216, no. February, pp. 504– 520, 2018. L. Alejandro, A. Barragán, E. R. Trujillo, and F. Santamaría, “Respuesta de la demanda en el mercado eléctrico Colombiano : modelado e implementación web Demand response in the Colombian electricity market : modeling and web implementation,” p. 22, 2018. M. Z. Carvajal, “Políticas para la autogestión de electricidad en el sector residencial urbano de Colombia,” Universidad Nacional de Colombia, 2014. Á. R. Restrepo, S. E. Nope, and D. E. Enríquez, “Beneficios Económicos de la Gestión de la Demanda y la Energía Autogenerada en el Contexto de la Regulación Colombiana Economic Benefits of Demand Management and SelfGenerated energy in the Context of Colombian Regulations,” vol. 29, no. 1, pp. 105–116, 2018. J. Sebastián, G. Marín, J. Sebastián, and G. Marín, “Propuesta de implementación de programas de gestión de demanda de energía eléctrica para el sector residencial en Colombia Propuesta de implementación de programas de gestión de demanda de energía eléctrica para el sector residencial en Colombia,” Universidad Nacional de colombia, 2015. H. Cui and K. Zhou, “Industrial power load scheduling considering demand response,” J. Clean. Prod., vol. 204, pp. 447–460, 2018. E. congreso de Colombia, Ley 1715 - 13 de Mayo 2014 Por Medio de la cual se Regula a la Integración de las Energías Renovables no convencionales al Sistema Energético Nacional, no. May. Bogot, 2014. N. Oconnell, P. Pinson, H. Madsen, and M. Omalley, “Benefits and challenges of electrical demand response: A critical review,” Renew. Sustain. Energy Rev., vol. 39, pp. 686–699, 2014.
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spelling	Aristizábal Cardona, Andrés JuliánMoncada Pinzón, Favian LeonardoMagíster en Ingeniería - Gestión Sostenible de la Energía2021-02-10T20:53:18Z2021-02-10T20:53:18Z2021http://hdl.handle.net/20.500.12010/1719719 páginasapplication/pdfspaUniversidad de Bogotá Jorge Tadeo LozanoDemandaEnergiaDemanda de energíaEmpresas de energíaRecursos energéticosResponse demandControlEnergy management systemsAlgorithmPower renewableRevisión general de respuesta a la demanda y sus implicacionesAbierto (Texto Completo)http://purl.org/coar/access_right/c_abf2A. V. Escobar., “Gestión de la energía eléctrica domiciliaria con base en la Gestión Activa de la Demanda. Tesis Doctoral. Universidad Distrital Francisco José de Caldas.,” Universidad Distrital Francisco Jose de caldas, 2016.V. Orejuela, D. Arias, and A. Aguila, “Response of Residential Electricity Demand Against Price Signals in Ecuador,” Proc. 2015 IEEE Thirty Fifth Cent. Am. Panama Conv., no. Concapan XXXV, pp. 373–378, 2015.M. A. Fotouhi Ghazvini, J. Soares, O. Abrishambaf, R. Castro, and Z. Vale, “Demand response implementation in smart households,” Energy Build., vol. 143, pp. 129–148, 2017.A. Arteconi, D. Patteeuw, K. Bruninx, E. Delarue, W. D’haeseleer, and L. Helsen, “Active demand response with electric heating systems: Impact of market penetration,” Appl. Energy, vol. 177, pp. 636–648, 2016.D. Hurley, P. Peterson, and M. Whited, “Demand Response as a Power System Resource: Program Designs, Performance, and Lessons Learned in the United States,” Regulatory Assistance Project, no. May. p. 76, 2013.F. Lauro, F. Moretti, A. Capozzoli, and S. Panzieri, “Model predictive control for building active demand response systems,” Energy Procedia, vol. 83, pp. 494– 503, 2015.J. A. P. Lopes, N. Hatziargyriou, J. Mutale, P. Djapic, and N. Jenkins, “Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities,” Electr. Power Syst. Res., vol. 77, no. 9, pp. 1189–1203, 2007. [8] R. Schleicher-TappeserR. Schleicher-Tappeser, “How renewables will change electricity markets in the next five years,” Energy Policy, vol. 48, pp. 64–75, 2012.C. A. Ramírez Escobar, “Los Precios del Mercado Mayorista de Electricidad como Expresión de la Participación Activa de la Demanda: Aplicación de la Economía Experimental,” UNIVERSIDAD POLITÉCNICA DE VALENCIA, 2012.M. Curtis, J. Torriti, and S. T. Smith, “A comparative analysis of building energy estimation methods in the context of demand response,” Energy Build., vol. 174, pp. 13–25, 2018.M. Vallés Rodriguez, P. Frías Marín, J. Reneses Guillén, and L. González Sotres, “Gestión Activa de la Demanda para una Europa más eficiente,” Anales De Mecánica Y Electricidad, no. 2009, pp. 55–61, 2013.M. J. Fell, D. Shipworth, G. M. Huebner, and C. A. Elwell, “Public acceptability of domestic demand-side response in Great Britain: The role of automation and direct load control,” Energy Res. Soc. Sci., vol. 9, pp. 72–84, 2015.G. Prinsloo, R. Dobson, and K. Schreve, “Carbon footprint optimization as PLC control strategy in solar power system automation,” Energy Procedia, vol. 49, pp. 2180–2190, 2013.M. Humayun, A. Safdarian, M. Ali, M. Z. Degefa, and M. Lehtonen, “Optimal capacity planning of substation transformers by demand response combined with network automation,” Electr. Power Syst. Res., vol. 134, pp. 176–185, 2016.C. Girón, F. J. Rodríguez, L. Giménez de Urtasum, and S. Borroy, “Assessing the contribution of automation to the electric distribution network reliability,” Int. J. Electr. Power Energy Syst., vol. 97, no. October 2017, pp. 120–126, 2018.K. Kostková, Ľ. Omelina, P. Kyčina, and P. Jamrich, “An introduction to load management,” Electr. Power Syst. Res., vol. 95, pp. 184–191, 2013.S. Yao, H. Liu, D. Lu, and X. Yuan, “Time-space Characteristic of Interruptible Load on Dispatch Solution,” IFAC-PapersOnLine, vol. 48, no. 28, pp. 1355– 1358, 2015.X. Jian, L. Zhang, X. Miao, Y. Zhang, and X. Han, “Designing interruptible load management scheme based on customer performance using mechanism design theory,” Int. J. Electr. Power Energy Syst., vol. 95, pp. 476–489, 2018.M. T. Angulo and J. X. Velasco-Hernandez, “Robust qualitative estimation of time-varying contact rates in uncertain epidemics,” Epidemics, vol. 24, no. April 2017, pp. 98–104, 2018.C. Águila et al., “Propuesta de mejora en la gestión de empresas del rubro de distribución de energía eléctrica Propuesta de mejora en la Gestión de compras de un grupo de empresas del rubro de distribución de energía eléctrica Para optar por el grado académico de Maestro e,” UNIVERSIDAD PERUANA DE CIENCIAS APLICADAS ESCUELA, 2018.A. Faruqui, S. Sergici, and C. Warner, “Arcturus 2.0: A meta-analysis of timevarying rates for electricity,” Electr. J., vol. 30, no. 10, pp. 64–72, 2017.K. Thirumala, Shantanu, T. Jain, and A. C. Umarikar, “Visualizing time-varying power quality indices using generalized empirical wavelet transform,” Electr. Power Syst. Res., vol. 143, pp. 99–109, 2017X. Zhao, H. Bai, and J. Hao, “Research on the load shifting potential of on-site power plants with byproduct gasholders in steel enterprises under time-of-use power price,” Energy Procedia, vol. 142, pp. 2704–2710, 2017.J. M. Bright, S. Killinger, D. Lingfors, and N. A. Engerer, “Improved satellitederived PV power nowcasting using real-time power data from reference PV systems,” Sol. Energy, vol. 168, no. November 2017, pp. 118–139, 2018.M. F. Hung and T. H. Huang, “Dynamic demand for residential electricity in Taiwan under seasonality and increasing-block pricing,” Energy Econ., vol. 48, pp. 168–177, 2015.H. T. Haider, O. H. See, and W. Elmenreich, “Residential demand response scheme based on adaptive consumption level pricing,” Energy, vol. 113, pp. 301–308, 2016.M. Fera, R. Macchiaroli, R. Iannone, S. Miranda, and S. Riemma, “Economic evaluation model for the energy Demand Response,” Energy, vol. 112, pp. 457– 468, 2016.J. Ko and D. Kim, “Employer-based travel demand management program: Employer’s choice and effectiveness,” Transp. Policy, vol. 59, no. June, pp. 1–9, 2017.F. Zaeim-Kohan, H. Razmi, and H. Doagou-Mojarrad, “Multi-objective transmission congestion management considering demand response programs and generation rescheduling,” Appl. Soft Comput. J., vol. 70, pp. 169–181, 2018.V. Guti, T. H. Ach, I. Hidr, and L. Habana, “La formación de ingenieros desde el enfoque Ciencia , Tecnología y Sociedad The training of engineers Technology and Society from the approach on Science ,” La Habana, Cuba, pp. 16–28, 2017.F. Wang, H. Xu, T. Xu, K. Li, M. Shafie-khah, and J. P. S. Catalï¿½o, “The values of market-based demand response on improving power system reliability under extreme circumstances,” Appl. Energy, vol. 193, pp. 220–231, 2017.S. S. S. B. Yecid A. Muñoz Maldonado1 , Ronald F. Güiza Pineda2, “ANÁLISIS DE INTEGRACIÓN REGIONAL CON FUENTES DE ENERGÍA RENOVABLES EN AMÉRICA LATINA Y EL CARIBE (ALC),” ENERLAC., Bucaramanga, colombia, pp. 106–125, 2017.D. J. M. Gers1, “AMÉRICA LATINA Y EL CARIBE: ESTADO DEL ARTE DE LAS REDES ELÉCTRICAS INTELIGENTES,” Recibido: 01/nov/2016 y Aceptado: 30/jun/2017 ENERLAC. Volumen I. Número 1. Octubre, 2017 (24- 41), Quito, Ecuador, pp. 24–41, 2017.Y. Jiang et al., “Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system,” Appl. Energy, vol. 190, pp. 1126–1137, 2017.S. Nojavan, M. Majidi, A. Najafi-Ghalelou, M. Ghahramani, and K. Zare, “A cost-emission presence of demand response program: ε-constraint method and fuzzy satisfying approach,” Energy Convers. Manag., vol. 138, pp. 383–392, 2017. model for fuel cell/PV/battery hybrid energy system in theCIDEL, “Congreso INternaciónal de Distribución Eléctrica 22,23 y 24 de Septiembre de 2014,” CIDEL 2014, 2014. [Online]. Available: http://www.cidel2014.com/sesiones.asp?lang=esp.D. J. M. Gers1, “AMÉRICA LATINA Y EL CARIBE: ESTADO DEL ARTE DE LAS REDES ELÉCTRICAS INTELIGENTES,” Recibido 1/ Noviembre/2016 y Aceptado 30/junio/2017, p. 21, 2017.G. R. Aghajani, H. A. Shayanfar, and H. Shayeghi, “Demand side management in a smart micro-grid in the presence of renewable generation and demand response,” Energy, vol. 126, pp. 622–637, 2017.T. H. Pedersen, R. E. Hedegaard, and S. Petersen, “Space heating demand response potential of retrofitted residential apartment blocks,” Energy Build., vol. 141, pp. 158–166, 2017.E. Nyholm, M. Odenberger, and F. Johnsson, “An economic assessment of distributed solar PV generation in Sweden from a consumer perspective – The impact of demand response,” Renewable Energy, vol. 108, Elsevier Ltd, Göteborg, Sweden, pp. 169–178, Jun-2017.M. Qadrdan, M. Cheng, J. Wu, and N. Jenkins, “Benefits of demand-side response in combined gas and electricity networks,” Appl. Energy, vol. 192, pp. 360–369, 2017.A. Chauhan and R. P. Saini, “Size optimization and demand response of a standalone integrated renewable energy system,” Energy, vol. 124, Elsevier B.V., India, pp. 59–73, 2017.J. Wang, H. Zhong, X. Lai, Q. Xia, C. Shu, and C. Kang, Distributed real-time demand response based on Lagrangian multiplier optimal selection approach, vol. 190. Lisbon, Portugal: Elsevier Ltd, 2017.S. Nojavan, M. Majidi, and K. Zare, “Risk-based optimal performance of a PV/fuel cell/battery/grid hybrid energy system using information gap decision theory in the presence of demand response program,” Int. J. Hydrogen Energy, vol. 42, no. 16, pp. 11857–11867, 2017.E. Reihani, M. Motalleb, M. Thornton, and R. Ghorbani, “A novel approach using flexible scheduling and aggregation to optimize demand response in the developing interactive grid market architecture,” Appl. Energy, vol. 183, pp. 445–455, 2016.B. Baeten, F. Rogiers, and L. Helsen, “Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response,” Appl. Energy, vol. 195, pp. 184–195, 2017.X. Han, S. You, and H. Bindner, “Critical kick-back mitigation through improved design of demand response,” Appl. Therm. Eng., vol. 114, pp. 1507– 1514, 2017.T. M. Lawrence, R. T. Watson, and M. Boudreau, “Data flow requirements for integrating smart buildings and a smart grid through model predictive control,” Procedia Eng., vol. 180, pp. 1402–1412, 2017.A. M. Kosek, G. T. Costanzo, H. W. Bindner, and O. Gehrke, “An Overview of Demand Side Management Control Schemes for Buildings in Smart Grids.”D. Behrens, T. Schoormann, and R. Knackstedt, “Developing an Algorithm to Consider Mutliple Demand Response Objectives Applying an Algorithm Engineering-Oriented Approach for the Residential Context,” vol. 8, no. 1, pp. 2621–2626, 2017.M. Pipattanasomporn, S. Member, M. Kuzlu, and S. Rahman, “An Algorithm for Intelligent Home Energy Management and Demand Response Analysis,” pp. 1– 8, 2012.R. B. M. P. Author, Residential Demand Response Algorithms: State-of-theArt, Key Issues and Challenges, 1st ed. University, Incheon, South Korea: IEEE Transactions on Smart Grid.T. Yanan, D. Srinivasan, and A. Trivedi, “Multi Objective Optimal Energy Consumption Scheduling in Smart Grids,” 2017.J. M. Lujano-rojas and J. Contreras, “Multi-objective demand response to realtime prices ( RTP ) using a task scheduling methodology s Cort e Tom a,” vol. 138, 2017.A. R. Hevner, S. T. March, J. Park, and S. Ram, “Design Science in Information Systems Research,” vol. 28, no. 1, seul, korea, pp. 75–105, 2013.T. Yang, Y. Zhao, H. Pen, and Z. Wang, “Data center holistic demand response algorithm to smooth microgrid tie-line power fl uctuation,” Appl. Energy, vol. 231, no. December 2017, pp. 277–287, 2018.G. K. Chellamani and P. V. Chandramani, “Demand Response Management System with Discrete Time Window using Supervised Learning Algorithm,” Cogn. Syst. Res., p. 11, 2018.B. Sivaneasan, N. K. Kandasamy, M. L. Lim, and K. P. Goh, “A new demand response algorithm for solar PV intermittency management ☆,” Appl. Energy, vol. 218, no. March, pp. 36–45, 2018.M. Conceptual, J. José, M. Flórez, R. Andres, E. Martinez, and R. Ferreira, “Parte I Antecedentes y Marco Conceptual del Análisis, Evaluación y Recomendaciones para la Implementación de Redes Inteligentes en Colombia,” Bogota, 2016.J. D. Morcillo, C. J. Franco, and F. Angulo, “Simulation of demand growth scenarios in the Colombian electricity market : An integration of system dynamics and dynamic systems,” Appl. Energy, vol. 216, no. February, pp. 504– 520, 2018.L. Alejandro, A. Barragán, E. R. Trujillo, and F. Santamaría, “Respuesta de la demanda en el mercado eléctrico Colombiano : modelado e implementación web Demand response in the Colombian electricity market : modeling and web implementation,” p. 22, 2018.M. Z. Carvajal, “Políticas para la autogestión de electricidad en el sector residencial urbano de Colombia,” Universidad Nacional de Colombia, 2014.Á. R. Restrepo, S. E. Nope, and D. E. Enríquez, “Beneficios Económicos de la Gestión de la Demanda y la Energía Autogenerada en el Contexto de la Regulación Colombiana Economic Benefits of Demand Management and SelfGenerated energy in the Context of Colombian Regulations,” vol. 29, no. 1, pp. 105–116, 2018.J. Sebastián, G. Marín, J. Sebastián, and G. Marín, “Propuesta de implementación de programas de gestión de demanda de energía eléctrica para el sector residencial en Colombia Propuesta de implementación de programas de gestión de demanda de energía eléctrica para el sector residencial en Colombia,” Universidad Nacional de colombia, 2015.H. Cui and K. Zhou, “Industrial power load scheduling considering demand response,” J. Clean. Prod., vol. 204, pp. 447–460, 2018.E. congreso de Colombia, Ley 1715 - 13 de Mayo 2014 Por Medio de la cual se Regula a la Integración de las Energías Renovables no convencionales al Sistema Energético Nacional, no. May. Bogot, 2014.N. Oconnell, P. Pinson, H. Madsen, and M. Omalley, “Benefits and challenges of electrical demand response: A critical review,” Renew. Sustain. Energy Rev., vol. 39, pp. 686–699, 2014.#RespuestaADemandaThis document allows us to have a general overview of DR Demand Response management, and their respective implications by drawing a roadmap regarding its implementation and simulation, taking into account mainly that the Demand to Demand Offer allows us to provide a solution to provide energetic capacity and reliability to the constantly evolving network on which the collective intelligence or Smart Grid is enhanced. This document shows some technologies of demand management that can be dispatched within which are: Direct Load Control, Interruptible Load, Automated, Time Variation, Time Valuation. Some aspects to take into account are the Network restriction services, Security of supplies, Balance and economic markets (Financial Movement) assuming that the DR have a charging dynamic of electric power assessed between the end user and the energy retail producer . DR programs are also essential to improve the reliability of the system and the overall profitability taking into account a Latin American and country regional panorama. The control systems of the demand response management are based on the algorithms of the same which can face challenges such as photovoltaic networks in combination with electrical systems (electrical appliances), giving as a response a possibility to contribute in the task of managing particular energy loads, as a result of this would imply cost savings, load reductions. These algorithms tend to take a single solution factor, such as energy costs, maximum load among others, so that demand management on the client side DSM (Demand side management) becomes more important in order to improve the efficiency and sustainability generating a control on the peak of DR demand management, recalculate the load curve and improve costs, technical aspects for infrastructure use, generation and transmission of energy. The algorithm systems that adapt to the system depending on their needs through a mathematical formulation as a minimization of the problem with a limited number of loads, the HEM algorithms (home energy management) Handles high power loads according to the priority in households below certain levels enabling DR potentials.http://purl.org/coar/resource_type/c_2df8fbb1ORIGINALArticulo DR FM.pdfArticulo DR FM.pdfVer documentoapplication/pdf600294https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17197/1/Articulo%20DR%20FM.pdf8b1676c01a1cce1b448c8ae9aa0efe84MD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-82938https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17197/2/license.txtabceeb1c943c50d3343516f9dbfc110fMD52open accessFormato Autorización de publicación Favian Moncada firmado.pdfFormato Autorización de publicación Favian Moncada firmado.pdfapplication/pdf466863https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17197/3/Formato%20Autorizaci%c3%b3n%20de%20publicaci%c3%b3n%20Favian%20Moncada%20firmado.pdff2aa89264582aa13e7426702011aa8e7MD53open accessTHUMBNAILArticulo DR FM.pdf.jpgArticulo DR FM.pdf.jpgIM Thumbnailimage/jpeg11321https://expeditiorepositorio.utadeo.edu.co/bitstream/20.500.12010/17197/4/Articulo%20DR%20FM.pdf.jpge2d210e4cff3552ba70fbfd7f0a4020fMD54open access20.500.12010/17197oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/171972021-02-10 23:10:36.146open accessRepositorio Institucional - Universidad Jorge Tadeo Lozanoexpeditio@utadeo.edu.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

Revisión general de respuesta a la demanda y sus implicaciones

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