Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12494/33748
Exportar a:
Full metadata record
DC FieldValueLanguage
dc.coverage.temporal8es
dc.creatorColmenares-Quintero, Ramón Fernando-
dc.creatorGóez-Sánchez, German David-
dc.creatorColmenares-Quintero, Juan Carlos-
dc.creatorLatorre-Noguera, Luis Fernando-
dc.creatorKasperczyk, Damian-
dc.date.accessioned2021-04-05T21:44:04Z-
dc.date.available2021-04-05T21:44:04Z-
dc.date.issued2021-03-
dc.identifier.issn23311916es
dc.identifier.urihttps://doi.org/10.1080/23311916.2021.1909791es
dc.identifier.urihttp://hdl.handle.net/20.500.12494/33748-
dc.descriptionLa degradación de la calidad del aire, el sobrecalentamiento y la creciente demanda de energía son cuestiones estrechamente relacionadas que indican el impacto de la humanidad en el cambio climático. Por ello, los gobiernos y otros organismos multilaterales han mostrado su interés por reducir las emisiones contaminantes del aire procedentes de las fuentes de generación de energía con combustibles fósiles. Las opciones más aceptadas están relacionadas con las fuentes de energía limpias, pero, como todos sabemos, estamos lejos de satisfacer la demanda energética mundial con fuentes de generación limpias. Otras opciones se basan en la utilización de combustibles con menor carga de emisiones y en el desarrollo de técnicas para optimizar la generación de energía, reducir los costes mediante la eficiencia energética y reducir al mínimo posible las emisiones de gases de efecto invernadero. Este estudio propone un método para optimizar los factores de sensibilidad como punto de funcionamiento para un generador de energía de turbina de gas, basado en la demanda de energía, la eficiencia eléctrica, la eficiencia del combustible y la minimización de las emisiones de gases de efecto invernadero. Para ello, se ha desarrollado un marco de diseño/evaluación multidisciplinar. Los resultados obtenidos de la simulación de un modelo de consumo energético de un año para una familia media en Colombia produjeron el punto de operación para una turbina de gas basado en la demanda energética, la eficiencia energética y la reducción de las emisiones de CO2 a la atmósfera (es decir, el mejor compromiso). En este sentido, la principal aportación de este trabajo se dirige a la optimización bioinspirada de sistemas de generación de energía que reduzcan las emisiones de CO2 a la atmósfera, especialmente en zonas no interconectadas (fuera de la red), pero que dependen de la generación distribuida basada en plantas de combustibles fósileses
dc.description.abstractThe degradation of air quality, overheating and growing energy demand are closely related issues that indicate the impact of humankind on climate change. Consequently, governments and other multilateral agencies have shown interest in reducing air pollutant emissions from fossil fuel power generation sources. The most accepted options are related to clean energy sources, but, as we all know, we are far from meeting the world’s energy demand with clean generation sources. Other options are based on using fuels with a lower load of emissions and on the development of techniques to optimise power generation, reduce costs through efficient energy and reduce greenhouse gas emissions to the possible minimum. This study proposes a method to optimise the sensitivity factors as the operating point for a gas turbine power generator based on energy demands, electrical efficiency, fuel efficiency and the minimisation of greenhouse gas emissions. In order to address this, a multidisciplinary design/assessment framework was developed. The results obtained from the simulation of a one-year energy consumption model for an average family in Colombia produced the point of operation for a gas turbine based on energy demands, efficient energy and the reduction of CO2 emissions to the atmosphere (i.e. the best trade-off). In this sense, the main contribution of this work is aimed at energy generation systems bio-inspired optimisation that reduce CO2 emissions into the atmosphere, especially in non-interconnected (off-grid) zones, but they rely on fossil fuel plant-based-power distributed generationes
dc.format.extent1 - 9 p.es
dc.publisherUniversidad Cooperativa de Colombia, Facultad de Ingenierías, Ingeniería Mecánica, Medellín y Envigadoes
dc.relation.ispartofCogent Engineeringes
dc.relation.isversionofhttps://doi.org/10.1080/23311916.2021.1909791es
dc.subjectMicro-cogeneraciónes
dc.subjectEmisiones de CO2es
dc.subjectGrupo de partículases
dc.subjectEficiencia eléctricaes
dc.subjectEficiencia del combustiblees
dc.subjectObjetivos de Desarrollo Sosteniblees
dc.subject.othermicro-cogenerationes
dc.subject.otherCO2 emissionses
dc.subject.otherParticle clusteres
dc.subject.otherElectrical efficiencyes
dc.subject.otherFuel efficiencyes
dc.subject.otherSustainable Development Goalses
dc.titleApplication of a simulation tool based on a bio-inspired algorithm for optimisation of distributed power generation systemses
dc.typeArtículos Científicoses
dc.rights.licenseAtribuciónes
dc.publisher.departmentMedellínes
dc.publisher.programIngeniería mecanicaes
dc.creator.mailramon.colmenaresq@campusucc.edu.coes
dc.creator.mailgermangoez@itm.edu.coes
dc.creator.mailjcarloscolmenares@ichf.edu.ples
dc.creator.mailluis.latorren@campusucc.edu.coes
dc.creator.mailbiuro@ekoinwentyka.ples
dc.identifier.bibliographicCitationColmenares-Quintero R., Góez-Sáchez D., Colmenares-Quintero J., Latorre-Noguera L., & Kasperczyk D. | (2021) Application of a simulation tool based on a bio-inspired algorithm for optimisation of distributed power generation systems, Cogent Engineering, 8:1, DOI: 10.1080/2331916.2021.1909791es
dc.rights.accessRightsopenAccesses
dc.source.bibliographicCitationKanchev H, Lu D, Colas F, Lazarov V, Francois B. Energy management and operational planning of a microgrid with a PV based active generator for smart grid applications. IEEE Trans Ind Electron. 2011;58(10):4583-4592. doi:10.1109/TIE.2011.2119451es
dc.source.bibliographicCitationKong XQ, Wang RZ, Huang XH. Energy optimization model for a CCHP system with available gas turbines. Appl Therm Eng. 2005;25(2-3):377-391. doi:10.1016/j.applthermaleng.2004.06.014es
dc.source.bibliographicCitationGu W, Wu Z, Bo R, et al. Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review. Int J Electr Power Energy Syst. 2014;54:26-37. doi:10.1016/j.ijepes.2013.06.028es
dc.source.bibliographicCitationWang JJ, Jing YY, Zhang CF. Optimization of capacity and operation for CCHP system by genetic algorithm. Appl Energy. 2010;87(4):1325-1335. doi:10.1016/j.apenergy.2009.08.005es
dc.source.bibliographicCitationMago PJ, Chamra LM. Analysis and optimization of CCHP systems based on energy, economical, and environmental considerations. Energy Build. 2009;41(10):1099-1106. doi:10.1016/j.enbuild.2009.05.014es
dc.source.bibliographicCitationTolba M, Rezk H, Tulsky V, Diab A, Abdelaziz A, Vanin A. Impact of optimum allocation of renewable distributed generations on distribution networks based on different optimization algorithms. Energies. 2018;11(2):245. doi:10.3390/en11010245es
dc.source.bibliographicCitationYang G, Zhai X. Optimization and performance analysis of solar hybrid CCHP systems under different operation strategies. Appl Therm Eng. 2018;133(September 2017):327-340. doi:10.1016/j.applthermaleng.2018.01.046es
dc.source.bibliographicCitationRomán S, Libra J, Berge N, et al. Hydrothermal carbonization: Modeling, final properties design and applications: A review. Energies. 2018;11(1):216. doi:10.3390/en11010216es
dc.source.bibliographicCitationZeng L, Zhao L, Wang Q, et al. Modeling interprovincial cooperative energy saving in China: An electricity utilization perspective. Energies. 2018;11(1):1-25. doi:10.3390/en11010241es
dc.source.bibliographicCitationKamdem BG, Shittu E. Optimal commitment strategies for distributed generation systems under regulation and multiple uncertainties. Renew Sustain Energy Rev. 2015;80(2014):1597-1612. doi:10.1016/j.rser.2016.12.062es
dc.source.bibliographicCitationRafiei S, Bakhshai A. A review on energy efficiency optimization in Smart Grid. IECON 2012 - 38th Annu Conf IEEE Ind Electron Soc. 2012:5916-5919. doi:10.1109/IECON.2012.6389115es
dc.source.bibliographicCitationColmenares-Quintero RF, Góez-Sánchez GD, Colmenares-Quintero JC. Route planning in real time for short-range aircraft with a constant-volume-combustor-geared turbofan to minimize operating costs by particle swarm optimization. Cogent Eng. 2018;5(1). doi:10.1080/23311916.2018.1429984es
dc.source.bibliographicCitationColmenares-Quintero RF, Góez-Sánchez GD, Colmenares-Quintero JC. Trajectory optimization of an innovative-turbofan-powered aircraft based on particle swarm approach for low environmental impact. Cogent Eng. 2019;6(1). doi:10.1080/23311916.2019.1575637es
dc.source.bibliographicCitationLeonard BJ, Engelbrecht AP. On the optimality of particle swarm parameters in dynamic environments. IEEE congress on evolutionary computation 2013:1564-1569es
dc.source.bibliographicCitationClerc M. Particle Swarm Optimization. Hermes Science/Lavoisier 2005; 2005.es
dc.source.bibliographicCitationColmenares-Quintero RF, Latorre-Noguera LF, Dibdiakova J, Colmenares-Quintero JC. Techno-environmental assessment of a micro-cogeneration system based on natural gas for residential application. CT y F - Ciencia, Tecnologia y Futuro 2018;8(1). doi: 10.29047/01225383.97es
dc.source.bibliographicCitationClerc M. Standard Particle Swarm Optimisation. 2012:1-15. https://pdfs.semanticscholar.org/c26f/d373a085b72b9a83724ef44b0d3780f0b93e.pdfes
dc.description.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000192503es
dc.description.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001358296es
dc.description.cvlachttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001595655es
dc.description.orcidhttps://orcid.org/0000-0003-1166-1982es
dc.description.orcidhttps://orcid.org/0000-0001-7658-0994es
dc.description.orcidhttps://orcid.org/0000-0003-3701-6340es
dc.description.gruplachttps://scienti.minciencias.gov.co/gruplac/jsp/visualiza/visualizagr.jsp?nro=00000000005961es
dc.description.googlescholarhttps://scholar.google.com/citations?user=9HLAZYUAAAAJ&hl=eses
dc.description.googlescholarhttps://scholar.google.com/citations?user=EbY_kxoAAAAJ&hl=eses
dc.description.googlescholarhttps://scholar.google.pl/citations?user=9spgFMUAAAAJ&hl=ples
Appears in Collections:Ingeniería Mecánica



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.