Programación de la operación horaria de una microred minimizando el costo de operación usando el algoritmo heurístico DEEPSO

dc.contributor.advisorRivera Rodriguez, Sergio Raulspa
dc.contributor.authorBarón Moreno, Carlos Eduardospa
dc.contributor.researchgroupGrupo de Investigación EMC-UNspa
dc.date.accessioned2020-06-11T22:50:37Zspa
dc.date.available2020-06-11T22:50:37Zspa
dc.date.issued2019-12-13spa
dc.description.abstractPlanning the operation scheduling of the microgrid by using optimization heuristic algorithms allows the microgrids to be more efficient. This is possible since a microgrid can work as a power system that has to operate jointly having different aspects namely renewable and traditional power generation, energy storage and controllable loads. The test bed used during the development of this study consists of two aggregators that manage electric vehicles, power generation, photovoltaic, battery bank and two wind turbine generators. This research has three objectives: the first is to formulate an equation that describes the operation cost of the microgrid; the second aims to find such an operating point that the operation cost of the microgrid can be obtained through the DEEPSO (Differential Evolutionary Particle Swarm Optimization), metaheuristic algorithm, in which this value is minimum. Finally, the third objective is to analyze how the most adequate programming for the reduction of costs is affected by the useful life of the electric energy storage. After the development of this research, it was found that in a 24-hour horizon time, the use of energy storage allows the existence of savings or even profits only by using the microgrid, actually. Likewise, it was observed that as the time passes and the storage system gets old, the mentioned system resembles more to the current systems that have a no manageable operating regime (it means that the wind and solar power feed cannot be controlled). Ideally, these systems dispatch all the available renewable energy during that specific time, but without having planned a smart dispatch, for instance, by using the energy surplus stored in the batteries in other periods of time.spa
dc.description.abstractPlanear la programación de la operación de una microred mediante algoritmos de optimización metaheurísticos permite que las microredes sean más eficientes. Esto se debe a que una microred se puede manejar como un sistema de potencia que tiene que operar conjuntamente y que tiene varios aspectos como generación renovable, generación convencional, almacenamiento de energía y control de cargas. El test bed o la red de prueba utilizada en el desarrollo del presente trabajo consta de generación solar, dos generadores eólicos, un sistema de almacenamiento en baterías, dos sisteamas que gestionan vehículos eléctricos. Este trabajo tiene tres objetivos. El primero es formular una ecuación que describa el costo de operar una microred. El segundo es encontrar el punto de operación tal que el costo asociado a la operación de la microred se obtenga a través del algoritmo metaheurístico con nombre DEEPSO que por sus siglas en ingles significa Differential Evolutionary Particle Swarm Optimization, donde este valor es el mínimo. Finalmente, el tercer objetivo es analizar cómo la programación más adecuada para la reducción de los costos es afectada por la vida útil del almacenamiento de energía eléctrica. Después del desarrollo del trabajo, se analizó y encontró que en un horizonte de tiempo de 24 horas el uso de almacenamiento de energía permite que existan ahorros o incluso ganancias solamente en el uso de la microred. Así mismo, se observó que al transcurrir el tiempo y el sistema de almacenamiento envejece, este se acerca cada vez más a los sistemas actuales, que tienen un régimen de operación no gestionable (es decir que la inyección solar y eólica no se puede controlar). Estos sistemas tienen como ideal despachar toda la energía renovable disponible en ese mismo instante, pero sin planear el despacho de forma inteligente, por ejemplo, usando excedentes de energía almacenados en baterías en otros momentos de tiempospa
dc.description.degreelevelMaestríaspa
dc.format.extent113spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/77650
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Eléctricaspa
dc.relation.references[1] “The Paris Agreement | UNFCCC.” .spa
dc.relation.references[2] M. L. H. y E. M. E. 2016 García Arbeláez, C., G. Vallejo, El acuerdo de parís así actuará Colombia frente al cambio climático. .spa
dc.relation.references[3] “Ministerio de Minas y Energía.” .spa
dc.relation.references[4] “Unidad de Planeación Minero Energética UPME.” .spa
dc.relation.references[5] “Comisión de Regulación de Energía y Gas - CREG.” .spa
dc.relation.references[6] “Superintendencia de Servicios Públicos Domiciliarios.” .spa
dc.relation.references[7] “Superintendencia de Industria y Comercio.” .spa
dc.relation.references[8] “Sistema de Información Eléctrico Colombiano SIMEC.” .spa
dc.relation.references[9] R. C. Siabato Benavides, “Identificación de proyectos con potencial de generación de energía eólica como complemento a otras fuentes de generación eléctrica en el departamento de Boyacá,” p. 156, 2018.spa
dc.relation.references[10] S. Surender Reddy, J. Y. Park, and C. M. Jung, “Optimal operation of microgrid using hybrid differential evolution and harmony search algorithm,” Front. Energy, vol. 10, no. 3, pp. 355–362, 2016.spa
dc.relation.references[11] J. N. Bharothu, M. Sridhar, and R. S. Rao, “Modified adaptive differential evolution based optimal operation and security of AC-DC microgrid systems,” Int. J. Electr. Power Energy Syst., vol. 103, pp. 185–202, Dec. 2018.spa
dc.relation.references[12] T. K. Kristoffersen, K. Capion, and P. Meibom, “Optimal charging of electric drive vehicles in a market environment,” Appl. Energy, 2011.spa
dc.relation.references[13] H. L. Li, X. M. Bai, and W. Tan, “Impacts of plug-in hybrid electric vehicles charging on distribution grid and smart charging,” in 2012 IEEE International Conference on Power System Technology, POWERCON 2012, 2012.spa
dc.relation.references[14] J. Zhao, F. Wen, Z. Yang Dong, S. Member, Y. Xue, and K. Po Wong, “Optimal Dispatch of Electric Vehicles and Wind Power Using Enhanced Particle Swarm Optimization,” IEEE Trans. Ind. INFORMATICS, vol. 8, no. 4, 2012.spa
dc.relation.references[15] H. Kamankesh, V. G. Agelidis, and A. Kavousi-Fard, “Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand,” Energy, 2016.spa
dc.relation.references[16] Z. Ma, D. S. Callaway, and I. A. Hiskens, “Decentralized Charging Control of Large Populations of Plug-in Electric Vehicles,” IEEE Trans. Control Syst. Technol., vol. 21, no. 1, 2013.spa
dc.relation.references[17] A. Sheikhi, S. Bahrami, A. M. Ranjbar, and H. Oraee, “Strategic charging method for plugged in hybrid electric vehicles in smart grids; A game theoretic approach,” Int. J. Electr. Power Energy Syst., 2013.spa
dc.relation.references[18] R. H. Lasseter, “MicroGrids,” in 2002 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.02CH37309), 2002, vol. 1, pp. 305–308 vol.1.spa
dc.relation.references[19] N. Salvaterra, “Las baterias gigantescas alimentan los planes para las energías eólicas y solar,” p. B 8, 2019.spa
dc.relation.references[20] B. Zhao, X. Zhang, J. Chen, C. Wang, and L. Guo, “Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system,” IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 934–943, 2013.spa
dc.relation.references[21] C. Baron and S. Rivera, “Mono-objective minimization of operation cost for a microgrid with renewable power generation, energy storage and electric vehicles,” Rev. Int. Métodos Numéricos para Cálculo y Diseño en Ing., 2019.spa
dc.relation.references[22] M. Ross, R. Hidalgo, C. Abbey, and G. Joós, “Energy storage system scheduling for an isolated microgrid,” IET Renew. Power Gener., 2011.spa
dc.relation.references[23] H. Morais, P. Kádár, P. Faria, Z. A. Vale, and H. M. Khodr, “Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming,” Renew. Energy, 2010.spa
dc.relation.references[24] Y. A. Katsigiannis, P. S. Georgilakis, and E. S. Karapidakis, “Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables,” IET Renew. Power Gener., 2010.spa
dc.relation.references[25] R. Dufo-López and J. L. Bernal-Agustín, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems,” Renew. Energy, 2008.spa
dc.relation.references[26] S. X. Chen and H. B. Gooi, “Jump and shift method for multi-objective optimization,” IEEE Trans. Ind. Electron., 2011.spa
dc.relation.references[27] C. Chen, S. Duan, T. Cai, B. Liu, and G. Hu, “Smart energy management system for optimal microgrid economic operation,” IET Renew. Power Gener., 2011.spa
dc.relation.references[28] J. Li, F. Liu, Z. Wang, S. H. Low, and S. Mei, “Optimal Power Flow in Stand-alone DC Microgrids,” IEEE Trans. Power Syst., vol. 33, no. 5, pp. 5496–5506, 2017.spa
dc.relation.references[29] IEEE PES Working Group on Modern Heuristic Optimization, “Competition on ‘Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems,’” 2014.spa
dc.relation.references[30] J. Arévalo, F. Santos, and S. Rivera, “Application of Analytical Uncertainty Costs of Solar, Wind and Electric Vehicles in Optimal Power Dispatch,” Ingeniería, vol. 22, no. 3, pp. 324–346, 2017.spa
dc.relation.references[31] S. Rivera, C. Arevalo, and F. Santos, “Uncertainty Cost Functions for Solar Photovoltaic Generation, Wind Energy Generation, and Plug-In Electric Vehicles: Mathematical Expected Value and Verification by Monte Carlo Simulation,” Int. J. Power Energy Convers., vol. 10, no. 2, pp. 171–207, 2018.spa
dc.relation.references[32] D. U. Sauer and H. Wenzl, “Comparison of different approaches for lifetime prediction of electrochemical systems-Using lead-acid batteries as example,” J. Power Sources, 2008.spa
dc.relation.references[33] C. Liu, X. Wang, X. Wu, and J. Guo, “Economic scheduling model of microgrid considering the lifetime of batteries,” IET Gener. Transm. Distrib., vol. 11, no. 3, pp. 759–767, 2017.spa
dc.relation.references[34] F. Katiraei, R. Iravani, N. Hatziargyriou, and A. Dimeas, “Microgrids Management,” IEEE Trans. Smart Grid, no. june, pp. 54–65, 2008.spa
dc.relation.references[35] F. Garcia-torres and C. Bordons, “Optimal Economical Schedule of Hydrogen-Based Microgrids With Hybrid Storage Using Model Predictive Control,” IEEE Trans. Ind. Electron., vol. 62, no. 8, pp. 5195–5207, 2015.spa
dc.relation.references[36] A. Arabali, M. Ghofrani, M. S. Fadali, and Y. Baghzouz, “Genetic-Algorithm-Based Optimization Approach for Energy Management,” IEEE Trans. Power Deliv., vol. 28, no. 1, pp. 162–170, 2013.spa
dc.relation.references[37] S. Bahramirad, W. Reder, and A. Khodaei, “Reliability-Constrained Optimal Sizing of Energy Storage System in a Microgrid,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 2056–2062, 2012.spa
dc.relation.references[38] G. Carpinelli, F. Mottola, D. Proto, and A. Russo, “A Multi-Objective Approach for Microgrid Scheduling,” IEEE Trans. Smart Grid, vol. 8, no. 5, pp. 2109–2118, 2017.spa
dc.relation.references[39] S. Teleke et al., “Control Strategies for Battery Energy Storage for Wind Farm Dispatching,” IEEE Trans. Energy Convers., vol. 24, no. 3, pp. 725–732, 2009.spa
dc.relation.references[40] P. Mercier, R. Cherkaoui, S. Member, and A. Oudalov, “Optimizing a Battery Energy Storage System for Frequency Control Application in an Isolated Power System,” ieee tra, vol. 24, no. 3, pp. 1469–1477, 2009.spa
dc.relation.references[41] R. A. Gallego Rendon, A. H. Escobar Zuluaga, and E. M. Toro Ocampo, Tecnicas metaheuristicas de optimizacion., Segunda. Pereira: Universidad Tecnológica de Pereira, 2008.spa
dc.relation.references[42] J. L. Acosta, A. E. Alarcon, and S. Rivera, “Reconfiguración de sistemas de distribución para minimizar pérdidas utilizando optimización heurística: Métodos BPSO y DEEPSO,” Entre Cienc. e Ing., vol. 11, no. 22, pp. 110–117, Oct. 2017.spa
dc.relation.references[43] L. Dkul et al., “Unit Commitment With Non-Smooth Generation Cost Function Using Binary Particle Swarm Optimization,” Int. Semin. Intell. technoology its Appl., pp. 5–10, 2016.spa
dc.relation.references[44] C. A. Hernandez-Aramburo, T. C. Green, and N. Mugniot, “Fuel consumption minimization of a microgrid,” IEEE Trans. Ind. Appl., 2005.spa
dc.relation.references[45] M. Alramlawi, E. Mohagheghi, and P. Li, “Predictive active-reactive optimal power dispatch in PV-battery-diesel microgrid considering reactive power and battery lifetime costs,” Sol. Energy, vol. 193, no. September, pp. 529–544, 2019.spa
dc.relation.references[46] D. Soto, “Modeling and measurement of specific fuel consumption in diesel microgrids in Papua, Indonesia,” Energy Sustain. Dev., vol. 45, pp. 180–185, 2018.spa
dc.relation.references[47] M. I. Ennes and A. L. Diniz, “for Economic Dispatch Problems,” Computing, pp. 1–6, 2012.spa
dc.relation.references[48] N. Martinez et al., “Computer model for a wind–diesel hybrid system with compressed air energy storage,” Energies, vol. 12, no. 18, pp. 1–18, 2019.spa
dc.relation.references[49] Q. Zhang, Z. Ren, R. Ma, M. Tang, and Z. He, “Research on Double-Layer Optimized Configuration of Multi-Energy Storage in Regional Integrated Energy System with Connected Distributed Wind Power,” Energies, vol. 12, no. 3964, pp. 1–16, 2019.spa
dc.relation.references[50] “China Renewable Energy Outlook 2018.”spa
dc.relation.references[51] “Ofgem - Making a positive difference for energy consumers.” .spa
dc.relation.references[52] “Microgeneration Certification Scheme (MCS): Small installations | Ofgem.” .spa
dc.relation.references[53] “MCS - Tthe Microgeneration certification scheme.” .spa
dc.relation.references[54] Z. Xu, Z. Hu, Y. Song, W. Zhao, and Y. Zhang, “Coordination of PEVs charging across multiple aggregators,” Appl. Energy, 2014.spa
dc.relation.references[55] A. Hussain, V. Bui, J. Baek, and H. Kim, “Stationary Energy Storage System for Fast EV Charging Stations : Simultaneous Sizing of Battery,” Energies, vol. 12, no. 4516, 2019.spa
dc.relation.references[56] A. Serpi and M. Porru, “Modelling and Design of Real-Time Energy Management Systems for Fuel Cell / Battery Electric Vehicles,” Energies, vol. 12, no. 4260, 2019.spa
dc.relation.references[57] S. Z. Frida Berglund and M. K. and K. Uhlen, “Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff,” Energies, vol. 12, no. 4450, 2019.spa
dc.relation.references[58] T. Sikorsk et al., “A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept : Economic Aspects,” Energies, vol. 12, no. 4447, 2019.spa
dc.relation.references[59] A. Castellazzi, E. Gurpinar, Z. Wang, A. S. Hussein, and P. G. Fernandez, “Impact of Wide-Bandgap Technology on Renewable Energy and Smart-Grid Power Conversion Applications Including Storage,” Energies, vol. 12, no. 4462, 2019.spa
dc.relation.references[60] C. Jankowiak, A. Zacharopoulos, C. Brandoni, P. Keatley, P. MacArtain, and N. Hewitt, “The role of domestic integrated battery energy storage systems for electricity network performance enhancement,” Energies, vol. 12, no. 3954, pp. 1–27, 2019.spa
dc.relation.references[61] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective Intelligent Energy Management for a Microgrid _ Aymen Chaouachi - Academia,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1688–1699, 2013.spa
dc.relation.references[62] U.S. Energy Information Administration (EIA), “Annual Energy Outlook 2013 with projections to 2040.” Washington, DC, p. 244, 2013.spa
dc.relation.references[63] Planeamiento Minero-Energético (UPME), “Integración de las energías renovables no convencionales en Colombia,” Minist. Minas y Energía, pp. 23–33, 2015.spa
dc.relation.references[64] “GWEC | GLOBAL WIND REPORT 2018,” 2019.spa
dc.relation.references[65] B. K. Sahu, “Wind energy developments and policies in China: A short review,” Renew. Sustain. Energy Rev., vol. 81, pp. 1393–1405, 2018.spa
dc.relation.references[66] J. Garcia-guarin et al., “Smart Microgrids Operation Considering a Variable Neighborhood Search : The Differential Evolutionary Particle Swarm Optimization Algorithm,” Energies, vol. 12, no. 3149, pp. 1–13, 2019.spa
dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.ddc530 - Física::537 - Electricidad y electrónicaspa
dc.subject.proposalMetaheuristic Algorithmeng
dc.subject.proposalAlgoritmos Metaheurísticosspa
dc.subject.proposalAlmacenamiento de Energíaspa
dc.subject.proposalUseful Life.eng
dc.subject.proposalElectric Vehicleseng
dc.subject.proposalCosto de Incertidumbrespa
dc.subject.proposalDEEPSOspa
dc.subject.proposalMicrogrideng
dc.subject.proposalDespacho Económicospa
dc.subject.proposalRenewable Energieseng
dc.subject.proposalEconomic Dispatcheng
dc.subject.proposalEnergías Renovablesspa
dc.subject.proposalDEEPSO,eng
dc.subject.proposalMicroredspa
dc.subject.proposalUncertainty Costeng
dc.subject.proposalVehículos Eléctricosspa
dc.subject.proposalVida Útilspa
dc.subject.proposalEnergy Storageeng
dc.titleProgramación de la operación horaria de una microred minimizando el costo de operación usando el algoritmo heurístico DEEPSOspa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
Programación de la operación horaria de una microred minimizando el costo de operación usando el algoritmo heurístico DEEPSO.pdf
Tamaño:
2.21 MB
Formato:
Adobe Portable Document Format

Bloque de licencias

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