Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos

ilustraciones, graficas

Autores:: Nitola Chaparro, Lizeth Alejandra

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
dc.title.translated.eng.fl_str_mv	Programming the operation of a test microgrid minimizing congestion and operating cost through heuristic algorithms
title	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
spellingShingle	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos 110 - Metafísica::118 - Fuerza y energía Congestión Costo de operación Optimización multiobjetivo MOPSO Pareto óptimo Microred Energías renovables Congestion Cost of operation Multi-object optimization Optimal pareto Microgrid Renewable energies
title_short	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
title_full	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
title_fullStr	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
title_full_unstemmed	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
title_sort	Programación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticos
dc.creator.fl_str_mv	Nitola Chaparro, Lizeth Alejandra
dc.contributor.advisor.none.fl_str_mv	Rivera Rodríguez, Sergio Raúl
dc.contributor.author.none.fl_str_mv	Nitola Chaparro, Lizeth Alejandra
dc.contributor.researchgroup.spa.fl_str_mv	Grupo de Investigación EMC-UN
dc.subject.ddc.spa.fl_str_mv	110 - Metafísica::118 - Fuerza y energía
topic	110 - Metafísica::118 - Fuerza y energía Congestión Costo de operación Optimización multiobjetivo MOPSO Pareto óptimo Microred Energías renovables Congestion Cost of operation Multi-object optimization Optimal pareto Microgrid Renewable energies
dc.subject.proposal.spa.fl_str_mv	Congestión Costo de operación Optimización multiobjetivo MOPSO Pareto óptimo Microred Energías renovables
dc.subject.proposal.eng.fl_str_mv	Congestion Cost of operation Multi-object optimization Optimal pareto Microgrid Renewable energies
description	ilustraciones, graficas
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-06-08T17:13:59Z
dc.date.available.none.fl_str_mv	2021-06-08T17:13:59Z
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
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
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/79615 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	[1] S. Huang, Q. Wu, Z. Liu, and A. H. Nielsen, “Review of congestion management methods for distribution networks with high penetration of distributed energy resources,” in IEEE PES Innovative Smart Grid Technologies Conference Europe, Jan. 2015, vol. 2015-Janua, no. January, doi: 10.1109/ISGTEurope.2014.7028811. [2] A. Abdolahi, J. Salehi, F. Samadi Gazijahani, and A. Safari, “Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system,” J. Renew. Sustain. Energy, vol. 10, no. 4, Jul. 2018, doi: 10.1063/1.5035081. [3] K. Spiliotis, S. Claeys, A. Ramos Gutierrez, and J. Driesen, “Utilizing Local Energy Storage for Congestion Management and Investment Deferral in Distribution Networks.” [4] J. M. L. Lezama, “PROPUESTAS ALTERNATIVAS PARA MANEJO DE CONGESTIÓN EN EL MERCADO DE ENERGÍA ELÉCTRICA COLOMBIANO,” 2006. [5] S. Gope, S. Dawn, R. Mitra, A. K. Goswami, and P. K. Tiwari, “Transmission congestion relief with integration of photovoltaic power using lion optimization algorithm,” in Advances in Intelligent Systems and Computing, 2019, vol. 816, pp. 327–338, doi: 10.1007/978-981-13-1592-3_25. [6] E. Reihani, P. Siano, and M. Genova, “A new method for peer-to-peer energy exchange in distribution grids,” Energies, vol. 13, no. 4, 2020, doi: 10.3390/en13040799. [7] K. Vijayakumar, “Multiobjective optimization methods for congestion management in deregulated power systems,” J. Electr. Comput. Eng., 2012, doi: 10.1155/2012/962402. [8] J. M. L. LEZAMA, “UNIVERSIDAD NACIONAL DE COLOMBIA SEDE MANIZALES DEPARTAMENTO DE INGENIERÍA ELÉCTRICA, ELECTRÓNICA Y COMPUTACIÓN,” Manizalez, 2006. [9] Institute of Electrical and Electronics Engineers, IEEE Dielectrics and Electrical Insulation Society. Kolkata Chapter, IEEE Power & Energy Society. Kolkata Chapter, Institute of Electrical and Electronics Engineers. Kolkata section, and North Eastern Regional Institute of Science and Technology, 2012 1st International Conference on Power and Energy in NERIST (lCPEN) : proceedings : [held at new seminar hall of North Eastern Regional Institute of Science and Technology] : Nirjuli, Arunachal Pradesh, India : 28-29 December 2012. . [10] J. Hazra and A. K. Sinha, “Congestion management using multiobjective particle swarm optimization,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1726–1734, Nov. 2007, doi: 10.1109/TPWRS.2007.907532. [11] I. Kalogeropoulos and H. Sarimveis, “Predictive control algorithms for congestion management in electric power distribution grids,” Appl. Math. Model., vol. 77, pp. 635–651, Jan. 2020, doi: 10.1016/j.apm.2019.07.034. [12] W. Liu, Q. Wu, F. Wen, and J. Ostergaard, “Day-ahead congestion management in distribution systems through household demand response and distribution congestion prices,” IEEE Trans. Smart Grid, vol. 5, no. 6, pp. 2739–2747, 2014, doi: 10.1109/TSG.2014.2336093. [13] M. Kashyap and S. Kansal, “Hybrid approach for congestion management using optimal placement of distributed generator,” Int. J. Ambient Energy, vol. 39, no. 2, pp. 132–142, 2018, doi: 10.1080/01430750.2016.1269676. [14] J. Li and F. Li, “A Congestion Index considering the Characteristics of Generators & Networks.” [15] P. Biswas and B. B. Pal, “A fuzzy goal programming method to solve congestion management problem using genetic algorithm,” Decis. Mak. Appl. Manag. Eng., vol. 2, no. 2, Oct. 2019, doi: 10.31181/dmame1902040b. [16] S. Patil and N. Asati, “CONGESTION MANAGEMENT USING GENETIC ALGORITHM,” 2019. [Online]. Available: www.irjeas.org,. [17] H. Khani, M. R. D. Zadeh, and A. H. Hajimiragha, “Transmission Congestion Relief Using Privately Owned Large-Scale Energy Storage Systems in a Competitive Electricity Market,” IEEE Trans. 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Bliek, “Congestion management in smart distribution network,” in Proceedings of the Universities Power Engineering Conference, Oct. 2014, doi: 10.1109/UPEC.2014.6934751. [22] R. T. Elliott et al., “Sharing Energy Storage Between Transmission and Distribution,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 152–162, Jan. 2019, doi: 10.1109/TPWRS.2018.2866420. [23] G. Koeppel, M. Geidl, G. Andersson, G. Koeppel, M. Geidl, and G. Andersson, “‘Value of Storage Devices in Congestion Constrained Distribution Networks’ Value of Storage Devices in Congestion Constrained Distribution Networks,” 2004. [24] B. K. Sarkar, A. De, and A. Chakrabarti, “Impact of Distributed Generation for congestion relief in power networks,” in 2012 1st International Conference on Power and Energy in NERIST, ICPEN 2012 - Proceedings, 2012, doi: 10.1109/ICPEN.2012.6492324. [25] K. Zhang, S. Troitzsch, S. Hanif, and T. Hamacher, “Coordinated Market Design for Peer-to-Peer Energy Trade and Ancillary Services in Distribution Grids Control-oriented Building Model (CoBMo) View project Platform for Interconnected Micro-grid Operation (PRIMO) View project Coordinated Market Design for Peer-to-Peer Energy Trade and Ancillary Services in Distribution Grids.” [Online]. Available: https://www.researchgate.net/publication/338501038. [26] J. Hu, G. Yang, C. Ziras, and K. Kok, “Aggregator Operation in the Balancing Market Through Network-Constrained Transactive Energy,” IEEE Trans. Power Syst., vol. 34, no. 5, pp. 4071–4080, Sep. 2019, doi: 10.1109/TPWRS.2018.2874255. [27] J. Zhao, Y. Wang, G. Song, P. Li, C. Wang, and J. Wu, “Congestion Management Method of Low-Voltage Active Distribution Networks Based on Distribution Locational Marginal Price,” IEEE Access, vol. 7, pp. 32240–32255, 2019, doi: 10.1109/ACCESS.2019.2903210. [28] Carlos Eduardo Barón Moreno, “TESIS Programación de la operación horaria de una microred minimizando el costo de operación usando el algoritmo heurístico DEEPSO (1),” Nacional de Colombia, 2019. [29] 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, doi: 10.14483/23448393.11673. [30] R. S. Wibowo, F. F. Utama, D. F. U. Putra, and N. K. Aryani, “Unit commitment with non-smooth generation cost function using binary particle swarm optimization,” in Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy, Jan. 2017, pp. 571–576, doi: 10.1109/ISITIA.2016.7828723. [31] 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. 20, Oct. 2019, doi: 10.3390/en12203964. [32] 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., vol. 35, no. 3, Jul. 2019, doi: 10.23967/j.rimni.2019.06.005. [33] J. C. 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,” Int. J. Power Energy Convers., vol. 10, no. 2, pp. 171–207, 2019, doi: 10.1504/IJPEC.2019.098621. [34] Z. Xu, Z. Hu, Y. Song, W. Zhao, and Y. Zhang, “Coordination of PEVs charging across multiple aggregators,” Appl. Energy, vol. 136, pp. 582–589, Dec. 2014, doi: 10.1016/j.apenergy.2014.08.116. [35] A. Serpi and M. Porru, “Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles,” Energies, vol. 12, no. 22, p. 4260, Nov. 2019, doi: 10.3390/en12224260. [36] A. Hussain, V. H. Bui, J. W. Baek, and H. M. Kim, “Stationary energy storage system for fast EV charging stations: Simultaneous sizing of battery and converter,” Energies, vol. 12, no. 23, 2019, doi: 10.3390/en12234516. [37] R. Dufo-López and J. L. Bernal-Agustín, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems,” Renew. Energy, vol. 33, no. 12, pp. 2559–2572, Dec. 2008, doi: 10.1016/j.renene.2008.02.027. [38] F. Berglund, S. Zaferanlouei, M. Korpås, and K. Uhlen, “Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study,” Energies, vol. 12, no. 23, p. 4450, Nov. 2019, doi: 10.3390/en12234450. [39] C. Jankowiak, A. 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[49] metode penelitian Nursalam, 2016 and A. . Fallis, “ESTUDIO COMPARATIVO DE TÉCNICAS DE OPTIMIZACIÓN MULTIOBJETIVO PARA DETERMINAR LA MÁS ADECUADA EN PROBLEMAS MULTI-CRITERIO,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2013. [50] C. A. Coello Coello and M. S. Lechuga, “MOPSO: A proposal for multiple objective particle swarm optimization,” Proc. 2002 Congr. Evol. Comput. CEC 2002, vol. 2, pp. 1051–1056, 2002, doi: 10.1109/CEC.2002.1004388. [51] J. Yang, J. Zhou, L. Liu, and Y. Li, “A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO),” Comput. Math. with Appl., vol. 57, no. 11–12, pp. 1995–2000, Jun. 2009, doi: 10.1016/j.camwa.2008.10.009. [52] H. M. Khodr, F. G. Olsina, P. M. D. O. De Jesus, and J. M. Yusta, “Maximum savings approach for location and sizing of capacitors in distribution systems,” Electr. Power Syst. Res., vol. 78, no. 7, pp. 1192–1203, 2008, doi: 10.1016/j.epsr.2007.10.002. [53] S. Bhullar and S. 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dc.format.extent.spa.fl_str_mv	1 recurso en linea (121 paginas)
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería Eléctrica
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
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rivera Rodríguez, Sergio Raúlebc09c48c256e8bad61b48321e3a32c5Nitola Chaparro, Lizeth Alejandra99db85d2171e52a7965724fc11e9a8d5Grupo de Investigación EMC-UN2021-06-08T17:13:59Z2021-06-08T17:13:59Z2021https://repositorio.unal.edu.co/handle/unal/79615Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficasEl presente estudio revisa el impacto que se puede presentar por la inmersión de fuentes de generación a la red de distribución, con un enfoque técnico, operativo y comercial, dadas por las transacciones de energía entre cliente y operador. De esta manera se requiere de un arreglo matemático que permita identificar el balance entre la congestión y el costo de operación de una microred. en el momento de realizar la programación de la operación del sistema en un horizonte de tiempo de 24 horas. Así la investigación se encamina a la solución mediante algoritmos heurísticos, que permiten abordar las restricciones no-covexas del planteamiento del problema propuesto. El algoritmo de optimización propuesto para el análisis esta dado por el método de optimización de enjambre de partículas multiobjetivo (MOPSO), proporcionando un conjunto de soluciones que son conocidas como Pareto óptimo. Este algoritmo se plantea en un sistema IEEE de 141 buses/nodos, el cual consta de una red radial de distribución que considera 141 buses usado como uno de los casos base o caso de estudio en Matpower. Para ello, este sistema fue modificado y enél se incluyeron una serie de inyecciones de generación renovable, sistemas que coordinan vehículos eléctricos, (agregadores), almacenamiento en baterías y el nodo slack se mantuvo igual que el caso base y se asumió que este tiene (generación tradicional). Al final se puede evidenciar que el algoritmo puede aportar soluciones para la planificación de la operación de la red, probar la robustez del sistema y verificar algunas contingencias de forma comparativa. Siempre optimizando el balance entre la congestión y el costo.This study reviews the impact that can be presented by the immersion of generation sources into the distribution network, with a technical, operational and commercial approach, given by the energy transactions between customer and operator. This requires a mathematical arrangement to identify the balance between congestion and the operating cost of a microgrid when it is required the operation scheduling of the system in a day ahead horizon time. Thus, the research is directed to the solution, using heuristic algorithms, since they allow the non-convex constraints of the mathematical proposed problem. The optimization algorithm proposed for the analysis is given by the Multi-Object Particle Swarm Optimization (MOPSO) method, it provides a set of solutions that are known as Optimal Pareto. This algorithm is presented in an IEEE 141-bus system, which consists of a radial distribution network that considers 141 buses used by Matpower, this system was modified and included a series of renewable generation injections, systems that coordinate electric vehicles, battery storage and the slack node was maintained and assumed to have (traditional generation). In the end it can be shown that the algorithm can provide solutions for network operation planning, test system robustness and verify some contingencies comparatively. Always optimizing the balance between congestion and cost.MaestríaMagister en Ingeniería EléctricaSistemas de PotenciaSmart GridsOptimización usando algoritmos heurísticos1 recurso en linea (121 paginas)application/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería EléctricaDepartamento de Ingeniería Eléctrica y ElectrónicaFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá110 - Metafísica::118 - Fuerza y energíaCongestiónCosto de operaciónOptimización multiobjetivoMOPSOPareto óptimoMicroredEnergías renovablesCongestionCost of operationMulti-object optimizationOptimal paretoMicrogridRenewable energiesProgramación de la operación de una microred de prueba minimizando la congestión y el costo de operación mediante algoritmos heurísticosProgramming the operation of a test microgrid minimizing congestion and operating cost through heuristic algorithmsTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[1] S. Huang, Q. Wu, Z. Liu, and A. H. Nielsen, “Review of congestion management methods for distribution networks with high penetration of distributed energy resources,” in IEEE PES Innovative Smart Grid Technologies Conference Europe, Jan. 2015, vol. 2015-Janua, no. January, doi: 10.1109/ISGTEurope.2014.7028811.[2] A. Abdolahi, J. Salehi, F. Samadi Gazijahani, and A. Safari, “Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system,” J. Renew. Sustain. Energy, vol. 10, no. 4, Jul. 2018, doi: 10.1063/1.5035081.[3] K. Spiliotis, S. Claeys, A. Ramos Gutierrez, and J. Driesen, “Utilizing Local Energy Storage for Congestion Management and Investment Deferral in Distribution Networks.”[4] J. M. L. Lezama, “PROPUESTAS ALTERNATIVAS PARA MANEJO DE CONGESTIÓN EN EL MERCADO DE ENERGÍA ELÉCTRICA COLOMBIANO,” 2006.[5] S. Gope, S. Dawn, R. Mitra, A. K. Goswami, and P. K. 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