Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión

El presente artículo describe los modelos de optimización recientemente aplicados al diseño y operación de los sistemas de potencia hacia la conformación de las redes inteligentes e identifica las tendencias, barreras y posibles brechas en esta área. Se describen modelos para optimizar el diseño y l...

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Autores:
Tello Maita, Josimar
Marulanda Guerra, Agustín
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Escuela Colombiana de Ingeniería Julio Garavito
Repositorio:
Repositorio Institucional ECI
Idioma:
spa
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oai:repositorio.escuelaing.edu.co:001/1403
Acceso en línea:
https://repositorio.escuelaing.edu.co/handle/001/1403
https://doi.org/10.15446/dyna.v84n202.63354
Palabra clave:
Sistemas híbridos de energía
Hybrid power systems
Optimización
Energías renovables
Redes inteligentes
Flujo óptimo de potencia
Optimization
Renewable energies
Smart grids
Optimal power flow
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openAccess
License
The author; licensee Universidad Nacional de Colombia.
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oai_identifier_str oai:repositorio.escuelaing.edu.co:001/1403
network_acronym_str ESCUELAIG2
network_name_str Repositorio Institucional ECI
repository_id_str
dc.title.spa.fl_str_mv Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
dc.title.alternative.spa.fl_str_mv Optimization models for power systems in the evolution to smart grids: A review
title Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
spellingShingle Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
Sistemas híbridos de energía
Hybrid power systems
Optimización
Energías renovables
Redes inteligentes
Flujo óptimo de potencia
Optimization
Renewable energies
Smart grids
Optimal power flow
title_short Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
title_full Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
title_fullStr Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
title_full_unstemmed Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
title_sort Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión
dc.creator.fl_str_mv Tello Maita, Josimar
Marulanda Guerra, Agustín
dc.contributor.author.none.fl_str_mv Tello Maita, Josimar
Marulanda Guerra, Agustín
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Modelación Estratégica en Energía y Potencia
dc.subject.armarc.spa.fl_str_mv Sistemas híbridos de energía
topic Sistemas híbridos de energía
Hybrid power systems
Optimización
Energías renovables
Redes inteligentes
Flujo óptimo de potencia
Optimization
Renewable energies
Smart grids
Optimal power flow
dc.subject.armarc.eng.fl_str_mv Hybrid power systems
dc.subject.proposal.spa.fl_str_mv Optimización
Energías renovables
Redes inteligentes
Flujo óptimo de potencia
Optimization
Renewable energies
Smart grids
Optimal power flow
description El presente artículo describe los modelos de optimización recientemente aplicados al diseño y operación de los sistemas de potencia hacia la conformación de las redes inteligentes e identifica las tendencias, barreras y posibles brechas en esta área. Se describen modelos para optimizar el diseño y la operación de los sistemas de potencia considerando las energías renovables, la generación distribuida, las micro redes, la gestión de la demanda y los sistemas de almacenamiento de energía. Se concluyó que es necesario validar muchos de los modelos que se han formulado recientemente para la optimización de la operación mediante pruebas con datos reales y a gran escala. Además, la gestión de la demanda y las micro redes son aspectos en los cuales se requieren desarrollar modelos para el flujo óptimo de potencia. Finalmente, es necesario predecir con mayor precisión las variables estocásticas para que estos modelos se adapten al comportamiento real del sistema.
publishDate 2017
dc.date.issued.none.fl_str_mv 2017
dc.date.accessioned.none.fl_str_mv 2021-05-06T15:09:37Z
2021-10-01T17:24:41Z
dc.date.available.none.fl_str_mv 2021-05-06T15:09:37Z
2021-10-01T17:24:41Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.none.fl_str_mv 10.15446/dyna.v84n202.63354
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https://doi.org/10.15446/dyna.v84n202.63354
dc.language.iso.spa.fl_str_mv spa
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dc.relation.citationedition.spa.fl_str_mv Revista DYNA, 84(202), pp. 102-111, September, 2017.
dc.relation.citationendpage.spa.fl_str_mv 111
dc.relation.citationissue.spa.fl_str_mv 202
dc.relation.citationstartpage.spa.fl_str_mv 102
dc.relation.citationvolume.spa.fl_str_mv 84
dc.relation.indexed.spa.fl_str_mv N/A
dc.relation.ispartofjournal.spa.fl_str_mv Dyna
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Electric Power Research Institute (EPRI), Report to NIST on the smart grid interoperability standards roadmap, EPRI, Contract No. SB1341-09-CN-0031-Deliverable, pp. 1-167, 2009.
National Institute of Standards and Technology, NIST Special Publication 1108 NIST Framework and roadmap for smart grid interoperability standards, 2010.
Pappu, V., Carvalho, M. and Pardalos, P.M., Optimization and security challenges in smart power grids. Springer, 2013.
Momoh, J.A., Electric power system applications of optimization, 53, 2001.
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Sánchez, P. et al., Modelos matemáticos de optimización, Madrid, 2010.
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Arrillaga, J., Watson, N. and Liu, Y., Flexible power transmission the HVDC Options. John Wiley & Sons, Ltd, 2007.
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Eftekharnejad, S., Vittal, V., Heydt, G.T., Keel, B. and Loehr, J., Impact of increased penetration of photovoltaic generation on power systems, Power Syst. IEEE Trans., 28(2), pp. 893-901, 2013. DOI: 10.1109/TPWRS.2012.2216294
Mahmoud, M.S. and AL-Sunni, F.M., Control and optimization of distributed generation systems, 2015.
Ackermann, T., Andersson, G. and Söder, L., Distributed generation: A definition, Electr. Power Syst. Res., 57(3), pp. 195-204, 2001. DOI: 10.1016/S0378-7796(01)00101-8
Li, R. and Zhou, F., Microgrid technology and engineering application. Elsevier Science & Technology Books, 2015.
Vanitha, R., Baskaran, J. and Sudhakaran, M., Multi Objective Optimal Power Flow with STATCOM using DE in WAFGP, Indian J. Sci. Technol., 8(2, pp. 191-198, 2015. DOI: 10.17485/ijst/2015/v8i1/56654
Kim, C.K., Sood, V.K., Jang, G.S., Lim, S.J. and Lee, S.J., HVDC Transmission: Power Conversion Applications in Power Systems. Wiley, 2009.
Carrizosa, M.J.J., Navas, F.D., Damm, G. and Lamnabhi-Lagarrigue, F.F., Optimal power flow in multi-terminal HVDC grids with offshore wind farms and storage devices, Int. J. Electr. Power Energy Syst., 65, pp. 291-298, 2015. DOI: 10.1016/j.ijepes.2014.10.016
Kondoh, J. et al., Electrical energy storage systems for energy networks, Energy Convers. Manag., 41(17), pp. 1863-1874, 2000.
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Zhao, H., Wu, Q., Hu, S., Xu, H., Rasmussen, C.N. and Nygaard, C., Review of energy storage system for wind power integration support, Appl. Energy, 137, pp. 545-553, DOI: 2014. 10.1016/j.apenergy.2014.04.103
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Abdelaziz, A.Y., Hegazy, Y.G., El-Khattam, W. and Othman, M.M., Optimal allocation of stochastically dependent renewable energy based distributed generators in unbalanced distribution networks, Electr. Power Syst. Res., 119, pp. 34-44, 2015. DOI: 10.1016/j.epsr.2014.09.005
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Summers, T., Warrington, J., Morari, M. and Lygeros, J., Stochastic optimal power flow based on conditional value at risk and distributional robustness, Int. J. Electr. Power Energy Syst., 72, pp. 116-125, 2015. DOI: 10.1016/j.ijepes.2015.02.024
Dufo-López, R. and Bernal-Agustín, J.L., Multi-objective design of PV-wind-diesel-hydrogen-battery systems, Renew. Energy, 33(12), pp. 2559-2572, 2008.
Ould-Bilal, B., Sambou, V., Ndiaye, P.A., Kébé, C.M.F. and Ndongo, M., Optimal design of a hybrid solar-wind-battery system using the minimization of the annualized cost system and the minimization of the loss of power supply probability (LPSP), Renew. Energy, 35(10), pp. 2388-2390, DOI: 2010. 10.1016/j.renene.2010.03.004
Martinez, G., Anderson, L.C., Anderson, L. and Martínez, G., Toward a scalable chance-constrained formulation for unit commitment to manage high penetration of variable generation, en 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014, pp. 723-730, 2014.
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Lasseter R., et al., The CERTS MicroGrid Concept, 2002.
Bhuiyan, F.A., Yazdani, A. and Primak, S.L., Optimal sizing approach for islanded microgrids, IET Renew. Power Gener., 9(2), pp. 166-175, 2015.
Ma, Y., Ji, J. and Tang, X., Triple-objective optimal sizing based on dynamic strategy for an islanded hybrid energy microgrid, Int. J. Green Energy, November, 2016.
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Ghiani, E., Vertuccio, C. and Pilo, F., Optimal sizing and management of a smart Microgrid for prevailing self-consumption, 2015 IEEE Eindhoven Power Tech. pp. 1-6, 2015. DOI: 10.1109/PTC.2015.7232554
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spelling Tello Maita, Josimarf598676c3d8942f8a3ae2d39bddaacfd600Marulanda Guerra, Agustín92272fe631b59c5ab46aafe2f81739a7600Grupo de Modelación Estratégica en Energía y Potencia2021-05-06T15:09:37Z2021-10-01T17:24:41Z2021-05-06T15:09:37Z2021-10-01T17:24:41Z20170012-7353https://repositorio.escuelaing.edu.co/handle/001/140310.15446/dyna.v84n202.63354https://doi.org/10.15446/dyna.v84n202.63354El presente artículo describe los modelos de optimización recientemente aplicados al diseño y operación de los sistemas de potencia hacia la conformación de las redes inteligentes e identifica las tendencias, barreras y posibles brechas en esta área. Se describen modelos para optimizar el diseño y la operación de los sistemas de potencia considerando las energías renovables, la generación distribuida, las micro redes, la gestión de la demanda y los sistemas de almacenamiento de energía. Se concluyó que es necesario validar muchos de los modelos que se han formulado recientemente para la optimización de la operación mediante pruebas con datos reales y a gran escala. Además, la gestión de la demanda y las micro redes son aspectos en los cuales se requieren desarrollar modelos para el flujo óptimo de potencia. Finalmente, es necesario predecir con mayor precisión las variables estocásticas para que estos modelos se adapten al comportamiento real del sistema.The present paper aims to describe the optimization models recently applied to the design and operation of power systems in the road to the formation of smart grids and to identify the trends, challenges and possible gaps existing in this field of study. The models described allow performing optimization of the design and operation of power systems considering aspects as renewable energies and its related variability, distributed generation and micro grids, demand-site management and energy storage systems. Conclusions point out that several of the models recently formulated need to be validated with real data and large-scale systems tests. Moreover, demand-site management and micro grids are aspects that lack of the development of complete optimal power flow models. Finally, the accurate forecasting of stochastic variables is necessary to accomplish a better adaptation of models to real behavior of the power system.a Facultad de Ingeniería, Universidad del Zulia, Maracaibo, Venezuela. jtello@fing.luz.edu.ve b Escuela Colombiana de Ingeniería Julio Garavito, Bogotá, Colombia. agustin.marulanda@escuelaing.edu.co10 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellin, Colombia.The author; licensee Universidad Nacional de Colombia.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2https://revistas.unal.edu.co/index.php/dyna/article/view/63354/62427Modelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisiónOptimization models for power systems in the evolution to smart grids: A reviewArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85Revista DYNA, 84(202), pp. 102-111, September, 2017.11120210284N/ADynaBorlase, S., Smart grids: Infrastructure, technology, and solutions. CRC Press, 2016.Electric Power Research Institute (EPRI), Report to NIST on the smart grid interoperability standards roadmap, EPRI, Contract No. SB1341-09-CN-0031-Deliverable, pp. 1-167, 2009.National Institute of Standards and Technology, NIST Special Publication 1108 NIST Framework and roadmap for smart grid interoperability standards, 2010.Pappu, V., Carvalho, M. and Pardalos, P.M., Optimization and security challenges in smart power grids. Springer, 2013.Momoh, J.A., Electric power system applications of optimization, 53, 2001.Calafiore, G.C. and Ghaoui, LE., Optimization models. Cambridge University Press, 2014.Sánchez, P. et al., Modelos matemáticos de optimización, Madrid, 2010.Renewable Energy Policy Network for the 21st Century (REN21), Renewables 2016: Global status report. 2016.Arrillaga, J., Watson, N. and Liu, Y., Flexible power transmission the HVDC Options. John Wiley & Sons, Ltd, 2007.European Technology Platform on Smartgrids, Consolidated view of the european platform on smartgrids, 2015.U.S. Department of Energy, 2014 Smart grid system report(August. 2014.Holttinen, H., Meibom, P. and Orths, A., Impacts of large amounts of wind power on design and operation of power systems, results of IEA collaboration, Wind Energy, 14(2), pp. 179-192, 2011. DOI: 10.1109/TPWRS.2010.2070848Eftekharnejad, S., Vittal, V., Heydt, G.T., Keel, B. and Loehr, J., Impact of increased penetration of photovoltaic generation on power systems, Power Syst. IEEE Trans., 28(2), pp. 893-901, 2013. DOI: 10.1109/TPWRS.2012.2216294Mahmoud, M.S. and AL-Sunni, F.M., Control and optimization of distributed generation systems, 2015.Ackermann, T., Andersson, G. and Söder, L., Distributed generation: A definition, Electr. Power Syst. Res., 57(3), pp. 195-204, 2001. DOI: 10.1016/S0378-7796(01)00101-8Li, R. and Zhou, F., Microgrid technology and engineering application. Elsevier Science & Technology Books, 2015.Vanitha, R., Baskaran, J. and Sudhakaran, M., Multi Objective Optimal Power Flow with STATCOM using DE in WAFGP, Indian J. Sci. Technol., 8(2, pp. 191-198, 2015. DOI: 10.17485/ijst/2015/v8i1/56654Kim, C.K., Sood, V.K., Jang, G.S., Lim, S.J. and Lee, S.J., HVDC Transmission: Power Conversion Applications in Power Systems. Wiley, 2009.Carrizosa, M.J.J., Navas, F.D., Damm, G. and Lamnabhi-Lagarrigue, F.F., Optimal power flow in multi-terminal HVDC grids with offshore wind farms and storage devices, Int. J. Electr. Power Energy Syst., 65, pp. 291-298, 2015. DOI: 10.1016/j.ijepes.2014.10.016Kondoh, J. et al., Electrical energy storage systems for energy networks, Energy Convers. Manag., 41(17), pp. 1863-1874, 2000.Luo, X., Wang, J., Dooner, M. and Clarke, J., Overview of current development in electrical energy storage technologies and the application potential in power system operation, Appl. Energy, 137, pp. 511-536, 2015. 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DOI: 10.1109/PTC.2015.7232554Sistemas híbridos de energíaHybrid power systemsOptimizaciónEnergías renovablesRedes inteligentesFlujo óptimo de potenciaOptimizationRenewable energiesSmart gridsOptimal power flowLICENSElicense.txttext/plain1881https://repositorio.escuelaing.edu.co/bitstream/001/1403/1/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD51open accessORIGINALModelos de optimización para sistemas de potencia en la evolución hacia redes inteligentes: Una revisión.pdfapplication/pdf844897https://repositorio.escuelaing.edu.co/bitstream/001/1403/2/Modelos%20de%20optimizaci%c3%b3n%20para%20sistemas%20de%20potencia%20en%20la%20evoluci%c3%b3n%20hacia%20redes%20inteligentes%3a%20Una%20revisi%c3%b3n.pdf9ffb1ab20c9f77927fb9d5dd40a87ff0MD52metadata only accessTEXT10.15446dyna.v84n202.63354.pdf.txt10.15446dyna.v84n202.63354.pdf.txtExtracted texttext/plain61923https://repositorio.escuelaing.edu.co/bitstream/001/1403/3/10.15446dyna.v84n202.63354.pdf.txt0b1dcff2ffb75f4aad2adeab16221842MD53open 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