Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow
This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimizationof three different objec...
- Autores:
-
Grisales-Noreña, Luis Fernando
Rosales-Muñoz, Andrés Alfonso
Cortés-Caicedo, Brandon
Montoya, Oscar
Andrade, Fabio
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/11854
- Palabra clave:
- Direct current networks
Grid-connected network
Standalone network
Metaheuristic optimization methods
Master–slave methodology
Photovoltaic generation
Minimization of operating costs
Minimization of energy losses
Minimization of CO2 emissions
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
title |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
spellingShingle |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow Direct current networks Grid-connected network Standalone network Metaheuristic optimization methods Master–slave methodology Photovoltaic generation Minimization of operating costs Minimization of energy losses Minimization of CO2 emissions LEMB |
title_short |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
title_full |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
title_fullStr |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
title_full_unstemmed |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
title_sort |
Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow |
dc.creator.fl_str_mv |
Grisales-Noreña, Luis Fernando Rosales-Muñoz, Andrés Alfonso Cortés-Caicedo, Brandon Montoya, Oscar Andrade, Fabio |
dc.contributor.author.none.fl_str_mv |
Grisales-Noreña, Luis Fernando Rosales-Muñoz, Andrés Alfonso Cortés-Caicedo, Brandon Montoya, Oscar Andrade, Fabio |
dc.subject.keywords.spa.fl_str_mv |
Direct current networks Grid-connected network Standalone network Metaheuristic optimization methods Master–slave methodology Photovoltaic generation Minimization of operating costs Minimization of energy losses Minimization of CO2 emissions |
topic |
Direct current networks Grid-connected network Standalone network Metaheuristic optimization methods Master–slave methodology Photovoltaic generation Minimization of operating costs Minimization of energy losses Minimization of CO2 emissions LEMB |
dc.subject.armarc.none.fl_str_mv |
LEMB |
description |
This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimizationof three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times. |
publishDate |
2022 |
dc.date.issued.none.fl_str_mv |
2022-12-26 |
dc.date.accessioned.none.fl_str_mv |
2023-05-24T21:12:57Z |
dc.date.available.none.fl_str_mv |
2023-05-24T21:12:57Z |
dc.date.submitted.none.fl_str_mv |
2023-05-24 |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.hasversion.spa.fl_str_mv |
info:eu-repo/semantics/draft |
dc.type.spa.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
status_str |
draft |
dc.identifier.citation.spa.fl_str_mv |
Grisales-Noreña, L.F.; Rosales-Muñoz, A.A.; Cortés-Caicedo, B.; Montoya, O.D.; Andrade, F. Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow. Mathematics 2023, 11, 93. https://doi.org/10.3390/math11010093 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/11854 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3390/math11010093 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
identifier_str_mv |
Grisales-Noreña, L.F.; Rosales-Muñoz, A.A.; Cortés-Caicedo, B.; Montoya, O.D.; Andrade, F. Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow. Mathematics 2023, 11, 93. https://doi.org/10.3390/math11010093 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
url |
https://hdl.handle.net/20.500.12585/11854 https://doi.org/10.3390/math11010093 |
dc.language.iso.spa.fl_str_mv |
eng |
language |
eng |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
dc.rights.cc.*.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 Internacional Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.format.extent.none.fl_str_mv |
28 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
dc.publisher.sede.spa.fl_str_mv |
Campus Tecnológico |
dc.source.spa.fl_str_mv |
Mathematics Vol. 11 No. 1 (2023) |
institution |
Universidad Tecnológica de Bolívar |
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Grisales-Noreña, Luis Fernando7c27cda4-5fe4-4686-8f72-b0442c58a5d1Rosales-Muñoz, Andrés Alfonso1cadd052-2b2e-4872-b1d3-7679f6be5f2aCortés-Caicedo, Brandon0b676225-338d-48dc-8f2a-694085d9bb42Montoya, Oscar008c220c-d50f-41c7-8294-a0fd23bfd9f2Andrade, Fabio3994c1b0-72d3-421d-97ba-4bf241fef00f2023-05-24T21:12:57Z2023-05-24T21:12:57Z2022-12-262023-05-24Grisales-Noreña, L.F.; Rosales-Muñoz, A.A.; Cortés-Caicedo, B.; Montoya, O.D.; Andrade, F. Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flow. Mathematics 2023, 11, 93. https://doi.org/10.3390/math11010093https://hdl.handle.net/20.500.12585/11854https://doi.org/10.3390/math11010093Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThis document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimizationof three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.28 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 InternacionalAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Mathematics Vol. 11 No. 1 (2023)Optimal Operation of PV Sources in DC Grids for Improving Technical, Economical, and Environmental Conditions by Using Vortex Search Algorithm and a Matrix Hourly Power Flowinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_b1a7d7d4d402bcceDirect current networksGrid-connected networkStandalone networkMetaheuristic optimization methodsMaster–slave methodologyPhotovoltaic generationMinimization of operating costsMinimization of energy lossesMinimization of CO2 emissionsLEMBCartagena de IndiasCampus TecnológicoPúblico generalSaeed, M.H.; Fangzong, W.; Kalwar, B.A.; Iqbal, S. A Review on Microgrids’ Challenges & Perspectives. IEEE Access 2021, 9, 166502–166517.Löfquist, L. Is there a universal human right to electricity? Int. J. Hum. Rights 2020, 24, 711–723.Sarkodie, S.A.; Adams, S. Electricity access, human development index, governance and income inequality in Sub-Saharan Africa. Energy Rep. 2020, 6, 455–466.Saint Akadiri, S.; Alola, A.A.; Olasehinde-Williams, G.; Etokakpan, M.U. The role of electricity consumption, globalization and economic growth in carbon dioxide emissions and its implications for environmental sustainability targets. Sci. Total. Environ. 2020, 708, 134653.Lamb, W.F.; Wiedmann, T.; Pongratz, J.; Andrew, R.; Crippa, M.; Olivier, J.G.J.; Wiedenhofer, D.; Mattioli, G.; Khourdajie, A.A.; House, J.; et al. A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ. Res. Lett. 2021, 16, 073005.Valencia, A.; Hincapie, R.A.; Gallego, R.A. Optimal location, selection, and operation of battery energy storage systems and renewable distributed generation in medium–low voltage distribution networks. J. Energy Storage 2021, 34, 102158Abdelgawad, H.; Sood, V.K. A comprehensive review on microgrid architectures for distributed generation. In Proceedings of the 2019 IEEE Electrical Power and Energy Conference (EPEC), IEEE, Montreal, QC, Canada, 6–18 October 2019; pp. 1–8.Li, J.; Liu, F.; Wang, Z.; Low, S.H.; Mei, S. Optimal power flow in stand-alone DC microgrids. IEEE Trans. Power Syst. 2018, 33, 5496–5506.Garcés, A. On the convergence of Newton’s method in power flow studies for DC microgrids. IEEE Trans. Power Syst. 2018, 33, 5770–5777.Tawalbeh, M.; Al-Othman, A.; Kafiah, F.; Abdelsalam, E.; Almomani, F.; Alkasrawi, M. Environmental impacts of solar photovoltaic systems: A critical review of recent progress and future outlook. Sci. Total. Environ. 2021, 759, 143528.Moreno, C.; Milanes, C.B.; Arguello, W.; Fontalvo, A.; Alvarez, R.N. Challenges and perspectives of the use of photovoltaic solar energy in Colombia. Int. J. Electr. Comput. Eng. 2022, 12, 4521–4528.López, A.R.; Krumm, A.; Schattenhofer, L.; Burandt, T.; Montoya, F.C.; Oberländer, N.; Oei, P.Y. Solar PV generation in Colombia A qualitative and quantitative approach to analyze the potential of solar energy market. Renew. Energy 2020, 148, 12Insuasty-Reina, J.G. A system dynamics model for the analysis of CO2 emissions derived from the inclusion of hydrogen obtained from coal in the energy matrix in Colombia. Int. J. Energy Econ. Policy 2022, 12, 72–82.Li, C.; De Bosio, F.; Chen, F.; Chaudhary, S.K.; Vasquez, J.C.; Guerrero, J.M. Economic dispatch for operating cost minimization under real-time pricing in droop-controlled DC microgrid. IEEE J. Emerg. Sel. Top. Power Electron. 2016, 5, 587–595. [Gao, S.; Jia, H.; Marnay, C. Techno-economic evaluation of mixed AC and DC power distribution network for integrating large-scale photovoltaic power generation. IEEE Access 2019, 7, 105019–105029.Tan, Q.; Ding, Y.; Ye, Q.; Mei, S.; Zhang, Y.; Wei, Y. Optimization and evaluation of a dispatch model for an integrated wind-photovoltaic-thermal power system based on dynamic carbon emissions trading. Appl. Energy 2019, 253, 113598.Younes, Z.; Alhamrouni, I.; Mekhilef, S.; Reyasudin, M. A memory-based gravitational search algorithm for solving economic dispatch problem in micro-grid. Ain Shams Eng. J. 2021, 12, 1985–1994.Velasquez, O.S.; Montoya Giraldo, O.D.; Garrido Arevalo, V.M.; Grisales Norena, L.F. Optimal power flow in direct-current power grids via black hole optimization. Adv. Electr. Electron. Eng. 2019, 17, 24–32.Garzon-Rivera, O.; Ocampo, J.; Grisales-Noreña, L.; Montoya, O.; Rojas-Montano, J. Optimal power flow in Direct Current Networks using the antlion optimizer. Stat. Optim. Inf. Comput. 2020, 8, 846–857.Radosavljevi´c, J.; Arsi´c, N.; Milovanovi´c, M.; Ktena, A. Optimal placement and sizing of renewable distributed generation using hybrid metaheuristic algorithm. J. Mod. Power Syst. Clean Energy 2020, 8, 499–510.Montoya, O.D.; Grisales-Noreña, L.F.; Gil-González, W.; Alcalá, G.; Hernandez-Escobedo, Q. Optimal location and sizing of PV sources in DC networks for minimizing greenhouse emissions in diesel generators. Symmetry 2020, 12, 322.Grisales-Noreña, L.F.; Montoya, O.D.; Ramos-Paja, C.A. An energy management system for optimal operation of BSS in DC distributed generation environments based on a parallel PSO algorithm. J. Energy Storage 2020, 29, 101488. [Rosales-Muñoz, A.A.; Grisales-Noreña, L.F.; Montano, J.; Montoya, O.D.; Perea-Moreno, A.J. Application of the multiverse optimization method to solve the optimal power flow problem in direct current electrical networks. Sustainability 2021, 13, 8703.Sahoo, R.R.; Ray, M. PSO based test case generation for critical path using improved combined fitness function. J. King Saud-Univ.-Comput. Inf. Sci. 2020, 32, 479–490.Zhang, X.; Beram, S.M.; Haq, M.A.; Wawale, S.G.; Buttar, A.M. Research on algorithms for control design of human–machine interface system using ML. Int. J. Syst. Assur. Eng. Manag. 2021, 13, 462–469.Jin, Y.; Olhofer, M.; Sendhoff, B. A framework for evolutionary optimization with approximate fitness functions. IEEE Trans. Evol. Comput. 2002, 6, 481–494.Grisales-Noreña, L.F.; Montoya, O.D.; Hernández, J.C.; Ramos-Paja, C.A.; Perea-Moreno, A.J. A Discrete-Continuous PSO for the Optimal Integration of D-STATCOMs into Electrical Distribution Systems by Considering Annual Power Loss and Investment Costs. Mathematics 2022, 10, 2453Özkı¸s, A.; Babalık, A. A novel metaheuristic for multi-objective optimization problems: The multi-objective vortex search algorithm. Inf. Sci. 2017, 402, 124–148.Fathy, A.; Abd Elaziz, M.; Alharbi, A.G. A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell. Renew. Energy 2020, 146, 1833–1845.Altintasi, C.; Aydin, O.; Taplamacioglu, M.C.; Salor, O. Power system harmonic and interharmonic estimation using Vortex Search Algorithm. Electr. Power Syst. Res. 2020, 182, 106187.Grisales-Noreña, L.; Montoya-Giraldo, O.; Gil-González, W. Optimal Integration of Distributed Generators into DC Microgrids Using a Hybrid Methodology: Genetic and Vortex Search Algorithms. Arab. J. Sci. Eng. 2022, 47, 14657–14672.Montoya, O.D.; Garrido, V.M.; Gil-González, W.; Grisales-Noreña, L.F. Power flow analysis in DC grids: Two alternative numerical methods. IEEE Trans. Circuits Syst. Ii: Express Briefs 2019, 66, 1865–1869.Baran, M.E.; Wu, F.F. Network reconfiguration in distribution systems for loss reduction and load balancing. IEEE Power Eng. Rev. 1989, 9, 101–102.v. Falaghi, H.; Ramezani, M.; Haghifam, M.R.; Milani, K.R. Optimal selection of conductors in radial distribution systems with time varying load. In Proceedings of the CIRED 2005-18th International Conference and Exhibition on Electricity Distribution, IET, Turin, Italy, 6–9 June 2005; pp. 1–4.Hassan, Q.; Jaszczur, M.; Przenzak, E.; Abdulateef, J. The PV cell temperature effect on the energy production and module efficiency. Contemp. Probl. Power Eng. Environ. Prot. 2016, 33, 1.Schwingshackl, C.; Petitta, M.; Wagner, J.; Belluardo, G.; Moser, D.; Castelli, M.; Zebisch, M.; Tetzlaff, A. Wind Effect on PV Module Temperature: Analysis of Different Techniques for an Accurate Estimation. Energy Procedia 2013, 40, 77–86.NASA. NASA Prediction Of Worldwide Energy Resources, Washington D. C., United States. Available online: https://power. larc.nasa.gov/ (accessed on 21 September 2022).XM SA ESP. Sinergox Database, Colombia. Available online: https://sinergox.xm.com.co/Paginas/Home.aspx (accessed on 21 September 2022).Instituto de Planificación y Promoción de Soluciones Energéticas para Zonas No Interconectadas. Informes Mensuales de Te limetría, Colombia. Available online: https://ipse.gov.co/cnm/informe-mensuales-telemetria/ (accessed on 2Sistema Único de Información de Servicios Públicos Domicialiarios. Consolidado de Energía por Empresa y Departamento, Colombia. Available online: https://sui.superservicios.gov.co/Reportes-del-sector/Energia/Reportes-comerciales/Consolidado de-energia-por-empresa-y-departamento (accessed on 21 September 2022)Sistema Único de Información de Servicios Públicos Domicialiarios. Consolidado de Información téCnica Operativa ZNI, Colombia. Available online: https://sui.superservicios.gov.co/Reportes-del-sector/Energia/Reportes-comerciales/Consolidado de-informacion-tecnica-operativa-ZNI (accessed on 21 September 2022).XM SA EPS. En Colombia Factor de emisión de CO2 por generación eléctrica del Sistema Interconectado: 164.38 gramos de CO2 por kilovatio hora, Colombia. Available online: https://www.xm.com.co/noticias/en-colombia-factor-de-emision-de-co2-por generacion-electrica-del-sistema-interconectado (accessed on 21 September 2022)Academia Colombiana de Ciencias Exactas, F. Factores de Emisión de los Combustibles Colombianos, Colombia, 2016. Available online: https://bdigital.upme.gov.co/bitstream/handle/001/1285/17%20Factores%20de%20emision%20de%20combustibles. pdf;jsessionid=5016BD31B13035A5FBF551BC26B1293E?sequence=18 (accessed on 21 September 2022).Abou El Ela, A.; El-Sehiemy, R.A.; Shaheen, A.; Shalaby, A. Application of the crow search algorithm for economic environmental dispatch. In Proceedings of the 2017 Nineteenth International Middle East Power Systems Conference (MEPCON), IEEE, Cairo, Egypt, 19–21 December 2017; pp. 78–83.Verma, S.; Shiva, C.K. A novel salp swarm algorithm for expansion planning with security constraints. Iran. J. Sci. Technol. Trans. Electr. 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