Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region
A hybrid system was analyzed and optimized to produce electric energy in non-interconnected zones in the Colombian Caribbean region, contributing to the reduction of greenhouse gas emissions and the improvement in efficient energy management. A comparative analysis of the performance of hybrid was c...
- Autores:
-
Guillermo, Valencia
Benavides, Aldair
Cárdenas, Yulineth
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/4934
- Acceso en línea:
- https://hdl.handle.net/11323/4934
https://repositorio.cuc.edu.co/
- Palabra clave:
- Fuel cell
Wind energy
Solar energy
Hybrid energy system
Colombian Caribbean region
Multiobjective optimization
- Rights
- openAccess
- License
- http://creativecommons.org/publicdomain/zero/1.0/
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| dc.title.spa.fl_str_mv |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| title |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| spellingShingle |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region Fuel cell Wind energy Solar energy Hybrid energy system Colombian Caribbean region Multiobjective optimization |
| title_short |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| title_full |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| title_fullStr |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| title_full_unstemmed |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| title_sort |
Economic and environmental multiobjective optimization of a wind–solar–fuel cell hybrid energy system in the colombian caribbean region |
| dc.creator.fl_str_mv |
Guillermo, Valencia Benavides, Aldair Cárdenas, Yulineth |
| dc.contributor.author.spa.fl_str_mv |
Guillermo, Valencia Benavides, Aldair Cárdenas, Yulineth |
| dc.subject.spa.fl_str_mv |
Fuel cell Wind energy Solar energy Hybrid energy system Colombian Caribbean region Multiobjective optimization |
| topic |
Fuel cell Wind energy Solar energy Hybrid energy system Colombian Caribbean region Multiobjective optimization |
| description |
A hybrid system was analyzed and optimized to produce electric energy in non-interconnected zones in the Colombian Caribbean region, contributing to the reduction of greenhouse gas emissions and the improvement in efficient energy management. A comparative analysis of the performance of hybrid was conducted using a proposed model, built with historical data for meteorological conditions, wind speed, and solar radiation. The model is integrated by a Southwest Wind Power Inc. wind turbine AIR 403, a proton-exchange membrane fuel cell (PEM), an electrolyzer, a solar panel, and a regulator based on proportional, integral, and derivative (PID) controllers to manipulate oxygen and hydrogen flow entering in the fuel cell. The transient responses of the cell voltage, current, and power were obtained for the demand of 200 W under changes in solar radiation and wind speed for each day of the year 2013 in different meteorological stations, such as Ernesto Cortissoz airport, Puerto Bolívar, Alfonso Lopez airport, and Simon Bolívar airport. Through the adjustment of the hydrogen and oxygen flow into the fuel cell, the maximum contribution of power generation from the fuel cell was presented for the Simon Bolívar airport in November with a value of 158.35 W (9.45%). Multiobjective design optimization under a Pareto diagram front is presented for each place studied to minimize the levelized cost of energy and CO2 emission, where the objective control variables are the number of panel and stack in the photovoltaic (PV) system and PEM. |
| publishDate |
2019 |
| dc.date.accessioned.none.fl_str_mv |
2019-07-04T18:50:12Z |
| dc.date.available.none.fl_str_mv |
2019-07-04T18:50:12Z |
| dc.date.issued.none.fl_str_mv |
2019-05-15 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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1996-1073 |
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https://hdl.handle.net/11323/4934 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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1996-1073 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.spa.fl_str_mv |
https://doi.org/10.3390/en12112119 |
| dc.relation.references.spa.fl_str_mv |
1. Sinha, A. Inequality of renewable energy generation across OECD countries: A note. Renew. Sustain. Energy Rev. 2017, 79, 9–14. [CrossRef] 2. Kim, K.; Park, H.; Kim, H. Real options analysis for renewable energy investment decisions in developing countries. Renew. Sustain. Energy Rev. 2017, 75, 918–926. [CrossRef] 3. Shukla, A.K.; Sudhakar, K.; Baredar, P. Renewable energy resources in South Asian countries: Challenges, policy and recommendations. Resour. Technol. 2017, 3, 342–346. [CrossRef] 4. Ahmed, S.; Mahmood, A.; Hasan, A.; Sidhu, G.A.S.; Butt, M.F.U. A comparative review of China, India and Pakistan renewable energy sectors and sharing opportunities. Renew. Sustain. Energy Rev. 2016, 57, 216–225. [CrossRef] 5. Romo-Fernández, L.M.; López-Pujalte, C.; Bote, V.P.G.; Moya-Anegón, F. Analysis of Europe’s scientific production on renewable energies. Renew. Energy 2011, 36, 2529–2537. [CrossRef] 6. Fernández, L.M.R.; Bote, V.P.G.; Anegón, F.M. Análisis de la producción científica española en energías renovables, sostenibilidad y medio ambiente (Scopus, 2003–2009) en el contexto mundial. Investig. Bibl. Arch. Bibl. Inf. 2013, 27, 125–151. [CrossRef] 7. Abbasi, S.A.; Abbasi, T. Abbasi. Impact of wind-energy generation on climate: A rising spectre. Renew. Sustain. Energy Rev. 2016, 59, 1591–1598. [CrossRef] 8. Congreso de Colombia Ley N◦ 1715 del 13 de mayo de 2014. Available online: www.fedebiocombustibles. com/files/1715.pdf (accessed on 8 September 2018). 9. Kannan, N.; Vakeesan, D. Solar energy for future world: A review. Renew. Sustain. Energy Rev. 2016, 62, 1092–1105. [CrossRef] 10. Islam, M.R.; Mekhilef, S.; Saidur, R. Progress and recent trends of wind energy technology. Renew. Sustain. Energy Rev. 2013, 21, 456–468. [CrossRef] 11. Procolombia Electric Power in Colombia. Power Generation—2015. Available online: http://www.energynet. co.uk/webfm_send/1839 (accessed on 9 January 2019). 12. Budes, F.B.; Escorcia, Y.C.; Ochoa, G.V. Optimization of a Biomass, solar and fuel cell Hybrid energy systems for a specific energy load using Homer Pro software®. Int. J. ChemTech Res. 2018, 11, 335–340. 13. Sikka, M.; Thornton, T.F.; Worl, R. Sustainable Biomass Energy and Indigenous Cultural Models of Well-being in an Alaska Forest Ecosystem. Ecol. Soc. 2013, 18, 531–543. [CrossRef] 14. Vides-Prado, A.; Camargo, E.O.; Vides-Prado, C.; Orozco, I.H.; Chenlo, F.; Candelo, J.E.; Sarmiento, A.B. Techno-economic feasibility analysis of photovoltaic systems in remote areas for indigenous communities in the Colombian Guajira. Renew. Sustain. Energy Rev. 2018, 82, 4245–4255. [CrossRef] 15. Mikati, M.; Santos, M.; Armenta, C. Modelado y Simulación de un Sistema Conjunto de Energía Solar y Eólica para Analizar su Dependencia de la Red Eléctrica. Rev. Iberoam. Autom. Inform. Ind. RIAI 2012, 9, 267–281. [CrossRef] 16. Bordons, C.; García-Torres, F.; Valverde, L. Gestión Óptima de la Energía en Microrredes con Generación Renovable. Rev. Iberoam. Autom. Inform. Ind. RIAI 2015, 12, 117–132. [CrossRef] 17. López, A.; Somolinos, J.A.; Núñez, L.R. Modelado Energético de Convertidores Primarios para el Aprovechamiento de las Energías Renovables Marinas. Rev. Iberoam. Autom. Inform. Ind. RIAI 2014, 11, 224–235. [CrossRef] 18. Esmaeili, S.; Shafiee, M. Simulation of Dynamic Response of Small Wind-Photovoltaic-Fuel Cell Hybrid Energy System. Smart Grid Renew. Energy 2012, 3, 194–203. [CrossRef] 19. Ochoa, G.V.; Blanco, C.; Martinez, C.; Ramos, E. Fuzzy Adaptive Control Applied to a Hybrid Electric-Power Generation System (HEPGS). Indian J. Sci. Technol 2017, 10, 1–9. [CrossRef] 20. De Dias, C.L.; Branco, D.A.C.; Arouca, M.C.; Legey, L.F.L. Performance estimation of photovoltaic technologies in Brazil. Renew. Energy 2017, 114, 367–375. [CrossRef] 21. Abbes, D.; Martinez, A.; Champenois, G. Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems. Math. Comput. Simul. 2014, 98, 46–62. [CrossRef] 22. Parida, B.; Iniyan, S.; Goic, R. A review of solar photovoltaic technologies. Renew. Sustain. Energy Rev. 2011, 15, 1625–1636. [CrossRef] 23. Cancino-Solórzano, Y.; Xiberta-Bernat, J. Statistical analysis of wind power in the region of Veracruz (Mexico). Renew. Energy 2009, 34, 1628–1634. [CrossRef] 24. Observatorio del Caribe Colombiano. Región Caribe Colombiana 2015. Available online: http://www.ocaribe. org/region-caribe (accessed on 7 January 2019). 25. Pandiarajan, N.; Ramaprabha, R.; Muthu, R. Application of circuit model for photovoltaic energy conversion system. Int. J. Photoenergy 2012, 2012, 410401. [CrossRef] 26. ¸Sen, S.; Demirer, G.N. Anaerobic tratment of real textile wastewater with a fluidized bed reactor. Water Res. 2003, 37, 1868–1878. [CrossRef] 27. Padilla, R.V.; Demirkaya, G.; Goswami, D.Y.; Stefanakos, E.; Rahman, M.M. Heat transfer analysis of parabolic trough solar receiver. Appl. Energy 2011, 88, 5097–5110. [CrossRef] 28. Ochoa, G.V.; Chamorro, M.V.; Jiménez, J.P. Análisis Estadístico de la Velocidad y Dirección del Viento en la Región Caribe Colombiana con Énfasis en la Guajira; Universidad del Atlántico: Barranquilla, Colombia, 2016. 29. Bilil, H.; Aniba, G.; Maaroufi, M. Multiobjective Optimization of Renewable Energy Penetration Rate in Power Systems. Energy Procedia 2014, 50, 368–375. [CrossRef] 30. Kamjoo, A.; Maheri, A.; Dizqah, A.M.; Putrus, G.A. Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. Int. J. Electr. Power Energy Syst. 2016, 74, 187–194. [CrossRef] 31. Ighravwe, D. A CRITIC-TOPSIS framework for hybrid renewable energy systems evaluation under techno-economic requirements. J. Proj. Manag. 2019, 4, 109–126. 32. Chen, S.J.; Hwang, C.L. Fuzzy Multiple Attribute Decision Making Methods. In Fuzzy Multiple Attribute Decision Making; Springer: Berlin/Heidelberg, Germany, 1992; Volume 375, pp. 289–486. |
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Guillermo, ValenciaBenavides, AldairCárdenas, Yulineth2019-07-04T18:50:12Z2019-07-04T18:50:12Z2019-05-151996-1073https://hdl.handle.net/11323/4934Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/A hybrid system was analyzed and optimized to produce electric energy in non-interconnected zones in the Colombian Caribbean region, contributing to the reduction of greenhouse gas emissions and the improvement in efficient energy management. A comparative analysis of the performance of hybrid was conducted using a proposed model, built with historical data for meteorological conditions, wind speed, and solar radiation. The model is integrated by a Southwest Wind Power Inc. wind turbine AIR 403, a proton-exchange membrane fuel cell (PEM), an electrolyzer, a solar panel, and a regulator based on proportional, integral, and derivative (PID) controllers to manipulate oxygen and hydrogen flow entering in the fuel cell. The transient responses of the cell voltage, current, and power were obtained for the demand of 200 W under changes in solar radiation and wind speed for each day of the year 2013 in different meteorological stations, such as Ernesto Cortissoz airport, Puerto Bolívar, Alfonso Lopez airport, and Simon Bolívar airport. Through the adjustment of the hydrogen and oxygen flow into the fuel cell, the maximum contribution of power generation from the fuel cell was presented for the Simon Bolívar airport in November with a value of 158.35 W (9.45%). Multiobjective design optimization under a Pareto diagram front is presented for each place studied to minimize the levelized cost of energy and CO2 emission, where the objective control variables are the number of panel and stack in the photovoltaic (PV) system and PEM.Guillermo, Valencia-0000-0001-5437-1964-600Benavides, Aldair-ecacf430-1d70-4386-adf5-6ad8a05d6e21-0Cárdenas, Yulineth-4adce3af-dcc3-45df-afb9-13e19a39ec62-0engEnergieshttps://doi.org/10.3390/en121121191. Sinha, A. Inequality of renewable energy generation across OECD countries: A note. Renew. Sustain. Energy Rev. 2017, 79, 9–14. [CrossRef] 2. Kim, K.; Park, H.; Kim, H. Real options analysis for renewable energy investment decisions in developing countries. Renew. Sustain. Energy Rev. 2017, 75, 918–926. [CrossRef] 3. Shukla, A.K.; Sudhakar, K.; Baredar, P. Renewable energy resources in South Asian countries: Challenges, policy and recommendations. Resour. Technol. 2017, 3, 342–346. [CrossRef] 4. Ahmed, S.; Mahmood, A.; Hasan, A.; Sidhu, G.A.S.; Butt, M.F.U. A comparative review of China, India and Pakistan renewable energy sectors and sharing opportunities. Renew. Sustain. Energy Rev. 2016, 57, 216–225. [CrossRef] 5. Romo-Fernández, L.M.; López-Pujalte, C.; Bote, V.P.G.; Moya-Anegón, F. Analysis of Europe’s scientific production on renewable energies. Renew. Energy 2011, 36, 2529–2537. [CrossRef] 6. Fernández, L.M.R.; Bote, V.P.G.; Anegón, F.M. Análisis de la producción científica española en energías renovables, sostenibilidad y medio ambiente (Scopus, 2003–2009) en el contexto mundial. Investig. Bibl. Arch. Bibl. Inf. 2013, 27, 125–151. [CrossRef] 7. Abbasi, S.A.; Abbasi, T. Abbasi. Impact of wind-energy generation on climate: A rising spectre. Renew. Sustain. Energy Rev. 2016, 59, 1591–1598. [CrossRef] 8. Congreso de Colombia Ley N◦ 1715 del 13 de mayo de 2014. Available online: www.fedebiocombustibles. com/files/1715.pdf (accessed on 8 September 2018). 9. Kannan, N.; Vakeesan, D. Solar energy for future world: A review. Renew. Sustain. Energy Rev. 2016, 62, 1092–1105. [CrossRef] 10. Islam, M.R.; Mekhilef, S.; Saidur, R. Progress and recent trends of wind energy technology. Renew. Sustain. Energy Rev. 2013, 21, 456–468. [CrossRef] 11. Procolombia Electric Power in Colombia. Power Generation—2015. Available online: http://www.energynet. co.uk/webfm_send/1839 (accessed on 9 January 2019). 12. Budes, F.B.; Escorcia, Y.C.; Ochoa, G.V. Optimization of a Biomass, solar and fuel cell Hybrid energy systems for a specific energy load using Homer Pro software®. Int. J. ChemTech Res. 2018, 11, 335–340. 13. Sikka, M.; Thornton, T.F.; Worl, R. Sustainable Biomass Energy and Indigenous Cultural Models of Well-being in an Alaska Forest Ecosystem. Ecol. Soc. 2013, 18, 531–543. [CrossRef] 14. Vides-Prado, A.; Camargo, E.O.; Vides-Prado, C.; Orozco, I.H.; Chenlo, F.; Candelo, J.E.; Sarmiento, A.B. Techno-economic feasibility analysis of photovoltaic systems in remote areas for indigenous communities in the Colombian Guajira. Renew. Sustain. Energy Rev. 2018, 82, 4245–4255. [CrossRef] 15. Mikati, M.; Santos, M.; Armenta, C. Modelado y Simulación de un Sistema Conjunto de Energía Solar y Eólica para Analizar su Dependencia de la Red Eléctrica. Rev. Iberoam. Autom. Inform. Ind. RIAI 2012, 9, 267–281. [CrossRef] 16. Bordons, C.; García-Torres, F.; Valverde, L. Gestión Óptima de la Energía en Microrredes con Generación Renovable. Rev. Iberoam. Autom. Inform. Ind. RIAI 2015, 12, 117–132. [CrossRef] 17. López, A.; Somolinos, J.A.; Núñez, L.R. Modelado Energético de Convertidores Primarios para el Aprovechamiento de las Energías Renovables Marinas. Rev. Iberoam. Autom. Inform. Ind. RIAI 2014, 11, 224–235. [CrossRef] 18. Esmaeili, S.; Shafiee, M. Simulation of Dynamic Response of Small Wind-Photovoltaic-Fuel Cell Hybrid Energy System. Smart Grid Renew. Energy 2012, 3, 194–203. [CrossRef] 19. Ochoa, G.V.; Blanco, C.; Martinez, C.; Ramos, E. Fuzzy Adaptive Control Applied to a Hybrid Electric-Power Generation System (HEPGS). Indian J. Sci. Technol 2017, 10, 1–9. [CrossRef] 20. De Dias, C.L.; Branco, D.A.C.; Arouca, M.C.; Legey, L.F.L. Performance estimation of photovoltaic technologies in Brazil. Renew. Energy 2017, 114, 367–375. [CrossRef] 21. Abbes, D.; Martinez, A.; Champenois, G. Life cycle cost, embodied energy and loss of power supply probability for the optimal design of hybrid power systems. Math. Comput. Simul. 2014, 98, 46–62. [CrossRef] 22. Parida, B.; Iniyan, S.; Goic, R. A review of solar photovoltaic technologies. Renew. Sustain. Energy Rev. 2011, 15, 1625–1636. [CrossRef] 23. Cancino-Solórzano, Y.; Xiberta-Bernat, J. Statistical analysis of wind power in the region of Veracruz (Mexico). Renew. Energy 2009, 34, 1628–1634. [CrossRef] 24. Observatorio del Caribe Colombiano. Región Caribe Colombiana 2015. Available online: http://www.ocaribe. org/region-caribe (accessed on 7 January 2019). 25. Pandiarajan, N.; Ramaprabha, R.; Muthu, R. Application of circuit model for photovoltaic energy conversion system. Int. J. Photoenergy 2012, 2012, 410401. [CrossRef] 26. ¸Sen, S.; Demirer, G.N. Anaerobic tratment of real textile wastewater with a fluidized bed reactor. Water Res. 2003, 37, 1868–1878. [CrossRef] 27. Padilla, R.V.; Demirkaya, G.; Goswami, D.Y.; Stefanakos, E.; Rahman, M.M. Heat transfer analysis of parabolic trough solar receiver. Appl. Energy 2011, 88, 5097–5110. [CrossRef] 28. Ochoa, G.V.; Chamorro, M.V.; Jiménez, J.P. Análisis Estadístico de la Velocidad y Dirección del Viento en la Región Caribe Colombiana con Énfasis en la Guajira; Universidad del Atlántico: Barranquilla, Colombia, 2016. 29. Bilil, H.; Aniba, G.; Maaroufi, M. Multiobjective Optimization of Renewable Energy Penetration Rate in Power Systems. Energy Procedia 2014, 50, 368–375. [CrossRef] 30. Kamjoo, A.; Maheri, A.; Dizqah, A.M.; Putrus, G.A. Multi-objective design under uncertainties of hybrid renewable energy system using NSGA-II and chance constrained programming. Int. J. Electr. Power Energy Syst. 2016, 74, 187–194. [CrossRef] 31. Ighravwe, D. A CRITIC-TOPSIS framework for hybrid renewable energy systems evaluation under techno-economic requirements. J. Proj. Manag. 2019, 4, 109–126. 32. Chen, S.J.; Hwang, C.L. Fuzzy Multiple Attribute Decision Making Methods. 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