Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming
Renewable energy sources have shown remarkable growth in recent times in terms of their contribution to sustainable societies. However, integrating them into the national power grids is usually hindered because of their weather-dependent nature and variability. The combination of different sources t...
- Autores:
-
Canales, Fausto
Jurasz, Jakub
Kies, Alexander
Arrieta-Castro, Marco
Peralta-Cayón, Andrés
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6467
- Acceso en línea:
- https://hdl.handle.net/11323/6467
https://doi.org/10.1016/j.mex.2020.100871
https://repositorio.cuc.edu.co/
- Palabra clave:
- Energetic complementarity
Renewable energy
Variable renewables
Geographic information systems
Complementariedad energética
Energía renovable
Renovables variables
Sistemas de Información Geográfica
- Rights
- openAccess
- License
- CC0 1.0 Universal
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| dc.title.spa.fl_str_mv |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| dc.title.translated.spa.fl_str_mv |
Representación espacial de temporal complementariedad entre tres energías variables fuentes que utilizan coeficientes de correlación y calendario de compromiso |
| title |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| spellingShingle |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming Energetic complementarity Renewable energy Variable renewables Geographic information systems Complementariedad energética Energía renovable Renovables variables Sistemas de Información Geográfica |
| title_short |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| title_full |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| title_fullStr |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| title_full_unstemmed |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| title_sort |
Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming |
| dc.creator.fl_str_mv |
Canales, Fausto Jurasz, Jakub Kies, Alexander Arrieta-Castro, Marco Peralta-Cayón, Andrés |
| dc.contributor.author.spa.fl_str_mv |
Canales, Fausto Jurasz, Jakub Kies, Alexander Arrieta-Castro, Marco Peralta-Cayón, Andrés |
| dc.subject.spa.fl_str_mv |
Energetic complementarity Renewable energy Variable renewables Geographic information systems Complementariedad energética Energía renovable Renovables variables Sistemas de Información Geográfica |
| topic |
Energetic complementarity Renewable energy Variable renewables Geographic information systems Complementariedad energética Energía renovable Renovables variables Sistemas de Información Geográfica |
| description |
Renewable energy sources have shown remarkable growth in recent times in terms of their contribution to sustainable societies. However, integrating them into the national power grids is usually hindered because of their weather-dependent nature and variability. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. Thus, efficient planning for optimizing the exploitation of these energy resources requires different types of decision support tools. A mathematical index for assessing energetic complementarity between multiple energy sources constitutes an important tool for this purpose, allowing a comparison of complementarity between existing facilities at different planning stages and also allowing a dynamic assessment of complementarity between variable energy sources throughout the operation, assisting in the dispatch of power supplies. This article presents a method for quantifying and spatially representing the total temporal energetic complementarity between three different variable renewable sources, through an index created from correlation coefficients and compromise programming. The method is employed to study the complementarity of wind speed, solar radiation and surface runoff on a monthly scale using continental Colombia as a case study during the year of 2015. This paper describes a method for quantifying and spatially representing energetic complementarity between three renewable energy sources. The method quantifies energetic complementarity by combining known metrics: correlations and compromise programming. The proposed index for energetic complementarity assessment is sensitive to the time scale adopted. |
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2020 |
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2020-07-06T20:18:01Z |
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2020-07-06T20:18:01Z |
| dc.date.issued.none.fl_str_mv |
2020-03-11 |
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Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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http://purl.org/coar/resource_type/c_6501 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/acceptedVersion |
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22150161 |
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https://hdl.handle.net/11323/6467 |
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https://doi.org/10.1016/j.mex.2020.100871 |
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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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| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.references.spa.fl_str_mv |
[1] F.A. Canales, J. Jurasz, A. Beluco, A. Kies, Assessing temporal complementarity between three variable energy sources through correlation and compromise programming, Energy 192 (2020) 116637, doi:10.1016/j.energy.2019.116637. [2] F.A. Canales, J. Jurasz, A. Kies, A. Beluco, M. Arrieta-castro, A. Peralta-cayón, Temporal complementarity between three variable renewable energy resources: A spatial representation, in: 11th Int. Conf. Appl. Energy, Applied Energy, Västerås, Sweden, 2019, pp. 1–6. http://www.energy-proceedings.org/temporal-complementarity-between-three-variablerenewable-energy-sources-a-spatial-representation/. [3] A. Beluco, P.K. de Souza, A. Krenzinger, A dimensionless index evaluating the time complementarity between solar and hydraulic energies, Renew. Energy 33 (2008) 2157–2165 https://doi.org/10.1016/j.renene.2008.01.019. [4] A. Beluco, A. Risso, F.A. Canales, Simplified evaluation of energetic complementarity based on monthly average data, MethodsX 6 (2019) 1194–1198 https://doi.org/10.1016/j.mex.2019.05.019 [5] E.M. Borba, R.M. Brito, An index assessing the energetic complementarity in time between more than two energy resources, Energy Power Eng 09 (2017) 505–514 https://doi.org/10.4236/epe.2017.99035 [6] S. Han, L. Zhang, Y. Liu, H. Zhang, J. Yan, L. Li, X. Lei, X. Wang, Quantitative evaluation method for the complementarity of wind–solar–hydro power and optimization of wind–solar ratio, Appl. Energy 236 (2019) 973–984 https://doi.org/10.1016/j. apenergy.2018.12.059. [7] I. Kougias, S. Szabó, F. Monforti-Ferrario, T. Huld, K. Bódis, A methodology for optimization of the complementarity between small-hydropower plants and solar pv systems, Renew. Energy 87 (2016) 1023–1030 https://doi.org/10.1016/j.renene.2015. 09.073. [8] J. Jurasz, A. Beluco, F.A. Canales, The impact of complementarity on power supply reliability of small scale hybrid energy systems, Energy 161 (2018) 737–743 https://doi.org/10.1016/j.energy.2018.07.182 [9] A.R. Silva, F.M. Pimenta, A.T. Assireu, M.H.C. Spyrides, Complementarity of Brazil ’ s hydro and offshore wind power, Renew. Sustain. Energy Rev. 56 (2016) 413–427 https://doi.org/10.1016/j.rser.2015.11.045. [10] M.A. Vega-Sánchez, P.D. Castañeda-Jiménez, R. Peña-Gallardo, A. Ruiz-Alonso, J.A. Morales-Saldaña, E.R. Palacios-hernández, Evaluation of complementarity of wind and solar energy resources over Mexico using an image processing approach, in: IEEE Int. Autumn Meet. Power, Electron. Comput., IEEE, Ixtapa, Mexico Evaluation, 2017, pp. 1–5 [11] M.P. Cantão, M.R. Bessa, R. Bettega, D.H.M. Detzel, J.M. Lima, Evaluation of hydro-wind complementarity in the Brazilian territory by means of correlation maps, Renew. Energy 101 (2017) 1215–1225 https://doi.org/10.1016/j.renene.2016.10.012. [12] A. Risso, A. Beluco, R.de C.M. Alves, Complementarity roses evaluating spatial complementarity in time between energy resources, Energies 11 (2018) 1–14 https://doi.org/10.3390/en11071918. [13] A. Risso, A. Beluco, R.de C.M. Alves, Qualitative evaluation of spatial complementarity between renewable energy resources with complementarity roses, MethodsX 6 (2019) 800–804 https://doi.org/10.1016/j.mex.2019.04.005. [14] M. Gershon, L. Duckstein, Multiobjective approaches to river basin planning, J. Water Resour. Plan. Manag. 109 (1983) 13–28 https://doi.org/10.1061/(ASCE)0733-9496(1983)109:1(13). [15] Copernicus Climate Change Service (C3S), ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), 2017 https://cds.climate.copernicus.eu/ (accessed April 28, 2019). |
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Canales, FaustoJurasz, JakubKies, AlexanderArrieta-Castro, MarcoPeralta-Cayón, Andrés2020-07-06T20:18:01Z2020-07-06T20:18:01Z2020-03-1122150161https://hdl.handle.net/11323/6467https://doi.org/10.1016/j.mex.2020.100871Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Renewable energy sources have shown remarkable growth in recent times in terms of their contribution to sustainable societies. However, integrating them into the national power grids is usually hindered because of their weather-dependent nature and variability. The combination of different sources to profit from their beneficial complementarity has often been proposed as a partial solution to overcome these issues. Thus, efficient planning for optimizing the exploitation of these energy resources requires different types of decision support tools. A mathematical index for assessing energetic complementarity between multiple energy sources constitutes an important tool for this purpose, allowing a comparison of complementarity between existing facilities at different planning stages and also allowing a dynamic assessment of complementarity between variable energy sources throughout the operation, assisting in the dispatch of power supplies. This article presents a method for quantifying and spatially representing the total temporal energetic complementarity between three different variable renewable sources, through an index created from correlation coefficients and compromise programming. The method is employed to study the complementarity of wind speed, solar radiation and surface runoff on a monthly scale using continental Colombia as a case study during the year of 2015. This paper describes a method for quantifying and spatially representing energetic complementarity between three renewable energy sources. The method quantifies energetic complementarity by combining known metrics: correlations and compromise programming. The proposed index for energetic complementarity assessment is sensitive to the time scale adopted.Las fuentes de energía renovable han mostrado un crecimiento notable en los últimos tiempos en términos de su contribución a sociedades sostenibles. Sin embargo, su integración en las redes eléctricas nacionales generalmente se ve obstaculizada debido a su naturaleza y variabilidad dependientes del clima. La combinación de diferentes fuentes para beneficiarse de su complementariedad beneficiosa a menudo se ha propuesto como una solución parcial para superar estos problemas. Por lo tanto, la planificación eficiente para optimizar la explotación de estos recursos energéticos requiere diferentes tipos de herramientas de apoyo a la decisión. Un índice matemático para evaluar la complementariedad energética entre múltiples fuentes de energía constituye una herramienta importante para este propósito, lo que permite una comparación de la complementariedad entre las instalaciones existentes en diferentes etapas de planificación y también permite una evaluación dinámica de la complementariedad entre las fuentes de energía variables a lo largo de la operación, ayudando en la despacho de suministros de energía. Este artículo presenta un método para cuantificar y representar espacialmente la complementariedad energética temporal total entre tres fuentes renovables variables diferentes, a través de un índice creado a partir de coeficientes de correlación y programación de compromiso. El método se emplea para estudiar la complementariedad de la velocidad del viento, la radiación solar y la escorrentía superficial en una escala mensual utilizando Colombia continental como un estudio de caso durante el año 2015.Canales, Fausto-will be generated-orcid-0000-0002-6858-1855-600Jurasz, Jakub-will be generated-orcid-0000-0001-9576-7877-600Kies, Alexander-will be generated-orcid-0000-0002-0300-8142-600Arrieta-Castro, MarcoPeralta-Cayón, AndrésengMethodsXCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Energetic complementarityRenewable energyVariable renewablesGeographic information systemsComplementariedad energéticaEnergía renovableRenovables variablesSistemas de Información GeográficaSpatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programmingRepresentación espacial de temporal complementariedad entre tres energías variables fuentes que utilizan coeficientes de correlación y calendario de compromisoArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] F.A. Canales, J. Jurasz, A. Beluco, A. Kies, Assessing temporal complementarity between three variable energy sources through correlation and compromise programming, Energy 192 (2020) 116637, doi:10.1016/j.energy.2019.116637.[2] F.A. Canales, J. Jurasz, A. Kies, A. Beluco, M. Arrieta-castro, A. Peralta-cayón, Temporal complementarity between three variable renewable energy resources: A spatial representation, in: 11th Int. Conf. Appl. Energy, Applied Energy, Västerås, Sweden, 2019, pp. 1–6. http://www.energy-proceedings.org/temporal-complementarity-between-three-variablerenewable-energy-sources-a-spatial-representation/.[3] A. Beluco, P.K. de Souza, A. Krenzinger, A dimensionless index evaluating the time complementarity between solar and hydraulic energies, Renew. Energy 33 (2008) 2157–2165 https://doi.org/10.1016/j.renene.2008.01.019.[4] A. Beluco, A. Risso, F.A. Canales, Simplified evaluation of energetic complementarity based on monthly average data, MethodsX 6 (2019) 1194–1198 https://doi.org/10.1016/j.mex.2019.05.019[5] E.M. Borba, R.M. Brito, An index assessing the energetic complementarity in time between more than two energy resources, Energy Power Eng 09 (2017) 505–514 https://doi.org/10.4236/epe.2017.99035[6] S. Han, L. Zhang, Y. Liu, H. Zhang, J. Yan, L. Li, X. Lei, X. Wang, Quantitative evaluation method for the complementarity of wind–solar–hydro power and optimization of wind–solar ratio, Appl. Energy 236 (2019) 973–984 https://doi.org/10.1016/j. apenergy.2018.12.059.[7] I. Kougias, S. Szabó, F. Monforti-Ferrario, T. Huld, K. Bódis, A methodology for optimization of the complementarity between small-hydropower plants and solar pv systems, Renew. Energy 87 (2016) 1023–1030 https://doi.org/10.1016/j.renene.2015. 09.073.[8] J. Jurasz, A. Beluco, F.A. Canales, The impact of complementarity on power supply reliability of small scale hybrid energy systems, Energy 161 (2018) 737–743 https://doi.org/10.1016/j.energy.2018.07.182[9] A.R. Silva, F.M. Pimenta, A.T. Assireu, M.H.C. Spyrides, Complementarity of Brazil ’ s hydro and offshore wind power, Renew. Sustain. Energy Rev. 56 (2016) 413–427 https://doi.org/10.1016/j.rser.2015.11.045.[10] M.A. Vega-Sánchez, P.D. Castañeda-Jiménez, R. Peña-Gallardo, A. Ruiz-Alonso, J.A. Morales-Saldaña, E.R. Palacios-hernández, Evaluation of complementarity of wind and solar energy resources over Mexico using an image processing approach, in: IEEE Int. Autumn Meet. Power, Electron. Comput., IEEE, Ixtapa, Mexico Evaluation, 2017, pp. 1–5[11] M.P. Cantão, M.R. Bessa, R. Bettega, D.H.M. Detzel, J.M. Lima, Evaluation of hydro-wind complementarity in the Brazilian territory by means of correlation maps, Renew. Energy 101 (2017) 1215–1225 https://doi.org/10.1016/j.renene.2016.10.012.[12] A. Risso, A. Beluco, R.de C.M. Alves, Complementarity roses evaluating spatial complementarity in time between energy resources, Energies 11 (2018) 1–14 https://doi.org/10.3390/en11071918.[13] A. Risso, A. Beluco, R.de C.M. Alves, Qualitative evaluation of spatial complementarity between renewable energy resources with complementarity roses, MethodsX 6 (2019) 800–804 https://doi.org/10.1016/j.mex.2019.04.005.[14] M. Gershon, L. Duckstein, Multiobjective approaches to river basin planning, J. Water Resour. Plan. Manag. 109 (1983) 13–28 https://doi.org/10.1061/(ASCE)0733-9496(1983)109:1(13).[15] Copernicus Climate Change Service (C3S), ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate, Copernicus Climate Change Service Climate Data Store (CDS), 2017 https://cds.climate.copernicus.eu/ (accessed April 28, 2019).PublicationORIGINALCanales (2020) Spatial representation of temporal complementarity.pdfCanales (2020) Spatial representation of temporal complementarity.pdfapplication/pdf1360012https://repositorio.cuc.edu.co/bitstreams/9d556e74-cde1-4287-b901-9628f5ba9a00/downloadab60a40b0175c13bbdd3107e5b49dd2eMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/79e85a90-3742-4188-af66-1897b46d92bc/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; 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