Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/245169 |
Resumo: | The trend for renewable energies has motivated residential consumers around the world to have a rapid penetration in the installation of rooftop solar photovoltaic systems. For this reason, power utility companies must plan the inclusion of rooftop solar photovoltaic systems in their distribution grid. The proposed method projects the quantity and location of these systems. The method is divided into 3 modules: temporal, spatial, and potential modules. In the case of the temporal module, it uses census data by dividing the area into districts, and also, it calculates the number of residential customers, which can be converted into rooftop solar photovoltaic systems. On the other hand, the spatial module adjusts the temporal module based on the interaction and spatial influence of neighbours for each district. Finally, the potential module calculates their energy potential according to the geographical location of the districts and evaluates it with the forecast number of customers from the spatial module. The performance of the method is assessed in the service area of an Ecuadorian power utility. The results show that in Cuenca the greatest influence on adoption is given by two variables, the number of heads of households with permanent employment and the district's electrical power. The customers and energy results produced represent for each scenario only the 7% and 9% of the energy demanded, this concentration is shown through thematic maps that allow identifying the districts that have rapid adoption of solar panels. The results are important tools for the planning of the distribution company, the company will have the areas of highest rooftop solar photovoltaic systems penetration to evaluate its distribution system and maintain its reliability levels. |
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Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systemsspatial-temporallogistic growth modelgeographically weighted regressionsolar potentialsolar panelThe trend for renewable energies has motivated residential consumers around the world to have a rapid penetration in the installation of rooftop solar photovoltaic systems. For this reason, power utility companies must plan the inclusion of rooftop solar photovoltaic systems in their distribution grid. The proposed method projects the quantity and location of these systems. The method is divided into 3 modules: temporal, spatial, and potential modules. In the case of the temporal module, it uses census data by dividing the area into districts, and also, it calculates the number of residential customers, which can be converted into rooftop solar photovoltaic systems. On the other hand, the spatial module adjusts the temporal module based on the interaction and spatial influence of neighbours for each district. Finally, the potential module calculates their energy potential according to the geographical location of the districts and evaluates it with the forecast number of customers from the spatial module. The performance of the method is assessed in the service area of an Ecuadorian power utility. The results show that in Cuenca the greatest influence on adoption is given by two variables, the number of heads of households with permanent employment and the district's electrical power. The customers and energy results produced represent for each scenario only the 7% and 9% of the energy demanded, this concentration is shown through thematic maps that allow identifying the districts that have rapid adoption of solar panels. The results are important tools for the planning of the distribution company, the company will have the areas of highest rooftop solar photovoltaic systems penetration to evaluate its distribution system and maintain its reliability levels.Catholic Univ Cuenca, Acad Unit Posgrate Masters Program Renewable Ener, Cuenca 010101, EcuadorSao Paulo State Univ UNESP, Dept Elect Engn, BR-13506752 Sao Paulo, BrazilTech Univ Ambato, Fac Syst Elect & Ind Engn, Ambato 180104, EcuadorSao Paulo State Univ UNESP, Dept Elect Engn, BR-13506752 Sao Paulo, BrazilInt Journal Renewable Energy ResearchCatholic Univ CuencaUniversidade Estadual Paulista (UNESP)Tech Univ AmbatoMorales, R. E.Zambrano-Asanza, S. [UNESP]Rios, AlbertoGonzalez-Redrovan, J.2023-07-29T11:39:05Z2023-07-29T11:39:05Z2022-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1837-1845International Journal of Renewable Energy Research. Ankara: Int Journal Renewable Energy Research, v. 12, n. 4, p. 1837-1845, 2022.http://hdl.handle.net/11449/245169WOS:000904604700015Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Journal Of Renewable Energy Researchinfo:eu-repo/semantics/openAccess2023-07-29T11:39:05Zoai:repositorio.unesp.br:11449/245169Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:33:14.415483Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
title |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
spellingShingle |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems Morales, R. E. spatial-temporal logistic growth model geographically weighted regression solar potential solar panel |
title_short |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
title_full |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
title_fullStr |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
title_full_unstemmed |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
title_sort |
Spatio-Temporal Model to Estimate the Adoption of Rooftop Solar Photovoltaic Systems |
author |
Morales, R. E. |
author_facet |
Morales, R. E. Zambrano-Asanza, S. [UNESP] Rios, Alberto Gonzalez-Redrovan, J. |
author_role |
author |
author2 |
Zambrano-Asanza, S. [UNESP] Rios, Alberto Gonzalez-Redrovan, J. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Catholic Univ Cuenca Universidade Estadual Paulista (UNESP) Tech Univ Ambato |
dc.contributor.author.fl_str_mv |
Morales, R. E. Zambrano-Asanza, S. [UNESP] Rios, Alberto Gonzalez-Redrovan, J. |
dc.subject.por.fl_str_mv |
spatial-temporal logistic growth model geographically weighted regression solar potential solar panel |
topic |
spatial-temporal logistic growth model geographically weighted regression solar potential solar panel |
description |
The trend for renewable energies has motivated residential consumers around the world to have a rapid penetration in the installation of rooftop solar photovoltaic systems. For this reason, power utility companies must plan the inclusion of rooftop solar photovoltaic systems in their distribution grid. The proposed method projects the quantity and location of these systems. The method is divided into 3 modules: temporal, spatial, and potential modules. In the case of the temporal module, it uses census data by dividing the area into districts, and also, it calculates the number of residential customers, which can be converted into rooftop solar photovoltaic systems. On the other hand, the spatial module adjusts the temporal module based on the interaction and spatial influence of neighbours for each district. Finally, the potential module calculates their energy potential according to the geographical location of the districts and evaluates it with the forecast number of customers from the spatial module. The performance of the method is assessed in the service area of an Ecuadorian power utility. The results show that in Cuenca the greatest influence on adoption is given by two variables, the number of heads of households with permanent employment and the district's electrical power. The customers and energy results produced represent for each scenario only the 7% and 9% of the energy demanded, this concentration is shown through thematic maps that allow identifying the districts that have rapid adoption of solar panels. The results are important tools for the planning of the distribution company, the company will have the areas of highest rooftop solar photovoltaic systems penetration to evaluate its distribution system and maintain its reliability levels. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-12-01 2023-07-29T11:39:05Z 2023-07-29T11:39:05Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
International Journal of Renewable Energy Research. Ankara: Int Journal Renewable Energy Research, v. 12, n. 4, p. 1837-1845, 2022. http://hdl.handle.net/11449/245169 WOS:000904604700015 |
identifier_str_mv |
International Journal of Renewable Energy Research. Ankara: Int Journal Renewable Energy Research, v. 12, n. 4, p. 1837-1845, 2022. WOS:000904604700015 |
url |
http://hdl.handle.net/11449/245169 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Journal Of Renewable Energy Research |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1837-1845 |
dc.publisher.none.fl_str_mv |
Int Journal Renewable Energy Research |
publisher.none.fl_str_mv |
Int Journal Renewable Energy Research |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129085086367744 |