Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200215 |
Resumo: | Abstract The electrical sector is under constant evolution. One of the areas refers to the consumers that come to be generators, implementing distributed generation, interconnected to a smart grid. This article discusses the improvement of an algorithm, already presented in the literature, to make the best temporal allocation of loads, electric vehicle, storage and many sources of generation, aiming at the maximum financial performance, that is, the lowest value for the energy invoice The modeling consists of a Mixed Integer Linear Programming (MILP) algorithm, which considers each component of the system and weighs the maintenance and shelf life of storage devices, basically batteries, loads that can be reallocated and the concept of Vehicle-to-grid, performing a daily analysis. The simulation has considered the hypothetical case of a residence, in which are included storage, electric vehicle and redistribution of loads, as well as wind and solar generation. Several scenarios are simulated, with or without the presence of some of the components. The results indicate that the simplest model, only redistributing the loads, can provide a sensible monetary savings of approximately 60%, while with the application of all the components modeled, there can be a reduction in the invoice of 90%. |
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Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro NetworkMicrogridmicrogenerationdistributed generationwind energysolar energyload reallocationstorageelectric vehiclesmart gridAbstract The electrical sector is under constant evolution. One of the areas refers to the consumers that come to be generators, implementing distributed generation, interconnected to a smart grid. This article discusses the improvement of an algorithm, already presented in the literature, to make the best temporal allocation of loads, electric vehicle, storage and many sources of generation, aiming at the maximum financial performance, that is, the lowest value for the energy invoice The modeling consists of a Mixed Integer Linear Programming (MILP) algorithm, which considers each component of the system and weighs the maintenance and shelf life of storage devices, basically batteries, loads that can be reallocated and the concept of Vehicle-to-grid, performing a daily analysis. The simulation has considered the hypothetical case of a residence, in which are included storage, electric vehicle and redistribution of loads, as well as wind and solar generation. Several scenarios are simulated, with or without the presence of some of the components. The results indicate that the simplest model, only redistributing the loads, can provide a sensible monetary savings of approximately 60%, while with the application of all the components modeled, there can be a reduction in the invoice of 90%.Instituto de Tecnologia do Paraná - Tecpar2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200215Brazilian Archives of Biology and Technology v.61 n.spe 2018reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-smart-2018000030info:eu-repo/semantics/openAccessTiem,Wesley Thiago EgeaUnsihuay-Vila,Clodomiroeng2018-10-25T00:00:00Zoai:scielo:S1516-89132018000200215Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2018-10-25T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
title |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
spellingShingle |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network Tiem,Wesley Thiago Egea Microgrid microgeneration distributed generation wind energy solar energy load reallocation storage electric vehicle smart grid |
title_short |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
title_full |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
title_fullStr |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
title_full_unstemmed |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
title_sort |
Computational Model for Microgeneration Simulation, From Solar and Wind Renewable Sources, With Optimal Allocation of Loads, Electric Vehicle and Energy Storage, In a Residential Electrical Micro Network |
author |
Tiem,Wesley Thiago Egea |
author_facet |
Tiem,Wesley Thiago Egea Unsihuay-Vila,Clodomiro |
author_role |
author |
author2 |
Unsihuay-Vila,Clodomiro |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Tiem,Wesley Thiago Egea Unsihuay-Vila,Clodomiro |
dc.subject.por.fl_str_mv |
Microgrid microgeneration distributed generation wind energy solar energy load reallocation storage electric vehicle smart grid |
topic |
Microgrid microgeneration distributed generation wind energy solar energy load reallocation storage electric vehicle smart grid |
description |
Abstract The electrical sector is under constant evolution. One of the areas refers to the consumers that come to be generators, implementing distributed generation, interconnected to a smart grid. This article discusses the improvement of an algorithm, already presented in the literature, to make the best temporal allocation of loads, electric vehicle, storage and many sources of generation, aiming at the maximum financial performance, that is, the lowest value for the energy invoice The modeling consists of a Mixed Integer Linear Programming (MILP) algorithm, which considers each component of the system and weighs the maintenance and shelf life of storage devices, basically batteries, loads that can be reallocated and the concept of Vehicle-to-grid, performing a daily analysis. The simulation has considered the hypothetical case of a residence, in which are included storage, electric vehicle and redistribution of loads, as well as wind and solar generation. Several scenarios are simulated, with or without the presence of some of the components. The results indicate that the simplest model, only redistributing the loads, can provide a sensible monetary savings of approximately 60%, while with the application of all the components modeled, there can be a reduction in the invoice of 90%. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200215 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132018000200215 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-smart-2018000030 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.61 n.spe 2018 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318278766493696 |