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

Detalhes bibliográficos
Autor(a) principal: Tiem,Wesley Thiago Egea
Data de Publicação: 2018
Outros Autores: Unsihuay-Vila,Clodomiro
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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spelling 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
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