Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos
Ano de defesa: | 2017 |
---|---|
Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | , , |
Tipo de documento: | Tese |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Santa Maria
Centro de Tecnologia |
Programa de Pós-Graduação: |
Programa de Pós-Graduação em Engenharia Elétrica
|
Departamento: |
Engenharia Elétrica
|
País: |
Brasil
|
Palavras-chave em Português: | |
Palavras-chave em Inglês: | |
Área do conhecimento CNPq: | |
Link de acesso: | http://repositorio.ufsm.br/handle/1/13373 |
Resumo: | Accurate load modeling is a crucial task in distribution systems expansion planning. Traditionally, the load peak, which is viewed as the worst-case scenario, has been used to quantify new investment requirements. However, under the influence of the Distributed Generation, Demand Response, and Electric Vehicles, the load is becoming an Active Demand. In these conditions, the characteristics of the worst-case scenario may change as a result of the intermittent behavior of the renewable generation, the uncertainty in the consumers’ response to price signals as well as the uncertainties in electric vehicles charging. This thesis proposes new models for active low voltage consumers and electric vehicles on distribution systems expansion planning studies. In these models, the load uncertainty is considered by establishing different patterns for the behavior of LV consumers in the presence of DR programs. The load consumption is segmented according to the different uses of energy to stimulate behavioral adjustments based on the preferences and gains of different types of consumers. Electrical Vehicles charging is modeled considering different charging strategies in order to characterize different types of consumers. A case study based on the modified IEEE 33 Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of new Load Profiles (LPs) in scenarios with high penetration of renewable DG. Optimal 5-year expansion plans for AD quantiles were obtained using the metaheuristic EPSO (Evolutionary Particle Swarm Optimization) combined with nonlinear programming. Different incentive policies for AD are also analyzed to determine their impact on DS expansion planning. The experiments carried out reveal that considerable monetary savings in the DS can be achieved (up to 37%) as compared with the alternative with no AD by exploiting the flexibility associated with the active behavior of consumers and electric vehicles, by responding to price signals and by permitting adequate levels for the integration of DG into distribution grids. In addition, the results demonstrate that extreme scenarios of DR and/or DG penetration can result in investment expenditures greater than in the alternative with no AD, allowing to identify the best policy for DR and the optimal DG penetration level that result in the lowest investment cost. |
id |
UFSM_8f38b52cd5486566e547dc413c86b3f3 |
---|---|
oai_identifier_str |
oai:repositorio.ufsm.br:1/13373 |
network_acronym_str |
UFSM |
network_name_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
repository_id_str |
|
spelling |
2018-06-12T18:58:06Z2018-06-12T18:58:06Z2017-03-03http://repositorio.ufsm.br/handle/1/13373Accurate load modeling is a crucial task in distribution systems expansion planning. Traditionally, the load peak, which is viewed as the worst-case scenario, has been used to quantify new investment requirements. However, under the influence of the Distributed Generation, Demand Response, and Electric Vehicles, the load is becoming an Active Demand. In these conditions, the characteristics of the worst-case scenario may change as a result of the intermittent behavior of the renewable generation, the uncertainty in the consumers’ response to price signals as well as the uncertainties in electric vehicles charging. This thesis proposes new models for active low voltage consumers and electric vehicles on distribution systems expansion planning studies. In these models, the load uncertainty is considered by establishing different patterns for the behavior of LV consumers in the presence of DR programs. The load consumption is segmented according to the different uses of energy to stimulate behavioral adjustments based on the preferences and gains of different types of consumers. Electrical Vehicles charging is modeled considering different charging strategies in order to characterize different types of consumers. A case study based on the modified IEEE 33 Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of new Load Profiles (LPs) in scenarios with high penetration of renewable DG. Optimal 5-year expansion plans for AD quantiles were obtained using the metaheuristic EPSO (Evolutionary Particle Swarm Optimization) combined with nonlinear programming. Different incentive policies for AD are also analyzed to determine their impact on DS expansion planning. The experiments carried out reveal that considerable monetary savings in the DS can be achieved (up to 37%) as compared with the alternative with no AD by exploiting the flexibility associated with the active behavior of consumers and electric vehicles, by responding to price signals and by permitting adequate levels for the integration of DG into distribution grids. In addition, the results demonstrate that extreme scenarios of DR and/or DG penetration can result in investment expenditures greater than in the alternative with no AD, allowing to identify the best policy for DR and the optimal DG penetration level that result in the lowest investment cost.Modelar a carga de forma acurada é uma tarefa crucial para os estudos de Planejamento da Expansão de Sistemas de Distribuição (PESD). Tradicionalmente, o pico de demanda, visto na pior condição de carregamento, tem sido utilizado para quantificar os investimentos necessários para as redes de distribuição. Entretanto, sob a influência da Geração Distribuída (GD), Resposta da Demanda (RD) e Veículos Elétricos (VE), a carga está se tornando uma Demanda Ativa (DA). Nessas condições, as características de pior condição de carregamento podem mudar como consequência do comportamento intermitente da GD, da incerteza da resposta a sinais tarifários e também das incertezas relacionadas ao carregamento de VEs. Logo, essa tese propõe novos modelos de carga para consumidores de Baixa Tensão (BT) ativos e VEs para serem aplicados a estudos de PESD. A incerteza é considerada através do estabelecimento de diferentes padrões de comportamento de consumidores de BT na presença de programas de RD, GD e VEs. Consumidores são segmentados de acordo com níveis de consumo, possibilitando determinar ajustes ao modelo de carga conforme suas preferências. O carregamento de VEs é modelado através de diferentes estratégias de carregamento. Estudos de caso são realizados, baseados em um sistema IEEE 33 barras e em dados reais de uma distribuidora de energia. Planos ótimos de expansão num horizonte de 5 anos são obtidos através da meta-heurística EPSO (Otimização por Enxame de Partículas Evolutivo) combinada com programação não linear. Diferentes cenários de incentivos para integração de DA são analisados visando avaliar seus respectivos impactos no sistema de distribuição. Os estudos demonstram a possibilidade de reduzir e postergar gastos com expansão (até 37%) com a integração de DA caso a flexibilidade dos consumidores e de VEs sejam exploradas de forma a reduzir os impactos da demanda e da GD ao sistema. Além disso, resultados demonstraram que cenários de integração extrema, especial de GD, podem resultar em maiores investimentos. Assim, os modelos propostos permitem identificar as melhores políticas para integração de GD, RD e VEs.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESporUniversidade Federal de Santa MariaCentro de TecnologiaPrograma de Pós-Graduação em Engenharia ElétricaUFSMBrasilEngenharia ElétricaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessModelos de carga estocásticosGeração distribuídaResposta da demandaProcesso de poisson não homogêneoVeículos elétricosStochastic load modelingDistributed generationDemand responseActive demandNon-homogeneous poison processElectric vehiclesCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAMetodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricosMethodologies for load modeling of low voltage consumers considering the integration of demand response, distribution generation and electric vehiclesinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisAbaide, Alzenira da Rosahttp://lattes.cnpq.br/2427825596072142Bernardon, Daniel Pinheirohttp://lattes.cnpq.br/6004612278397270Barin, Alexandrehttp://lattes.cnpq.br/2477477379031706Pfitscher, Luciano Lopeshttp://lattes.cnpq.br/9139352677011006http://lattes.cnpq.br/8117456718259417Knak Neto, Nelson3004000000076006777278d-7b2f-4400-81f8-c04c5b371ad470db3314-9cc7-4cd9-b2da-66c60590f631d31b53a3-dd91-49c3-92da-1ef80629fe598def59c2-c7e6-4019-924d-2917b8a589cb612015bd-539d-49d3-b5ac-c6828145dfe2reponame:Biblioteca Digital de Teses e Dissertações do UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGEE_2017_KNAK NETO_NELSON.pdfTES_PPGEE_2017_KNAK NETO_NELSON.pdfTese de Doutoradoapplication/pdf8442003http://repositorio.ufsm.br/bitstream/1/13373/1/TES_PPGEE_2017_KNAK%20NETO_NELSON.pdf11705fcfe1e3086e2bfc978cb9ab9bf9MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8804http://repositorio.ufsm.br/bitstream/1/13373/2/license_rdfc1efe8e24d7281448e873be30ea326ffMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81956http://repositorio.ufsm.br/bitstream/1/13373/3/license.txt2f0571ecee68693bd5cd3f17c1e075dfMD53TEXTTES_PPGEE_2017_KNAK NETO_NELSON.pdf.txtTES_PPGEE_2017_KNAK NETO_NELSON.pdf.txtExtracted texttext/plain402921http://repositorio.ufsm.br/bitstream/1/13373/4/TES_PPGEE_2017_KNAK%20NETO_NELSON.pdf.txt14315f22d470e3f0aa885b8d38b8b026MD54THUMBNAILTES_PPGEE_2017_KNAK NETO_NELSON.pdf.jpgTES_PPGEE_2017_KNAK NETO_NELSON.pdf.jpgIM Thumbnailimage/jpeg5159http://repositorio.ufsm.br/bitstream/1/13373/5/TES_PPGEE_2017_KNAK%20NETO_NELSON.pdf.jpg0cda7117b67f7fabe3f2aec457f100e7MD551/133732018-06-12 15:58:07.092oai:repositorio.ufsm.br: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 Digital de Teses e Dissertaçõeshttps://repositorio.ufsm.br/ONGhttps://repositorio.ufsm.br/oai/requestatendimento.sib@ufsm.br||tedebc@gmail.comopendoar:2018-06-12T18:58:07Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM)false |
dc.title.por.fl_str_mv |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
dc.title.alternative.eng.fl_str_mv |
Methodologies for load modeling of low voltage consumers considering the integration of demand response, distribution generation and electric vehicles |
title |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
spellingShingle |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos Knak Neto, Nelson Modelos de carga estocásticos Geração distribuída Resposta da demanda Processo de poisson não homogêneo Veículos elétricos Stochastic load modeling Distributed generation Demand response Active demand Non-homogeneous poison process Electric vehicles CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
title_full |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
title_fullStr |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
title_full_unstemmed |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
title_sort |
Metodologias para modelagem de cargas de consumidores de baixa tensão considerando a integração de resposta da demanda, geração distribuída e veículos elétricos |
author |
Knak Neto, Nelson |
author_facet |
Knak Neto, Nelson |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Abaide, Alzenira da Rosa |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2427825596072142 |
dc.contributor.referee1.fl_str_mv |
Bernardon, Daniel Pinheiro |
dc.contributor.referee1Lattes.fl_str_mv |
http://lattes.cnpq.br/6004612278397270 |
dc.contributor.referee2.fl_str_mv |
Barin, Alexandre |
dc.contributor.referee2Lattes.fl_str_mv |
http://lattes.cnpq.br/2477477379031706 |
dc.contributor.referee3.fl_str_mv |
Pfitscher, Luciano Lopes |
dc.contributor.referee3Lattes.fl_str_mv |
http://lattes.cnpq.br/9139352677011006 |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8117456718259417 |
dc.contributor.author.fl_str_mv |
Knak Neto, Nelson |
contributor_str_mv |
Abaide, Alzenira da Rosa Bernardon, Daniel Pinheiro Barin, Alexandre Pfitscher, Luciano Lopes |
dc.subject.por.fl_str_mv |
Modelos de carga estocásticos Geração distribuída Resposta da demanda Processo de poisson não homogêneo Veículos elétricos |
topic |
Modelos de carga estocásticos Geração distribuída Resposta da demanda Processo de poisson não homogêneo Veículos elétricos Stochastic load modeling Distributed generation Demand response Active demand Non-homogeneous poison process Electric vehicles CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Stochastic load modeling Distributed generation Demand response Active demand Non-homogeneous poison process Electric vehicles |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
Accurate load modeling is a crucial task in distribution systems expansion planning. Traditionally, the load peak, which is viewed as the worst-case scenario, has been used to quantify new investment requirements. However, under the influence of the Distributed Generation, Demand Response, and Electric Vehicles, the load is becoming an Active Demand. In these conditions, the characteristics of the worst-case scenario may change as a result of the intermittent behavior of the renewable generation, the uncertainty in the consumers’ response to price signals as well as the uncertainties in electric vehicles charging. This thesis proposes new models for active low voltage consumers and electric vehicles on distribution systems expansion planning studies. In these models, the load uncertainty is considered by establishing different patterns for the behavior of LV consumers in the presence of DR programs. The load consumption is segmented according to the different uses of energy to stimulate behavioral adjustments based on the preferences and gains of different types of consumers. Electrical Vehicles charging is modeled considering different charging strategies in order to characterize different types of consumers. A case study based on the modified IEEE 33 Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of new Load Profiles (LPs) in scenarios with high penetration of renewable DG. Optimal 5-year expansion plans for AD quantiles were obtained using the metaheuristic EPSO (Evolutionary Particle Swarm Optimization) combined with nonlinear programming. Different incentive policies for AD are also analyzed to determine their impact on DS expansion planning. The experiments carried out reveal that considerable monetary savings in the DS can be achieved (up to 37%) as compared with the alternative with no AD by exploiting the flexibility associated with the active behavior of consumers and electric vehicles, by responding to price signals and by permitting adequate levels for the integration of DG into distribution grids. In addition, the results demonstrate that extreme scenarios of DR and/or DG penetration can result in investment expenditures greater than in the alternative with no AD, allowing to identify the best policy for DR and the optimal DG penetration level that result in the lowest investment cost. |
publishDate |
2017 |
dc.date.issued.fl_str_mv |
2017-03-03 |
dc.date.accessioned.fl_str_mv |
2018-06-12T18:58:06Z |
dc.date.available.fl_str_mv |
2018-06-12T18:58:06Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.ufsm.br/handle/1/13373 |
url |
http://repositorio.ufsm.br/handle/1/13373 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300400000007 |
dc.relation.confidence.fl_str_mv |
600 |
dc.relation.authority.fl_str_mv |
6777278d-7b2f-4400-81f8-c04c5b371ad4 70db3314-9cc7-4cd9-b2da-66c60590f631 d31b53a3-dd91-49c3-92da-1ef80629fe59 8def59c2-c7e6-4019-924d-2917b8a589cb 612015bd-539d-49d3-b5ac-c6828145dfe2 |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Engenharia Elétrica |
dc.publisher.initials.fl_str_mv |
UFSM |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Santa Maria Centro de Tecnologia |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações do UFSM instname:Universidade Federal de Santa Maria (UFSM) instacron:UFSM |
instname_str |
Universidade Federal de Santa Maria (UFSM) |
instacron_str |
UFSM |
institution |
UFSM |
reponame_str |
Biblioteca Digital de Teses e Dissertações do UFSM |
collection |
Biblioteca Digital de Teses e Dissertações do UFSM |
bitstream.url.fl_str_mv |
http://repositorio.ufsm.br/bitstream/1/13373/1/TES_PPGEE_2017_KNAK%20NETO_NELSON.pdf http://repositorio.ufsm.br/bitstream/1/13373/2/license_rdf http://repositorio.ufsm.br/bitstream/1/13373/3/license.txt http://repositorio.ufsm.br/bitstream/1/13373/4/TES_PPGEE_2017_KNAK%20NETO_NELSON.pdf.txt http://repositorio.ufsm.br/bitstream/1/13373/5/TES_PPGEE_2017_KNAK%20NETO_NELSON.pdf.jpg |
bitstream.checksum.fl_str_mv |
11705fcfe1e3086e2bfc978cb9ab9bf9 c1efe8e24d7281448e873be30ea326ff 2f0571ecee68693bd5cd3f17c1e075df 14315f22d470e3f0aa885b8d38b8b026 0cda7117b67f7fabe3f2aec457f100e7 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações do UFSM - Universidade Federal de Santa Maria (UFSM) |
repository.mail.fl_str_mv |
atendimento.sib@ufsm.br||tedebc@gmail.com |
_version_ |
1793240146683887616 |