Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Repositório Institucional Manancial UFSM |
Texto Completo: | http://repositorio.ufsm.br/handle/1/29162 |
Resumo: | The increase in distributed generation (DG) participation is a reality, influencing the way it generates, distributes and consumes electricity. Virtual Power Plant (VPP) will play an important role in integrating decentralized power generation systems with markets. However, the scheduling of distributed energy resources present in the VPP are important issues and require commitment forecasts from participating units. Predicting production or demand profiles is not a trivial task, as they rely heavily on weather characteristics, and the predictability of consumer demand is inherently variable. The development of methodologies and tools to meet these future uncertainties in different scenarios is relevant to the interests in the electricity markets. Probability forecasts are growing as a tool for managing variability. In this context, this thesis proposes a methodology for forecasting generation dispatches and individual user demands by modeling stochastic uncertainty using Unscented Transform (UT) in a VPP. These forecasts using UT were more satisfactory for more assertive decision making and better estimates for likely scenarios, minimizing decision risks for VPP aggregators, thus reducing their uncertainty about operations. The results validate the efficiency of the proposed technique using data and simulations. The UT, when inserted in this scenario, presents a good performance from a technical point of view in a VPP as evidenced in this thesis. Knowledge of the predictability and uncertainties that UT provides in a VPP will leverage the assertiveness of electrical scenarios, optimize economic results, leverage smart statistics, and improve electrical system planning. |
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2023-05-23T13:11:37Z2023-05-23T13:11:37Z2019-12-19http://repositorio.ufsm.br/handle/1/29162The increase in distributed generation (DG) participation is a reality, influencing the way it generates, distributes and consumes electricity. Virtual Power Plant (VPP) will play an important role in integrating decentralized power generation systems with markets. However, the scheduling of distributed energy resources present in the VPP are important issues and require commitment forecasts from participating units. Predicting production or demand profiles is not a trivial task, as they rely heavily on weather characteristics, and the predictability of consumer demand is inherently variable. The development of methodologies and tools to meet these future uncertainties in different scenarios is relevant to the interests in the electricity markets. Probability forecasts are growing as a tool for managing variability. In this context, this thesis proposes a methodology for forecasting generation dispatches and individual user demands by modeling stochastic uncertainty using Unscented Transform (UT) in a VPP. These forecasts using UT were more satisfactory for more assertive decision making and better estimates for likely scenarios, minimizing decision risks for VPP aggregators, thus reducing their uncertainty about operations. The results validate the efficiency of the proposed technique using data and simulations. The UT, when inserted in this scenario, presents a good performance from a technical point of view in a VPP as evidenced in this thesis. Knowledge of the predictability and uncertainties that UT provides in a VPP will leverage the assertiveness of electrical scenarios, optimize economic results, leverage smart statistics, and improve electrical system planning.O aumento das participações das gerações distribuídas (GD) é uma realidade, influenciando a maneira com que se gera, distribui e consome energia elétrica no setor elétrico. A Usina de Energia Virtual (Virtual Power Plant - VPP) desempenhará um papel importante na integração dos sistemas de gerações de energia descentralizados com os mercados. Porém, os agendamentos dos recursos energéticos distribuídos presentes na VPP são questões importantes e necessitam de previsões de compromissos das unidades participantes. Prever os perfis das produções ou demandas não é uma tarefa trivial, pois dependem fortemente de características meteorológicas, além disso, a previsibilidade da demanda do consumidor é inerentemente variável. O desenvolvimento de metodologias e ferramentas para conhecer essas incertezas futuras em cenários distintos, é relevante para as participações nos mercados de eletricidade. As previsões probabilísticas mostram-se em crescimento, como uma ferramenta para gerenciar a variabilidade. Nesse contexto, esta tese propões uma metodologia para as previsões de despachos das gerações e das demandas de usuários individuais, através da modelagem da incerteza estocástica utilizando Transformadas de Incertezas (Unscented Transform - UT) em uma VPP. Essas previsões utilizando a UT mostraram-se mais satisfatórias para tomadas de decisões mais assertivas e obtenção de melhores estimativas para prováveis cenários, minimizando os riscos de decisões para os agregadores das VPPs, reduzindo assim suas incertezas quanto às operações. Os resultados validam a eficiência da técnica proposta utilizado dados e simulações. A UT quando inserida nesse cenário apresenta bom desempenho do ponto de vista técnico em uma VPP como evidenciado nessa tese. Os conhecimentos das previsibilidades e incertezas que a UT fornece em um VPP poderão potencializar assertividade de cenários elétricos, otimizar resultados econômicos, alavancar estatísticas inteligentes e aprimorar o planejamento do sistema elétrico.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/openAccessTransformada de incertezaUsina de energia virtualPrevisãoRedes inteligentesRecursos energéticos distribuídosUnscented transformVirtual power plantForecastSmart gridDistributed energy resourcesCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAModelagem estocástica em usinas virtuais de energia utilizando transformada de incertezaStochastic modeling in virtual power plant using unscented transforminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisCanha, Luciane Neveshttp://lattes.cnpq.br/6991878627141193Menezes, Leonardo Rodrigues Araujo Xavier deCarvalho, Ricardo Siqueira deRangel, Camilo Alberto SepúlvedaMilbradt, Rafael Gresslerhttp://lattes.cnpq.br/4278790639558306Ramos, Lucas Feksa300400000007600600600600600600600ab53fdc5-93b0-417d-b96d-9442b235a1ff64a17773-1e7b-4b47-abff-403f936ac820ae28873d-89ff-498f-84e8-fe02bbd09c13df55c4a5-de93-44fc-8f00-3dd0370ab20dd94d6681-58b8-4d10-beab-53ae0ebcb4bc2cc2b5e3-9010-4030-8561-821a5ce6acbcreponame:Repositório Institucional Manancial UFSMinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSMORIGINALTES_PPGEE_2019_RAMOS_LUCAS.pdfTES_PPGEE_2019_RAMOS_LUCAS.pdfTeseapplication/pdf40274667http://repositorio.ufsm.br/bitstream/1/29162/1/TES_PPGEE_2019_RAMOS_LUCAS.pdf019ba61c3bf68dae29c775b925dd7695MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.por.fl_str_mv |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
dc.title.alternative.eng.fl_str_mv |
Stochastic modeling in virtual power plant using unscented transform |
title |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
spellingShingle |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza Ramos, Lucas Feksa Transformada de incerteza Usina de energia virtual Previsão Redes inteligentes Recursos energéticos distribuídos Unscented transform Virtual power plant Forecast Smart grid Distributed energy resources CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
title_full |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
title_fullStr |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
title_full_unstemmed |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
title_sort |
Modelagem estocástica em usinas virtuais de energia utilizando transformada de incerteza |
author |
Ramos, Lucas Feksa |
author_facet |
Ramos, Lucas Feksa |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Canha, Luciane Neves |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/6991878627141193 |
dc.contributor.referee1.fl_str_mv |
Menezes, Leonardo Rodrigues Araujo Xavier de |
dc.contributor.referee2.fl_str_mv |
Carvalho, Ricardo Siqueira de |
dc.contributor.referee3.fl_str_mv |
Rangel, Camilo Alberto Sepúlveda |
dc.contributor.referee4.fl_str_mv |
Milbradt, Rafael Gressler |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/4278790639558306 |
dc.contributor.author.fl_str_mv |
Ramos, Lucas Feksa |
contributor_str_mv |
Canha, Luciane Neves Menezes, Leonardo Rodrigues Araujo Xavier de Carvalho, Ricardo Siqueira de Rangel, Camilo Alberto Sepúlveda Milbradt, Rafael Gressler |
dc.subject.por.fl_str_mv |
Transformada de incerteza Usina de energia virtual Previsão Redes inteligentes Recursos energéticos distribuídos |
topic |
Transformada de incerteza Usina de energia virtual Previsão Redes inteligentes Recursos energéticos distribuídos Unscented transform Virtual power plant Forecast Smart grid Distributed energy resources CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
dc.subject.eng.fl_str_mv |
Unscented transform Virtual power plant Forecast Smart grid Distributed energy resources |
dc.subject.cnpq.fl_str_mv |
CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
The increase in distributed generation (DG) participation is a reality, influencing the way it generates, distributes and consumes electricity. Virtual Power Plant (VPP) will play an important role in integrating decentralized power generation systems with markets. However, the scheduling of distributed energy resources present in the VPP are important issues and require commitment forecasts from participating units. Predicting production or demand profiles is not a trivial task, as they rely heavily on weather characteristics, and the predictability of consumer demand is inherently variable. The development of methodologies and tools to meet these future uncertainties in different scenarios is relevant to the interests in the electricity markets. Probability forecasts are growing as a tool for managing variability. In this context, this thesis proposes a methodology for forecasting generation dispatches and individual user demands by modeling stochastic uncertainty using Unscented Transform (UT) in a VPP. These forecasts using UT were more satisfactory for more assertive decision making and better estimates for likely scenarios, minimizing decision risks for VPP aggregators, thus reducing their uncertainty about operations. The results validate the efficiency of the proposed technique using data and simulations. The UT, when inserted in this scenario, presents a good performance from a technical point of view in a VPP as evidenced in this thesis. Knowledge of the predictability and uncertainties that UT provides in a VPP will leverage the assertiveness of electrical scenarios, optimize economic results, leverage smart statistics, and improve electrical system planning. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019-12-19 |
dc.date.accessioned.fl_str_mv |
2023-05-23T13:11:37Z |
dc.date.available.fl_str_mv |
2023-05-23T13:11:37Z |
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/1/29162 |
url |
http://repositorio.ufsm.br/handle/1/29162 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.cnpq.fl_str_mv |
300400000007 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 600 600 600 |
dc.relation.authority.fl_str_mv |
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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 |
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