Clustering methods to find representative days for modelling the Portuguese electricity system

Detalhes bibliográficos
Autor(a) principal: Palmeiro, João Vasco da Silva
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/131205
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Clustering methods to find representative days for modelling the Portuguese electricity systemClusteringEnergy EconomicsPower System ModellingRenewable Energy SourcesRepresentative daysTime SeriesDissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligencePower system modelling affects decisions on over $450 billion worth of assets world-wide each year. While complex and computationally demanding models, when properly simplified a balance between accuracy and simulation time can be achieved. Solutions and results for this thoroughly studied problem tend to be rather case-specific, and the Portuguese system presents challenges that make existing approaches insufficient. To better understand this system and how its peculiarities can be used to reduce its modelling complexity, a model of the Portuguese electricity system using PLEXOS software was developed and used to test the impact of different clustering techniques on the model’s output results. We show that including natural hydro inflow in the clustering to find representative days for a system where hydro generation plays such a large role can improve model output accuracy. This is typically ignored in the literature. Additionally, we demonstrate that using data disregarding daylight saving time changes can have an impact on results. Finally, we indicate that intraday downsampling might have limited effect on modelling accuracy, and open the way for future work on weighting clustering input dimensions differently to improve accuracy of representative days.Scott, Ian JamesRUNPalmeiro, João Vasco da Silva2022-01-20T17:24:41Z2021-11-022021-11-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/131205TID:202815404enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:09:46Zoai:run.unl.pt:10362/131205Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:47:00.876625Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Clustering methods to find representative days for modelling the Portuguese electricity system
title Clustering methods to find representative days for modelling the Portuguese electricity system
spellingShingle Clustering methods to find representative days for modelling the Portuguese electricity system
Palmeiro, João Vasco da Silva
Clustering
Energy Economics
Power System Modelling
Renewable Energy Sources
Representative days
Time Series
title_short Clustering methods to find representative days for modelling the Portuguese electricity system
title_full Clustering methods to find representative days for modelling the Portuguese electricity system
title_fullStr Clustering methods to find representative days for modelling the Portuguese electricity system
title_full_unstemmed Clustering methods to find representative days for modelling the Portuguese electricity system
title_sort Clustering methods to find representative days for modelling the Portuguese electricity system
author Palmeiro, João Vasco da Silva
author_facet Palmeiro, João Vasco da Silva
author_role author
dc.contributor.none.fl_str_mv Scott, Ian James
RUN
dc.contributor.author.fl_str_mv Palmeiro, João Vasco da Silva
dc.subject.por.fl_str_mv Clustering
Energy Economics
Power System Modelling
Renewable Energy Sources
Representative days
Time Series
topic Clustering
Energy Economics
Power System Modelling
Renewable Energy Sources
Representative days
Time Series
description Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2021
dc.date.none.fl_str_mv 2021-11-02
2021-11-02T00:00:00Z
2022-01-20T17:24:41Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/131205
TID:202815404
url http://hdl.handle.net/10362/131205
identifier_str_mv TID:202815404
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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