A new clustering algorithm for load profiling based on billing data

Detalhes bibliográficos
Autor(a) principal: José Nuno Fidalgo
Data de Publicação: 2012
Outros Autores: Manuel Matos, Luís Ribeiro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/2222
Resumo: In open energy markets, the settlement process between distribution operators and traders is made on an hourly (or 15 min) basis, while LV consumers' billing data continues to result from monthly energy bills. In order to reconcile these two different realities, load profiling is used as a means to redistribute the consumed energy of each trader's portfolio by hourly intervals, according to recorded consumption patterns. This paper presents a new clustering approach to derive typical load diagrams that can be used in the process. The algorithm uses real load diagrams obtained in measurement campaigns to define classes (in the billing information space) that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP Distribution (the Portuguese distribution system operator) and the result was approved by the Regulatory Authority that adopted the proposed profiles for market use.
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spelling A new clustering algorithm for load profiling based on billing dataIn open energy markets, the settlement process between distribution operators and traders is made on an hourly (or 15 min) basis, while LV consumers' billing data continues to result from monthly energy bills. In order to reconcile these two different realities, load profiling is used as a means to redistribute the consumed energy of each trader's portfolio by hourly intervals, according to recorded consumption patterns. This paper presents a new clustering approach to derive typical load diagrams that can be used in the process. The algorithm uses real load diagrams obtained in measurement campaigns to define classes (in the billing information space) that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP Distribution (the Portuguese distribution system operator) and the result was approved by the Regulatory Authority that adopted the proposed profiles for market use.2017-11-16T13:22:23Z2012-01-01T00:00:00Z2012info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/2222engJosé Nuno FidalgoManuel MatosLuís Ribeiroinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:54Zoai:repositorio.inesctec.pt:123456789/2222Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:46.379547Repositório 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 A new clustering algorithm for load profiling based on billing data
title A new clustering algorithm for load profiling based on billing data
spellingShingle A new clustering algorithm for load profiling based on billing data
José Nuno Fidalgo
title_short A new clustering algorithm for load profiling based on billing data
title_full A new clustering algorithm for load profiling based on billing data
title_fullStr A new clustering algorithm for load profiling based on billing data
title_full_unstemmed A new clustering algorithm for load profiling based on billing data
title_sort A new clustering algorithm for load profiling based on billing data
author José Nuno Fidalgo
author_facet José Nuno Fidalgo
Manuel Matos
Luís Ribeiro
author_role author
author2 Manuel Matos
Luís Ribeiro
author2_role author
author
dc.contributor.author.fl_str_mv José Nuno Fidalgo
Manuel Matos
Luís Ribeiro
description In open energy markets, the settlement process between distribution operators and traders is made on an hourly (or 15 min) basis, while LV consumers' billing data continues to result from monthly energy bills. In order to reconcile these two different realities, load profiling is used as a means to redistribute the consumed energy of each trader's portfolio by hourly intervals, according to recorded consumption patterns. This paper presents a new clustering approach to derive typical load diagrams that can be used in the process. The algorithm uses real load diagrams obtained in measurement campaigns to define classes (in the billing information space) that maximize the compactness of the diagrams in each class. The methodology was developed in a project with EDP Distribution (the Portuguese distribution system operator) and the result was approved by the Regulatory Authority that adopted the proposed profiles for market use.
publishDate 2012
dc.date.none.fl_str_mv 2012-01-01T00:00:00Z
2012
2017-11-16T13:22:23Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/2222
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dc.language.iso.fl_str_mv eng
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