Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/9358 |
Resumo: | The use of distributed generation and demand-response (DR) programs is needed for improving business models, namely concerning the remuneration of these resources in the context of smart grids. In this paper, a methodology is proposed in which a virtual power player aggregates several small-sized resources, including consumers participating in DR programs. The global operation costs resulting from the resource scheduling are minimized. After scheduling the resources in several operation scenarios, clustering tools are applied in order to obtain distinct resources’ groups. The remuneration structure that better fits the aggregator goals is then determined. Two clustering algorithms are compared: 1) hierarchical; nd 2) fuzzy c-means clustering. The remuneration of small resources and consumers that are aggregated is made considering the maximum tariff in each group. The implemented case study considers 2592 operation scenarios based on a real Portuguese distribution network with 548 distributed generators and 20 310 consumers. |
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Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response ProgramsClusteringDemand response (DR) programsDistributed generation (DG)Smart gridsThe use of distributed generation and demand-response (DR) programs is needed for improving business models, namely concerning the remuneration of these resources in the context of smart grids. In this paper, a methodology is proposed in which a virtual power player aggregates several small-sized resources, including consumers participating in DR programs. The global operation costs resulting from the resource scheduling are minimized. After scheduling the resources in several operation scenarios, clustering tools are applied in order to obtain distinct resources’ groups. The remuneration structure that better fits the aggregator goals is then determined. Two clustering algorithms are compared: 1) hierarchical; nd 2) fuzzy c-means clustering. The remuneration of small resources and consumers that are aggregated is made considering the maximum tariff in each group. The implemented case study considers 2592 operation scenarios based on a real Portuguese distribution network with 548 distributed generators and 20 310 consumers.IEEERepositório Científico do Instituto Politécnico do PortoFaria, PedroSpínola, JoãoVale, Zita2017-01-24T14:25:16Z2016-062016-06-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/9358eng10.1109/TII.2016.2541542metadata only accessinfo: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:RCAAP2023-03-13T12:50:44Zoai:recipp.ipp.pt:10400.22/9358Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:29:58.547531Repositório 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 |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
title |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
spellingShingle |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs Faria, Pedro Clustering Demand response (DR) programs Distributed generation (DG) Smart grids |
title_short |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
title_full |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
title_fullStr |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
title_full_unstemmed |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
title_sort |
Aggregation and Remuneration of Electricity Consumers and Producers for the Definition of Demand-Response Programs |
author |
Faria, Pedro |
author_facet |
Faria, Pedro Spínola, João Vale, Zita |
author_role |
author |
author2 |
Spínola, João Vale, Zita |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Faria, Pedro Spínola, João Vale, Zita |
dc.subject.por.fl_str_mv |
Clustering Demand response (DR) programs Distributed generation (DG) Smart grids |
topic |
Clustering Demand response (DR) programs Distributed generation (DG) Smart grids |
description |
The use of distributed generation and demand-response (DR) programs is needed for improving business models, namely concerning the remuneration of these resources in the context of smart grids. In this paper, a methodology is proposed in which a virtual power player aggregates several small-sized resources, including consumers participating in DR programs. The global operation costs resulting from the resource scheduling are minimized. After scheduling the resources in several operation scenarios, clustering tools are applied in order to obtain distinct resources’ groups. The remuneration structure that better fits the aggregator goals is then determined. Two clustering algorithms are compared: 1) hierarchical; nd 2) fuzzy c-means clustering. The remuneration of small resources and consumers that are aggregated is made considering the maximum tariff in each group. The implemented case study considers 2592 operation scenarios based on a real Portuguese distribution network with 548 distributed generators and 20 310 consumers. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06 2016-06-01T00:00:00Z 2017-01-24T14:25:16Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/9358 |
url |
http://hdl.handle.net/10400.22/9358 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1109/TII.2016.2541542 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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