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Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods

Bibliographic Details
Main Author: Silva, Cátia
Publication Date: 2019
Other Authors: Faria, Pedro, Vale, Zita
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.22/22383
Summary: Nowadays, the data can be considered an asset when properly managed. An entity with the right tool to analyse the amount of data existent and withdraw crucial information will have the power to obliterate the competition. In the Energy sector, with Smart Grid introduction, small resources have more influence in the market through Demand Response and bidirectional communication. However, none of the actual business models is prepared to deal with the uncertainty related to these resources. The authors, in order to find a solution for this complex problem, proposed a methodology which the goal is to minimize operation costs and give fair compensation for resources who participate in the management of local markets. With this fair payment, it is expected continuous participation. Through clustering methods, remuneration groups are created. In the present paper, a study about the optimal number of clusters is performed. The information gives the Aggregator control in results of the following phases, understanding the impact in the remuneration of the resources.
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spelling Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering MethodsClusteringAggregationConsumersRemunerationEnergy MarketNowadays, the data can be considered an asset when properly managed. An entity with the right tool to analyse the amount of data existent and withdraw crucial information will have the power to obliterate the competition. In the Energy sector, with Smart Grid introduction, small resources have more influence in the market through Demand Response and bidirectional communication. However, none of the actual business models is prepared to deal with the uncertainty related to these resources. The authors, in order to find a solution for this complex problem, proposed a methodology which the goal is to minimize operation costs and give fair compensation for resources who participate in the management of local markets. With this fair payment, it is expected continuous participation. Through clustering methods, remuneration groups are created. In the present paper, a study about the optimal number of clusters is performed. The information gives the Aggregator control in results of the following phases, understanding the impact in the remuneration of the resources.IEEEREPOSITÓRIO P.PORTOSilva, CátiaFaria, PedroVale, Zita2023-02-23T15:56:01Z20192019-01-01T00:00:00Zconference objectinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10400.22/22383eng978-1-72813-192-410.1109/ISAP48318.2019.9065957info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-02T03:04:33Zoai:recipp.ipp.pt:10400.22/22383Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T00:39:22.354209Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
title Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
spellingShingle Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
Silva, Cátia
Clustering
Aggregation
Consumers
Remuneration
Energy Market
title_short Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
title_full Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
title_fullStr Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
title_full_unstemmed Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
title_sort Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
author Silva, Cátia
author_facet Silva, Cátia
Faria, Pedro
Vale, Zita
author_role author
author2 Faria, Pedro
Vale, Zita
author2_role author
author
dc.contributor.none.fl_str_mv REPOSITÓRIO P.PORTO
dc.contributor.author.fl_str_mv Silva, Cátia
Faria, Pedro
Vale, Zita
dc.subject.por.fl_str_mv Clustering
Aggregation
Consumers
Remuneration
Energy Market
topic Clustering
Aggregation
Consumers
Remuneration
Energy Market
description Nowadays, the data can be considered an asset when properly managed. An entity with the right tool to analyse the amount of data existent and withdraw crucial information will have the power to obliterate the competition. In the Energy sector, with Smart Grid introduction, small resources have more influence in the market through Demand Response and bidirectional communication. However, none of the actual business models is prepared to deal with the uncertainty related to these resources. The authors, in order to find a solution for this complex problem, proposed a methodology which the goal is to minimize operation costs and give fair compensation for resources who participate in the management of local markets. With this fair payment, it is expected continuous participation. Through clustering methods, remuneration groups are created. In the present paper, a study about the optimal number of clusters is performed. The information gives the Aggregator control in results of the following phases, understanding the impact in the remuneration of the resources.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01T00:00:00Z
2023-02-23T15:56:01Z
dc.type.driver.fl_str_mv conference object
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/22383
url http://hdl.handle.net/10400.22/22383
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 978-1-72813-192-4
10.1109/ISAP48318.2019.9065957
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.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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