Defining the Optimal Number of Demand Response Programs and Tariffs Using Clustering Methods
Main Author: | |
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Publication Date: | 2019 |
Other Authors: | , |
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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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 |
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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) |
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Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia |
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