Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response
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Publication Date: | 2022 |
Other Authors: | , , , |
Format: | Article |
Language: | eng |
Source: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Download full: | http://hdl.handle.net/10400.22/22058 |
Summary: | In the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources. |
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Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand ResponseCitizen energy communityDemand responseEnd-user participationEnergy flexibilityUnsupervised learningIn the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources.This article is a result of the project RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the support of the GECAD research center (UIDB/ 00760/2020) for providing to the project team the needed work facilities and equipment.MDPIRepositório Científico do Instituto Politécnico do PortoBarreto, RúbenGoncalves, CalvinGomes, LuisFaria, PedroVale, Zita2023-02-01T11:00:56Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/22058eng10.3390/en15072380info: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:RCAAP2023-03-13T13:18:25Zoai:recipp.ipp.pt:10400.22/22058Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:42:07.847749Repositório 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 |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
title |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
spellingShingle |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response Barreto, Rúben Citizen energy community Demand response End-user participation Energy flexibility Unsupervised learning |
title_short |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
title_full |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
title_fullStr |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
title_full_unstemmed |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
title_sort |
Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response |
author |
Barreto, Rúben |
author_facet |
Barreto, Rúben Goncalves, Calvin Gomes, Luis Faria, Pedro Vale, Zita |
author_role |
author |
author2 |
Goncalves, Calvin Gomes, Luis Faria, Pedro Vale, Zita |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Barreto, Rúben Goncalves, Calvin Gomes, Luis Faria, Pedro Vale, Zita |
dc.subject.por.fl_str_mv |
Citizen energy community Demand response End-user participation Energy flexibility Unsupervised learning |
topic |
Citizen energy community Demand response End-user participation Energy flexibility Unsupervised learning |
description |
In the energy sector, prosumers are becoming relevant entities for energy management systems since they can share energy with their citizen energy community (CEC). Thus, this paper proposes a novel methodology based on demand response (DR) participation in a CEC context, where unsupervised learning algorithms such as convolutional neural networks and k-means are used. This novel methodology can analyze future events on the grid and balance the consumption and generation using end-user flexibility. The end-users’ invitations to the DR event were according to their ranking obtained through three metrics. These metrics were energy flexibility, participation ratio, and flexibility history of the end-users. During the DR event, a continuous balancing assessment is performed to allow the invitation of additional end-users. Real data from a CEC with 50 buildings were used, where the results demonstrated that the end-users’ participation in two DR events allows reduction of energy costs by EUR 1.31, balancing the CEC energy resources. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-02-01T11:00:56Z |
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/10400.22/22058 |
url |
http://hdl.handle.net/10400.22/22058 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.3390/en15072380 |
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 |
MDPI |
publisher.none.fl_str_mv |
MDPI |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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