A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers
Autor(a) principal: | |
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Data de Publicação: | 2015 |
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/10400.22/7325 |
Resumo: | In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem. |
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A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailersElectricity retail marketEvolutionary multi-objective optimizationRetailerNSGA-IIIn this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem.ElsevierRepositório Científico do Instituto Politécnico do PortoFotouhi Ghazvini, Mohammad AliSoares, JoãoHorta, NunoNeves, RuiCastro, RuiVale, Zita2016-01-07T15:59:33Z2015-082015-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/7325eng10.1016/j.apenergy.2015.04.067metadata 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:RCAAP2023-03-13T12:47:49Zoai:recipp.ipp.pt:10400.22/7325Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:27:51.121028Repositório 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 multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
title |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
spellingShingle |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers Fotouhi Ghazvini, Mohammad Ali Electricity retail market Evolutionary multi-objective optimization Retailer NSGA-II |
title_short |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
title_full |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
title_fullStr |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
title_full_unstemmed |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
title_sort |
A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers |
author |
Fotouhi Ghazvini, Mohammad Ali |
author_facet |
Fotouhi Ghazvini, Mohammad Ali Soares, João Horta, Nuno Neves, Rui Castro, Rui Vale, Zita |
author_role |
author |
author2 |
Soares, João Horta, Nuno Neves, Rui Castro, Rui Vale, Zita |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Fotouhi Ghazvini, Mohammad Ali Soares, João Horta, Nuno Neves, Rui Castro, Rui Vale, Zita |
dc.subject.por.fl_str_mv |
Electricity retail market Evolutionary multi-objective optimization Retailer NSGA-II |
topic |
Electricity retail market Evolutionary multi-objective optimization Retailer NSGA-II |
description |
In this paper, we formulate the electricity retailers’ short-term decision-making problem in a liberalized retail market as a multi-objective optimization model. Retailers with light physical assets, such as generation and storage units in the distribution network, are considered. Following advances in smart grid technologies, electricity retailers are becoming able to employ incentive-based demand response (DR) programs in addition to their physical assets to effectively manage the risks of market price and load variations. In this model, the DR scheduling is performed simultaneously with the dispatch of generation and storage units. The ultimate goal is to find the optimal values of the hourly financial incentives offered to the end-users. The proposed model considers the capacity obligations imposed on retailers by the grid operator. The profit seeking retailer also has the objective to minimize the peak demand to avoid the high capacity charges in form of grid tariffs or penalties. The non-dominated sorting genetic algorithm II (NSGA-II) is used to solve the multi-objective problem. It is a fast and elitist multi-objective evolutionary algorithm. A case study is solved to illustrate the efficient performance of the proposed methodology. Simulation results show the effectiveness of the model for designing the incentive-based DR programs and indicate the efficiency of NSGA-II in solving the retailers’ multi-objective problem. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-08 2015-08-01T00:00:00Z 2016-01-07T15:59:33Z |
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/10400.22/7325 |
url |
http://hdl.handle.net/10400.22/7325 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1016/j.apenergy.2015.04.067 |
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 |
Elsevier |
publisher.none.fl_str_mv |
Elsevier |
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) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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