Modelling state spaces and discrete control using MILP: computational cost considerations for demand response
Autor(a) principal: | |
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Data de Publicação: | 2021 |
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/10316/101192 https://doi.org/10.4108/eai.23-12-2020.167787 |
Resumo: | INTRODUCTION: Demand response (DR) has been proposed as a mechanism to induce electricity cost reductions and is typically assumed to require the adoption of time-differentiated electricity prices. Making the most of these requires using automated energy management systems to produce optimised DR plans. Mixed-integer linear programming (MILP) has been used for this purpose, including by modelling dynamic systems (DS). OBJECTIVES: In this paper, we compare the computational performance of MILP approaches for modelling state spaces and multi-level discrete control (MLDC) in DR problems involving DSs. METHODS: A state-of-the-art MILP solver was used to compute solutions and compare approaches. RESULTS: Modelling state spaces using decision variables proved to be the most efficient option in over 80% of cases. In turn, the new MLDC approaches outperformed the standard one in about 60% of cases despite performing in the same range. CONCLUSION: We conclude that using state variables is generally the better option and that all MLDC variants perform similarly. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Modelling state spaces and discrete control using MILP: computational cost considerations for demand responsecomputational performancestate spacediscrete controlmixed-integer linear programmingmultiple-choice programmingINTRODUCTION: Demand response (DR) has been proposed as a mechanism to induce electricity cost reductions and is typically assumed to require the adoption of time-differentiated electricity prices. Making the most of these requires using automated energy management systems to produce optimised DR plans. Mixed-integer linear programming (MILP) has been used for this purpose, including by modelling dynamic systems (DS). OBJECTIVES: In this paper, we compare the computational performance of MILP approaches for modelling state spaces and multi-level discrete control (MLDC) in DR problems involving DSs. METHODS: A state-of-the-art MILP solver was used to compute solutions and compare approaches. RESULTS: Modelling state spaces using decision variables proved to be the most efficient option in over 80% of cases. In turn, the new MLDC approaches outperformed the standard one in about 60% of cases despite performing in the same range. CONCLUSION: We conclude that using state variables is generally the better option and that all MLDC variants perform similarly.2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/101192http://hdl.handle.net/10316/101192https://doi.org/10.4108/eai.23-12-2020.167787eng2032-944XMagalhães, P. L.Antunes, C. H.info: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:RCAAP2022-08-16T20:49:49Zoai:estudogeral.uc.pt:10316/101192Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:18:26.376217Repositório 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 |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
title |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
spellingShingle |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response Magalhães, P. L. computational performance state space discrete control mixed-integer linear programming multiple-choice programming |
title_short |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
title_full |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
title_fullStr |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
title_full_unstemmed |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
title_sort |
Modelling state spaces and discrete control using MILP: computational cost considerations for demand response |
author |
Magalhães, P. L. |
author_facet |
Magalhães, P. L. Antunes, C. H. |
author_role |
author |
author2 |
Antunes, C. H. |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Magalhães, P. L. Antunes, C. H. |
dc.subject.por.fl_str_mv |
computational performance state space discrete control mixed-integer linear programming multiple-choice programming |
topic |
computational performance state space discrete control mixed-integer linear programming multiple-choice programming |
description |
INTRODUCTION: Demand response (DR) has been proposed as a mechanism to induce electricity cost reductions and is typically assumed to require the adoption of time-differentiated electricity prices. Making the most of these requires using automated energy management systems to produce optimised DR plans. Mixed-integer linear programming (MILP) has been used for this purpose, including by modelling dynamic systems (DS). OBJECTIVES: In this paper, we compare the computational performance of MILP approaches for modelling state spaces and multi-level discrete control (MLDC) in DR problems involving DSs. METHODS: A state-of-the-art MILP solver was used to compute solutions and compare approaches. RESULTS: Modelling state spaces using decision variables proved to be the most efficient option in over 80% of cases. In turn, the new MLDC approaches outperformed the standard one in about 60% of cases despite performing in the same range. CONCLUSION: We conclude that using state variables is generally the better option and that all MLDC variants perform similarly. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021 |
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/10316/101192 http://hdl.handle.net/10316/101192 https://doi.org/10.4108/eai.23-12-2020.167787 |
url |
http://hdl.handle.net/10316/101192 https://doi.org/10.4108/eai.23-12-2020.167787 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2032-944X |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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) |
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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1799134079090688000 |