Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes

Detalhes bibliográficos
Autor(a) principal: Lujano Rojas,JM
Data de Publicação: 2017
Outros Autores: Dufo Lopez,R, Bernal Agustin,JL, João Catalão
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/4808
http://dx.doi.org/10.1109/tsg.2016.2602268
Resumo: Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.
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spelling Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing SchemesModernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.2017-12-22T17:58:27Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4808http://dx.doi.org/10.1109/tsg.2016.2602268engLujano Rojas,JMDufo Lopez,RBernal Agustin,JLJoão Catalãoinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:19:42Zoai:repositorio.inesctec.pt:123456789/4808Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:06.853617Repositório 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 Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
title Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
spellingShingle Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
Lujano Rojas,JM
title_short Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
title_full Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
title_fullStr Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
title_full_unstemmed Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
title_sort Optimizing Daily Operation of Battery Energy Storage Systems Under Real-Time Pricing Schemes
author Lujano Rojas,JM
author_facet Lujano Rojas,JM
Dufo Lopez,R
Bernal Agustin,JL
João Catalão
author_role author
author2 Dufo Lopez,R
Bernal Agustin,JL
João Catalão
author2_role author
author
author
dc.contributor.author.fl_str_mv Lujano Rojas,JM
Dufo Lopez,R
Bernal Agustin,JL
João Catalão
description Modernization of electricity networks is currently being carried out using the concept of the smart grid; hence, the active participation of end-user consumers and distributed generators will be allowed in order to increase system efficiency and renewable power accommodation. In this context, this paper proposes a comprehensive methodology to optimally control lead-acid batteries operating under dynamic pricing schemes in both independent and aggregated ways, taking into account the effects of the charge controller operation, the variable efficiency of the power converter, and the maximum capacity of the electricity network. A genetic algorithm is used to solve the optimization problem in which the daily net cost is minimized. The effectiveness and computational efficiency of the proposed methodology is illustrated using real data from the Spanish electricity market during 2014 and 2015 in order to evaluate the effects of forecasting error of energy prices, observing an important reduction in the estimated benefit as a result of both factors: 1) forecasting error and 2) power system limitations.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-22T17:58:27Z
2017-01-01T00:00:00Z
2017
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/4808
http://dx.doi.org/10.1109/tsg.2016.2602268
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http://dx.doi.org/10.1109/tsg.2016.2602268
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