Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis

Detalhes bibliográficos
Autor(a) principal: Ricardo Jorge Bessa
Data de Publicação: 2013
Outros Autores: Manuel Matos
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/123456789/4172
http://dx.doi.org/10.1016/j.epsr.2012.08.013
Resumo: This paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.
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spelling Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysisThis paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.2017-12-16T15:08:11Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4172http://dx.doi.org/10.1016/j.epsr.2012.08.013engRicardo Jorge BessaManuel Matosinfo: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:RCAAP2023-05-15T10:20:47Zoai:repositorio.inesctec.pt:123456789/4172Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:38.021446Repositório 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 Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
title Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
spellingShingle Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
Ricardo Jorge Bessa
title_short Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
title_full Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
title_fullStr Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
title_full_unstemmed Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
title_sort Global against divided optimization for the participation of an EV aggregator in the day-ahead electricity market. Part II: Numerical analysis
author Ricardo Jorge Bessa
author_facet Ricardo Jorge Bessa
Manuel Matos
author_role author
author2 Manuel Matos
author2_role author
dc.contributor.author.fl_str_mv Ricardo Jorge Bessa
Manuel Matos
description This paper presents numerical analysis of two alternative optimization approaches intended to support an EV aggregation agent in optimizing buying bids for the day-ahead electricity market. A study with market data from the Iberian electricity market is used for comparison and validation of the forecasting and optimization performance of the global and divided optimization approaches. The results show that evaluating the forecast quality separately from its impact in the optimization results is misleading, because a forecast with a low error might result in a higher cost than a forecast with higher error. Both bidding approaches were also compared with an inflexible EV load approach where the EV are not controlled by an aggregator and start charging when they plug-in. Results show that optimized bids allow a considerable cost reduction when compared to an inflexible load approach, and the computational performance of the algorithms satisfies the requirements for operational use by a future real EV aggregation agent.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2017-12-16T15:08:11Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/4172
http://dx.doi.org/10.1016/j.epsr.2012.08.013
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http://dx.doi.org/10.1016/j.epsr.2012.08.013
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