Optimization Models for EV Aggregator Participation in a Manual Reserve Market

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/123456789/4171
http://dx.doi.org/10.1109/tpwrs.2012.2233222
Resumo: The charging flexibility of electric vehicles (EV) when aggregated by a market agent creates an opportunity for selling manual reserve in the electricity market. This paper describes a new optimization algorithm for optimizing manual reserve bids. Furthermore, two operational management algorithms covering alternative gate closures (i.e., day-ahead and hour-ahead) are also described. These operational algorithms coordinate EV charging for mitigating forecast errors. A case-study with data from the Iberian electricity market and synthetic EV time series is used for evaluating the algorithms.
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spelling Optimization Models for EV Aggregator Participation in a Manual Reserve MarketThe charging flexibility of electric vehicles (EV) when aggregated by a market agent creates an opportunity for selling manual reserve in the electricity market. This paper describes a new optimization algorithm for optimizing manual reserve bids. Furthermore, two operational management algorithms covering alternative gate closures (i.e., day-ahead and hour-ahead) are also described. These operational algorithms coordinate EV charging for mitigating forecast errors. A case-study with data from the Iberian electricity market and synthetic EV time series is used for evaluating the algorithms.2017-12-16T15:08:07Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4171http://dx.doi.org/10.1109/tpwrs.2012.2233222engRicardo 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:RCAAP2023-05-15T10:20:25Zoai:repositorio.inesctec.pt:123456789/4171Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:53:05.019675Repositório 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 Optimization Models for EV Aggregator Participation in a Manual Reserve Market
title Optimization Models for EV Aggregator Participation in a Manual Reserve Market
spellingShingle Optimization Models for EV Aggregator Participation in a Manual Reserve Market
Ricardo Jorge Bessa
title_short Optimization Models for EV Aggregator Participation in a Manual Reserve Market
title_full Optimization Models for EV Aggregator Participation in a Manual Reserve Market
title_fullStr Optimization Models for EV Aggregator Participation in a Manual Reserve Market
title_full_unstemmed Optimization Models for EV Aggregator Participation in a Manual Reserve Market
title_sort Optimization Models for EV Aggregator Participation in a Manual Reserve Market
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 The charging flexibility of electric vehicles (EV) when aggregated by a market agent creates an opportunity for selling manual reserve in the electricity market. This paper describes a new optimization algorithm for optimizing manual reserve bids. Furthermore, two operational management algorithms covering alternative gate closures (i.e., day-ahead and hour-ahead) are also described. These operational algorithms coordinate EV charging for mitigating forecast errors. A case-study with data from the Iberian electricity market and synthetic EV time series is used for evaluating the algorithms.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2017-12-16T15:08:07Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/4171
http://dx.doi.org/10.1109/tpwrs.2012.2233222
url http://repositorio.inesctec.pt/handle/123456789/4171
http://dx.doi.org/10.1109/tpwrs.2012.2233222
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