Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents

Detalhes bibliográficos
Autor(a) principal: Shafie khah,M
Data de Publicação: 2016
Outros Autores: Heydarian Forushani,E, Osorio,GJ, Gil,FAS, Aghaei,J, Barani,M, 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/123456789/4851
http://dx.doi.org/10.1109/tsg.2015.2496796
Resumo: With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.
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spelling Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation AgentsWith increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.2017-12-22T18:37:28Z2016-01-01T00:00:00Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4851http://dx.doi.org/10.1109/tsg.2015.2496796engShafie khah,MHeydarian Forushani,EOsorio,GJGil,FASAghaei,JBarani,MJoão 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:RCAAP2023-05-15T10:19:59Zoai:repositorio.inesctec.pt:123456789/4851Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:31.539634Repositório 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 Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
title Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
spellingShingle Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
Shafie khah,M
title_short Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
title_full Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
title_fullStr Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
title_full_unstemmed Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
title_sort Optimal Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
author Shafie khah,M
author_facet Shafie khah,M
Heydarian Forushani,E
Osorio,GJ
Gil,FAS
Aghaei,J
Barani,M
João Catalão
author_role author
author2 Heydarian Forushani,E
Osorio,GJ
Gil,FAS
Aghaei,J
Barani,M
João Catalão
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Shafie khah,M
Heydarian Forushani,E
Osorio,GJ
Gil,FAS
Aghaei,J
Barani,M
João Catalão
description With increasing environmental concerns, the electrification of transportation plays an outstanding role in the sustainable development. In this context, plug-in electric vehicle (PEV) and demand response have indispensable impacts on the future smart grid. Since integration of PEVs into the grid is a key element to achieve sustainable energy systems, this paper presents the optimal behavior of PEV parking lots in the energy and reserve markets. To this end, a model is developed to derive optimal strategies of parking lots, as responsive demands, in both price-based and incentive-based demand response programs (DRPs). The proposed model reflects the impacts of different DRPs on the operational behavior of parking lots and optimizes the participation level of parking lots in each DRP. Uncertainties of PEVs and electricity market are also considered by using a stochastic programming approach. Numerical studies indicate that the PEV parking lots can benefit from the selective participation in DRPs.
publishDate 2016
dc.date.none.fl_str_mv 2016-01-01T00:00:00Z
2016
2017-12-22T18:37:28Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/4851
http://dx.doi.org/10.1109/tsg.2015.2496796
url http://repositorio.inesctec.pt/handle/123456789/4851
http://dx.doi.org/10.1109/tsg.2015.2496796
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