Dynamic electricity pricing for electric vehicles using stochastic programming

Detalhes bibliográficos
Autor(a) principal: Soares, João
Data de Publicação: 2017
Outros Autores: Ghazvini, Mohammad Ali Fotouhi, Borges, Nuno, Vale, Zita
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/10219
Resumo: Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.
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spelling Dynamic electricity pricing for electric vehicles using stochastic programmingDemand responseElectric vehiclesEnergy resource schedulingOptimal pricingSmart gridStochastic programmingElectric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.ElsevierRepositório Científico do Instituto Politécnico do PortoSoares, JoãoGhazvini, Mohammad Ali FotouhiBorges, NunoVale, Zita2017-03-012117-01-01T00:00:00Z2017-03-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/10219eng10.1016/j.energy.2016.12.108metadata only accessinfo: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:RCAAP2023-03-13T12:51:46Zoai:recipp.ipp.pt:10400.22/10219Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:30:41.258002Repositório 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 Dynamic electricity pricing for electric vehicles using stochastic programming
title Dynamic electricity pricing for electric vehicles using stochastic programming
spellingShingle Dynamic electricity pricing for electric vehicles using stochastic programming
Soares, João
Demand response
Electric vehicles
Energy resource scheduling
Optimal pricing
Smart grid
Stochastic programming
title_short Dynamic electricity pricing for electric vehicles using stochastic programming
title_full Dynamic electricity pricing for electric vehicles using stochastic programming
title_fullStr Dynamic electricity pricing for electric vehicles using stochastic programming
title_full_unstemmed Dynamic electricity pricing for electric vehicles using stochastic programming
title_sort Dynamic electricity pricing for electric vehicles using stochastic programming
author Soares, João
author_facet Soares, João
Ghazvini, Mohammad Ali Fotouhi
Borges, Nuno
Vale, Zita
author_role author
author2 Ghazvini, Mohammad Ali Fotouhi
Borges, Nuno
Vale, Zita
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Soares, João
Ghazvini, Mohammad Ali Fotouhi
Borges, Nuno
Vale, Zita
dc.subject.por.fl_str_mv Demand response
Electric vehicles
Energy resource scheduling
Optimal pricing
Smart grid
Stochastic programming
topic Demand response
Electric vehicles
Energy resource scheduling
Optimal pricing
Smart grid
Stochastic programming
description Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business.
publishDate 2017
dc.date.none.fl_str_mv 2017-03-01
2017-03-01T00:00:00Z
2117-01-01T00:00:00Z
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/10219
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dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.energy.2016.12.108
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dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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