Novel Multi-Stage Stochastic DG Investment Planning with Recourse

Detalhes bibliográficos
Autor(a) principal: Santos,SF
Data de Publicação: 2017
Outros Autores: Fitiwi,DZ, Bizuayehu,AW, Shafie khah,M, Asensio,M, Contreras,J, Pereira Cabrita,CMP, 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/123456789/4806
http://dx.doi.org/10.1109/tste.2016.2590460
Resumo: This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.
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spelling Novel Multi-Stage Stochastic DG Investment Planning with RecourseThis paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.2017-12-22T17:58:22Z2017-01-01T00:00:00Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/4806http://dx.doi.org/10.1109/tste.2016.2590460engSantos,SFFitiwi,DZBizuayehu,AWShafie khah,MAsensio,MContreras,JPereira Cabrita,CMPJoão 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:RCAAP2023-05-15T10:19:49Zoai:repositorio.inesctec.pt:123456789/4806Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:15.389597Repositório 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 Novel Multi-Stage Stochastic DG Investment Planning with Recourse
title Novel Multi-Stage Stochastic DG Investment Planning with Recourse
spellingShingle Novel Multi-Stage Stochastic DG Investment Planning with Recourse
Santos,SF
title_short Novel Multi-Stage Stochastic DG Investment Planning with Recourse
title_full Novel Multi-Stage Stochastic DG Investment Planning with Recourse
title_fullStr Novel Multi-Stage Stochastic DG Investment Planning with Recourse
title_full_unstemmed Novel Multi-Stage Stochastic DG Investment Planning with Recourse
title_sort Novel Multi-Stage Stochastic DG Investment Planning with Recourse
author Santos,SF
author_facet Santos,SF
Fitiwi,DZ
Bizuayehu,AW
Shafie khah,M
Asensio,M
Contreras,J
Pereira Cabrita,CMP
João Catalão
author_role author
author2 Fitiwi,DZ
Bizuayehu,AW
Shafie khah,M
Asensio,M
Contreras,J
Pereira Cabrita,CMP
João Catalão
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Santos,SF
Fitiwi,DZ
Bizuayehu,AW
Shafie khah,M
Asensio,M
Contreras,J
Pereira Cabrita,CMP
João Catalão
description This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.
publishDate 2017
dc.date.none.fl_str_mv 2017-12-22T17:58:22Z
2017-01-01T00:00:00Z
2017
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/4806
http://dx.doi.org/10.1109/tste.2016.2590460
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http://dx.doi.org/10.1109/tste.2016.2590460
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