A methodology to incorporate risk and uncertainty in electricity power planning
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/44875 |
Resumo: | Deterministic models based on most likely forecasts can bring simplicity to the electricity power planning but do not explicitly consider uncertainties and risks which are always present on the electricity systems. Stochastic models can account for uncertain parameters that are critical to obtain a robust solution, requiring however higher modelling and computational effort. The aim of this work was to propose a methodology to identify major uncertainties presented in the electricity system and demonstrate their impact in the long-term electricity production mix, through scenario analysis. The case of an electricity system with high renewable contribution was used to demonstrate how renewables uncertainty can be included in long term planning, combining Monte Carlo Simulation with a deterministic optimization model. This case showed that the problem of including risk in electricity planning could be explored in short running time even for large real systems. The results indicate that high growth demand rate combined with climate uncertainty represent major sources of risk for the definition of robust optimal technology mixes for the future. This is particularly important for the case of electricity systems with high share of renewables as climate change can have a major role on the expected power output. |
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A methodology to incorporate risk and uncertainty in electricity power planningUncertaintyElectricity planningOptimization ModelMonte Carlo SimulationRenewable energy sourcesEngenharia e Tecnologia::Outras Engenharias e TecnologiasScience & TechnologyDeterministic models based on most likely forecasts can bring simplicity to the electricity power planning but do not explicitly consider uncertainties and risks which are always present on the electricity systems. Stochastic models can account for uncertain parameters that are critical to obtain a robust solution, requiring however higher modelling and computational effort. The aim of this work was to propose a methodology to identify major uncertainties presented in the electricity system and demonstrate their impact in the long-term electricity production mix, through scenario analysis. The case of an electricity system with high renewable contribution was used to demonstrate how renewables uncertainty can be included in long term planning, combining Monte Carlo Simulation with a deterministic optimization model. This case showed that the problem of including risk in electricity planning could be explored in short running time even for large real systems. The results indicate that high growth demand rate combined with climate uncertainty represent major sources of risk for the definition of robust optimal technology mixes for the future. This is particularly important for the case of electricity systems with high share of renewables as climate change can have a major role on the expected power output.COMPETE: POCI-01-0145-FEDER-007043University of Minho. ALGORITMIElsevierUniversidade do MinhoSantos, Maria João MartinsFerreira, PaulaAraújo, Madalena20162016-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/44875engSantos, MJ; Ferreira, Paula; Araújo, Madalena (2016) “A methodology to incorporate risk and uncertainty in electricity power planning”, Energy, Vol. 115, 1400-1411.0360-544210.1016/j.energy.2016.03.080http://www.sciencedirect.com/science/article/pii/S0360544216303243info: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:RCAAP2023-07-21T11:59:57Zoai:repositorium.sdum.uminho.pt:1822/44875Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:49:47.112698Repositório 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 |
A methodology to incorporate risk and uncertainty in electricity power planning |
title |
A methodology to incorporate risk and uncertainty in electricity power planning |
spellingShingle |
A methodology to incorporate risk and uncertainty in electricity power planning Santos, Maria João Martins Uncertainty Electricity planning Optimization Model Monte Carlo Simulation Renewable energy sources Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
title_short |
A methodology to incorporate risk and uncertainty in electricity power planning |
title_full |
A methodology to incorporate risk and uncertainty in electricity power planning |
title_fullStr |
A methodology to incorporate risk and uncertainty in electricity power planning |
title_full_unstemmed |
A methodology to incorporate risk and uncertainty in electricity power planning |
title_sort |
A methodology to incorporate risk and uncertainty in electricity power planning |
author |
Santos, Maria João Martins |
author_facet |
Santos, Maria João Martins Ferreira, Paula Araújo, Madalena |
author_role |
author |
author2 |
Ferreira, Paula Araújo, Madalena |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Santos, Maria João Martins Ferreira, Paula Araújo, Madalena |
dc.subject.por.fl_str_mv |
Uncertainty Electricity planning Optimization Model Monte Carlo Simulation Renewable energy sources Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
topic |
Uncertainty Electricity planning Optimization Model Monte Carlo Simulation Renewable energy sources Engenharia e Tecnologia::Outras Engenharias e Tecnologias Science & Technology |
description |
Deterministic models based on most likely forecasts can bring simplicity to the electricity power planning but do not explicitly consider uncertainties and risks which are always present on the electricity systems. Stochastic models can account for uncertain parameters that are critical to obtain a robust solution, requiring however higher modelling and computational effort. The aim of this work was to propose a methodology to identify major uncertainties presented in the electricity system and demonstrate their impact in the long-term electricity production mix, through scenario analysis. The case of an electricity system with high renewable contribution was used to demonstrate how renewables uncertainty can be included in long term planning, combining Monte Carlo Simulation with a deterministic optimization model. This case showed that the problem of including risk in electricity planning could be explored in short running time even for large real systems. The results indicate that high growth demand rate combined with climate uncertainty represent major sources of risk for the definition of robust optimal technology mixes for the future. This is particularly important for the case of electricity systems with high share of renewables as climate change can have a major role on the expected power output. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016 2016-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/44875 |
url |
http://hdl.handle.net/1822/44875 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Santos, MJ; Ferreira, Paula; Araújo, Madalena (2016) “A methodology to incorporate risk and uncertainty in electricity power planning”, Energy, Vol. 115, 1400-1411. 0360-5442 10.1016/j.energy.2016.03.080 http://www.sciencedirect.com/science/article/pii/S0360544216303243 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132264752218112 |