Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2021 |
| Tipo de documento: | Relatório |
| Idioma: | eng |
| Título da fonte: | Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) |
| Texto Completo: | http://hdl.handle.net/10400.9/3726 |
Resumo: | ABSTRACT: The work presented in this deliverable was developed by LNEG and R&D NESTER as part of the R&D activities of the project OPTIGRID - Methodology for the dynamic line rating analysis and optimal management of power networks. According to the plan activities of Tasks 2.1 and 2.4, the main objective of this deliverable is to present the methods applied to obtain the meteorological forecast data need to feed the models developed in this project and it merges all the datasets to be used in each case study. According to the work plan, and as reported in the deliverables from Task 4, three case studies were defined for: A) a region with large distributed wind capacity; B) a region with large photovoltaic (PV) potential and limited grid capacity; and C) market splitting occurrence in MIBEL due to congestion in the interconnections. For these regions the meteorological forecast data, used during this project, were obtained using a numerical weather prediction model and computational fluid dynamic model coupling approach. The numerical weather prediction (NWP) model is used to forecast the hourly spatial meteorological data (e.g., wind speed and direction, temperature) during 2018 with a maximum spatial resolution of 3 km. This model is calibrated regarding its physical parametrizations and initial/boundary conditions, among others. |
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Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2Renewable energy sourcesElectrical networksIntegrationElectricity marketsMIBELABSTRACT: The work presented in this deliverable was developed by LNEG and R&D NESTER as part of the R&D activities of the project OPTIGRID - Methodology for the dynamic line rating analysis and optimal management of power networks. According to the plan activities of Tasks 2.1 and 2.4, the main objective of this deliverable is to present the methods applied to obtain the meteorological forecast data need to feed the models developed in this project and it merges all the datasets to be used in each case study. According to the work plan, and as reported in the deliverables from Task 4, three case studies were defined for: A) a region with large distributed wind capacity; B) a region with large photovoltaic (PV) potential and limited grid capacity; and C) market splitting occurrence in MIBEL due to congestion in the interconnections. For these regions the meteorological forecast data, used during this project, were obtained using a numerical weather prediction model and computational fluid dynamic model coupling approach. The numerical weather prediction (NWP) model is used to forecast the hourly spatial meteorological data (e.g., wind speed and direction, temperature) during 2018 with a maximum spatial resolution of 3 km. This model is calibrated regarding its physical parametrizations and initial/boundary conditions, among others.Repositório do LNEGCouto, António2022-01-25T18:22:05Z2021-092021-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/reportapplication/pdfhttp://hdl.handle.net/10400.9/3726enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-10T11:32:16Zoai:null:10400.9/3726Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T01:14:21.528381Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse |
| dc.title.none.fl_str_mv |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| title |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| spellingShingle |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 Couto, António Renewable energy sources Electrical networks Integration Electricity markets MIBEL |
| title_short |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| title_full |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| title_fullStr |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| title_full_unstemmed |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| title_sort |
Meteorological forecast data: Coupling NWP and CFD Modeling. Merging the datasets: Deliverable D2.2 |
| author |
Couto, António |
| author_facet |
Couto, António |
| author_role |
author |
| dc.contributor.none.fl_str_mv |
Repositório do LNEG |
| dc.contributor.author.fl_str_mv |
Couto, António |
| dc.subject.por.fl_str_mv |
Renewable energy sources Electrical networks Integration Electricity markets MIBEL |
| topic |
Renewable energy sources Electrical networks Integration Electricity markets MIBEL |
| description |
ABSTRACT: The work presented in this deliverable was developed by LNEG and R&D NESTER as part of the R&D activities of the project OPTIGRID - Methodology for the dynamic line rating analysis and optimal management of power networks. According to the plan activities of Tasks 2.1 and 2.4, the main objective of this deliverable is to present the methods applied to obtain the meteorological forecast data need to feed the models developed in this project and it merges all the datasets to be used in each case study. According to the work plan, and as reported in the deliverables from Task 4, three case studies were defined for: A) a region with large distributed wind capacity; B) a region with large photovoltaic (PV) potential and limited grid capacity; and C) market splitting occurrence in MIBEL due to congestion in the interconnections. For these regions the meteorological forecast data, used during this project, were obtained using a numerical weather prediction model and computational fluid dynamic model coupling approach. The numerical weather prediction (NWP) model is used to forecast the hourly spatial meteorological data (e.g., wind speed and direction, temperature) during 2018 with a maximum spatial resolution of 3 km. This model is calibrated regarding its physical parametrizations and initial/boundary conditions, among others. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-09 2021-09-01T00:00:00Z 2022-01-25T18:22:05Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/report |
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report |
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publishedVersion |
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http://hdl.handle.net/10400.9/3726 |
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http://hdl.handle.net/10400.9/3726 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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