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Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State

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  • de Assis Tavares, Luiz Filipe
  • Shadman, Milad
  • Assad, Luiz Paulo de Freitas
  • Estefen, Segen F.

Abstract

The growing increase of offshore wind implementations has been made this modality subject to many resource assessment works. However, the variety of available wind datasets and the difficulty of the validation process over the ocean still leave uncertainty about the most relevant data for offshore wind resource assessment and how much they affect the energy estimation. This study investigates the influence of ERA5, CFSv2, MERRA2 atmospheric reanalyses, and the WRF model on the offshore wind resource potential and cost estimation of offshore wind projects. The coastline of the Rio de Janeiro state of Brazil is considered as the focus of the study region. Wind results are validated with in-situ measurements from three meteo-oceanographic buoys by the SIMCosta and PNBOIA programs, an anemometer placed on a floating vessel for production storage and offloading (FPSO), and seven weather stations positioned inland near the coastline from the National Meteorological Institute of Brazil (INMET). Then, energy production and cost estimation are calculated employing the IEA-15 MW wind turbine, and the differences between the models are analyzed. The comparative analysis reveals significant differences in the annual energy production, mainly near to the coastline, impacting the cost estimation of the projects in the planning phase.

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  • de Assis Tavares, Luiz Filipe & Shadman, Milad & Assad, Luiz Paulo de Freitas & Estefen, Segen F., 2022. "Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: A case study for Rio de Janeiro State," Energy, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:energy:v:240:y:2022:i:c:s0360544221030164
    DOI: 10.1016/j.energy.2021.122767
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    References listed on IDEAS

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    4. Li, Jiale & Song, Zihao & Wang, Xuefei & Wang, Yanru & Jia, Yaya, 2022. "A novel offshore wind farm typhoon wind speed prediction model based on PSO–Bi-LSTM improved by VMD," Energy, Elsevier, vol. 251(C).

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