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Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline

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  • Ulazia, Alain
  • Saenz, Jon
  • Ibarra-Berastegui, Gabriel

Abstract

In this work, the WRF meteorological model is run in three different modes to estimate the wind energy potential in the Bay of Biscay for the 1990–2001 period. The first simulation (NODA) involves a typical use of the WRF model and it does not use data assimilation. The second one (12hDA) performs 3DVAR data assimilation at 00 UTC and 12 UTC. Finally, 6hDA uses 3DVAR data assimilation at 00 UTC, 06 UTC, 12 UTC and 18 UTC. Verification for the three simulations has been carried out at a preliminary stage using wind data from buoys, and then a spatially distributed analysis has been conducted of surface wind based on satellite data from the Cross-Calibrated Multi-Platform (CCMP). To that purpose, the spatial correlation and error patterns over our study area have been used as statistical indicators. The results indicate that the wind values obtained with data assimilation every six hours (6hDA) yield the best verification scores at a 95% confidence level, thereby being the most accurate at reproducing wind observations in the area. Regarding the estimation of wind energy potential, at a second stage, we tested the calculation’s sensitivity to the use of data assimilation. The most reliable simulation with data assimilation (6hDA) estimates 21% less energy potential than the simulation without data assimilation. In the absence of historical wind observation records of the sea with sufficient time and space resolution, meteorological models such as WRF provide an estimation of the wind values in tentative areas for offshore wind farms. In this line, our study highlights the need to use meteorological models with data assimilation, as future wind energy production can then be more realistically estimated beforehand. This may also contribute to a more accurate economic and technical evaluation of the risks and benefits for future investments in offshore wind energy.

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  • Ulazia, Alain & Saenz, Jon & Ibarra-Berastegui, Gabriel, 2016. "Sensitivity to the use of 3DVAR data assimilation in a mesoscale model for estimating offshore wind energy potential. A case study of the Iberian northern coastline," Applied Energy, Elsevier, vol. 180(C), pages 617-627.
    • Handle: RePEc:eee:appene:v:180:y:2016:i:c:p:617-627
    DOI: 10.1016/j.apenergy.2016.08.033
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    Cited by:

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    3. Soares, Pedro M.M. & Lima, Daniela C.A. & Cardoso, Rita M. & Nascimento, Manuel L. & Semedo, Alvaro, 2017. "Western Iberian offshore wind resources: More or less in a global warming climate?," Applied Energy, Elsevier, vol. 203(C), pages 72-90.
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    5. Yuan, Renyu & Ji, Wenju & Luo, Kun & Wang, Jianwen & Zhang, Sanxia & Wang, Qiang & Fan, Jianren & Ni, MingJiang & Cen, Kefa, 2017. "Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm," Applied Energy, Elsevier, vol. 206(C), pages 113-125.
    6. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.
    7. Wang, Qiang & Luo, Kun & Yuan, Renyu & Zhang, Sanxia & Fan, Jianren, 2019. "Wake and performance interference between adjacent wind farms: Case study of Xinjiang in China by means of mesoscale simulations," Energy, Elsevier, vol. 166(C), pages 1168-1180.
    8. Annas Fauzy & Cheng-Dar Yue & Chien-Cheng Tu & Ta-Hui Lin, 2021. "Understanding the Potential of Wind Farm Exploitation in Tropical Island Countries: A Case for Indonesia," Energies, MDPI, vol. 14(9), pages 1-26, May.
    9. Mazhar Hussain Baloch & Dahaman Ishak & Sohaib Tahir Chaudary & Baqir Ali & Ali Asghar Memon & Touqeer Ahmed Jumani, 2019. "Wind Power Integration: An Experimental Investigation for Powering Local Communities," Energies, MDPI, vol. 12(4), pages 1-24, February.
    10. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    11. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegui, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2017. "Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean," Applied Energy, Elsevier, vol. 208(C), pages 1232-1245.
    12. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    13. Carreno-Madinabeitia, Sheila & Ibarra-Berastegi, Gabriel & Sáenz, Jon & Ulazia, Alain, 2021. "Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)," Energy, Elsevier, vol. 226(C).
    14. Nie, Bingchuan & Li, Jiachun, 2018. "Technical potential assessment of offshore wind energy over shallow continent shelf along China coast," Renewable Energy, Elsevier, vol. 128(PA), pages 391-399.
    15. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.

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