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Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil

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  • Gruber, Katharina
  • Klöckl, Claude
  • Regner, Peter
  • Baumgartner, Johann
  • Schmidt, Johannes

Abstract

NASA's MERRA-2 reanalysis is a widely used dataset in renewable energy resource modelling. The Global Wind Atlas (GWA) has been used to bias-correct MERRA-2 data before. There is, however, a lack of an analysis of the performance of MERRA-2 with bias correction from GWA on different spatial levels – and for regions outside of Europe, China or the United States. This study therefore evaluates different methods for wind power simulation on four spatial resolution levels from wind park to national level in Brazil. In particular, spatial interpolation methods and spatial as well as spatiotemporal wind speed bias correction using local wind speed measurements and mean wind speeds from the GWA are assessed. By validating the resulting timeseries against observed generation it is assessed at which spatial levels the different methods improve results – and whether global information derived from the GWA can compete with locally measured wind speed data as a source of bias correction. Results show that (i) bias correction with the GWA improves results on state, sub-system, and national-level, but not on wind park level, that (ii) the GWA improves results comparably to local measurements, and that (iii) complex spatial interpolation methods do not contribute in improving quality of the simulation.

Suggested Citation

  • Gruber, Katharina & Klöckl, Claude & Regner, Peter & Baumgartner, Johann & Schmidt, Johannes, 2019. "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil," Energy, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:energy:v:189:y:2019:i:c:s0360544219319073
    DOI: 10.1016/j.energy.2019.116212
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    Cited by:

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    2. Johann Baumgartner & Katharina Gruber & Sofia G. Simoes & Yves-Marie Saint-Drenan & Johannes Schmidt, 2020. "Less Information, Similar Performance: Comparing Machine Learning-Based Time Series of Wind Power Generation to Renewables.ninja," Energies, MDPI, vol. 13(9), pages 1-23, May.
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    4. de Aquino Ferreira, Saulo Custodio & Cyrino Oliveira, Fernando Luiz & Maçaira, Paula Medina, 2022. "Validation of the representativeness of wind speed time series obtained from reanalysis data for Brazilian territory," Energy, Elsevier, vol. 258(C).
    5. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    6. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    7. Pflugfelder, Yannik & Kramer, Hendrik & Weber, Christoph, 2024. "A novel approach to generate bias-corrected regional wind infeed timeseries based on reanalysis data," Applied Energy, Elsevier, vol. 361(C).
    8. Castro, Gabriel Malta & Klöckl, Claude & Regner, Peter & Schmidt, Johannes & Pereira, Amaro Olimpio, 2022. "Improvements to Modern Portfolio Theory based models applied to electricity systems," Energy Economics, Elsevier, vol. 111(C).
    9. Hai Lin & Yi Yang & Shuguang Wang & Shuyu Wang & Jianping Tang & Guangtao Dong, 2023. "Evaluation of MSWX Bias-Corrected Meteorological Forcing Datasets in China," Sustainability, MDPI, vol. 15(12), pages 1-16, June.
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    12. Luzia, Graziela & Koivisto, Matti J. & Hahmann, Andrea N., 2023. "Validating EURO-CORDEX climate simulations for modelling European wind power generation," Renewable Energy, Elsevier, vol. 217(C).
    13. Murcia, Juan Pablo & Koivisto, Matti Juhani & Luzia, Graziela & Olsen, Bjarke T. & Hahmann, Andrea N. & Sørensen, Poul Ejnar & Als, Magnus, 2022. "Validation of European-scale simulated wind speed and wind generation time series," Applied Energy, Elsevier, vol. 305(C).
    14. Prasad, Abhnil Amtesh & Yang, Yuqing & Kay, Merlinde & Menictas, Chris & Bremner, Stephen, 2021. "Synergy of solar photovoltaics-wind-battery systems in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    15. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    16. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    17. José Rafael Dorrego Portela & Geovanni Hernández Galvez & Quetzalcoatl Hernandez-Escobedo & Ricardo Saldaña Flores & Omar Sarracino Martínez & Orlando Lastres Danguillecourt & Pascual López de Paz & A, 2022. "Microscale Wind Assessment, Comparing Mesoscale Information and Observed Wind Data," Sustainability, MDPI, vol. 14(19), pages 1-12, September.
    18. Erika Carvalho Nogueira & Rafael Cancella Morais & Amaro Olimpio Pereira, 2023. "Offshore Wind Power Potential in Brazil: Complementarity and Synergies," Energies, MDPI, vol. 16(16), pages 1-18, August.
    19. Gabriel Malta Castro & Claude Klockl & Peter Regner & Johannes Schmidt & Amaro Olimpio Pereira Jr, 2021. "Improvements to Modern Portfolio Theory based models applied to electricity systems," Papers 2105.08182, arXiv.org.
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