IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v361y2024ics0306261924002733.html
   My bibliography  Save this article

A novel approach to generate bias-corrected regional wind infeed timeseries based on reanalysis data

Author

Listed:
  • Pflugfelder, Yannik
  • Kramer, Hendrik
  • Weber, Christoph

Abstract

Weather data, particularly from reanalysis models, are often applied in simulations of infeed patterns for renewable energy systems. The reanalysis datasets provide spatially differentiated weather timeseries for historical years. However, their exactness in wind power applications deserves detailed scrutiny. Notably, the physical model abstracts from boundary layer friction. Abstaining from physical flow models, scientific scholars proposed ex-post bias correction methods to better depict local wind speeds. Yet, such bias correction often is performed on national aggregated figures, as public data is scarce. In this work, a dataset of approx. 23,000 wind turbines for Germany is used to assess deviations between simulated and measured energy infeed for four different years. In line with other studies, we identify in detail that in flat terrain, simulations based on reanalysis data overestimate measured results. In topographically complex regions, a minor overestimation and occasionally an underestimation can be observed. Multilinear regression at turbine level shows that these deviations can be explained by local factors. Reanalysis data in combination with bias-correction based on local factors from 2016 enhance energy output simulations on turbine level on average by 71% for 2020, 93% for 2021 and 97% for 2022.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002733
    DOI: 10.1016/j.apenergy.2024.122890
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924002733
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2024.122890?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Hirth, Lion & Mühlenpfordt, Jonathan & Bulkeley, Marisa, 2018. "The ENTSO-E Transparency Platform – A review of Europe’s most ambitious electricity data platform," Applied Energy, Elsevier, vol. 225(C), pages 1054-1067.
    3. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    4. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren, 2017. "Overview of wind power intermittency: Impacts, measurements, and mitigation solutions," Applied Energy, Elsevier, vol. 204(C), pages 47-65.
    5. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    6. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. 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).
    3. Kies, Alexander & Schyska, Bruno U. & Bilousova, Mariia & El Sayed, Omar & Jurasz, Jakub & Stoecker, Horst, 2021. "Critical review of renewable generation datasets and their implications for European power system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    4. 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).
    5. 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).
    6. 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).
    7. Gruber, Katharina & Regner, Peter & Wehrle, Sebastian & Zeyringer, Marianne & Schmidt, Johannes, 2022. "Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas," Energy, Elsevier, vol. 238(PA).
    8. Houndekindo, Freddy & Ouarda, Taha B.M.J., 2024. "Prediction of hourly wind speed time series at unsampled locations using machine learning," Energy, Elsevier, vol. 299(C).
    9. 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.
    10. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    11. Gabrielli, Paolo & Aboutalebi, Reyhaneh & Sansavini, Giovanni, 2022. "Mitigating financial risk of corporate power purchase agreements via portfolio optimization," Energy Economics, Elsevier, vol. 109(C).
    12. Reinhold Lehneis & Daniela Thrän, 2023. "Temporally and Spatially Resolved Simulation of the Wind Power Generation in Germany," Energies, MDPI, vol. 16(7), pages 1-16, April.
    13. Onodera, Hiroaki & Delage, Rémi & Nakata, Toshihiko, 2024. "The role of regional renewable energy integration in electricity decarbonization—A case study of Japan," Applied Energy, Elsevier, vol. 363(C).
    14. Louise Christine Dammeier & Joyce H. C. Bosmans & Mark A. J. Huijbregts, 2023. "Variability in greenhouse gas footprints of the global wind farm fleet," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 272-282, February.
    15. Zhang, Juntao & Cheng, Chuntian & Yu, Shen, 2024. "Recognizing the mapping relationship between wind power output and meteorological information at a province level by coupling GIS and CNN technologies," Applied Energy, Elsevier, vol. 360(C).
    16. Nouri, Milad & Homaee, Mehdi, 2022. "Reference crop evapotranspiration for data-sparse regions using reanalysis products," Agricultural Water Management, Elsevier, vol. 262(C).
    17. 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.
    18. Berger, Mathias & Radu, David & Fonteneau, Raphaël & Henry, Robin & Glavic, Mevludin & Fettweis, Xavier & Le Du, Marc & Panciatici, Patrick & Balea, Lucian & Ernst, Damien, 2020. "Critical time windows for renewable resource complementarity assessment," Energy, Elsevier, vol. 198(C).
    19. 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).
    20. Wiese, Frauke & Schlecht, Ingmar & Bunke, Wolf-Dieter & Gerbaulet, Clemens & Hirth, Lion & Jahn, Martin & Kunz, Friedrich & Lorenz, Casimir & Mühlenpfordt, Jonathan & Reimann, Juliane & Schill, Wolf-P, 2019. "Open Power System Data – Frictionless data for electricity system modelling," Applied Energy, Elsevier, vol. 236(C), pages 401-409.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:361:y:2024:i:c:s0306261924002733. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.