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Frequency and duration of low-wind-power events in Germany

Author

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  • Ohlendorf, Nils
  • Schill, Wolf-Peter

Abstract

In the transition to a renewable energy system, the occurrence of low-wind-power events receives increasing attention. We analyze the frequency and duration of such events for onshore wind power in Germany, based on 40 years of reanalysis data and open software. We find that low-wind-power events are less frequent in winter than in summer, but the maximum duration is distributed more evenly between months. While short events are frequent, very long events are much rarer. Every year, a period of around five consecutive days with an average wind capacity factor below 10% occurs, and every ten years a respective period of nearly eight days. These durations decrease if only winter months are considered. The longest event in the data lasts nearly ten days. We conclude that public concerns about low-wind-power events in winter may be overrated, but recommend that modeling studies consider multiple weather years to properly account for such events.

Suggested Citation

  • Ohlendorf, Nils & Schill, Wolf-Peter, 2020. "Frequency and duration of low-wind-power events in Germany," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 15(8).
    • Handle: RePEc:zbw:espost:224469
    DOI: 10.1088/1748-9326/ab91e9
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    Cited by:

    1. Otero, Noelia & Martius, Olivia & Allen, Sam & Bloomfield, Hannah & Schaefli, Bettina, 2022. "A copula-based assessment of renewable energy droughts across Europe," Renewable Energy, Elsevier, vol. 201(P1), pages 667-677.
    2. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
    3. Lorin Jenkel & Stefan Jonas & Angela Meyer, 2023. "Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning," Energies, MDPI, vol. 16(17), pages 1-29, September.
    4. Orszaghova, J. & Lemoine, S. & Santo, H. & Taylor, P.H. & Kurniawan, A. & McGrath, N. & Zhao, W. & Cuttler, M.V.W., 2022. "Variability of wave power production of the M4 machine at two energetic open ocean locations: Off Albany, Western Australia and at EMEC, Orkney, UK," Renewable Energy, Elsevier, vol. 197(C), pages 417-431.
    5. François, B. & Puspitarini, H.D. & Volpi, E. & Borga, M., 2022. "Statistical analysis of electricity supply deficits from renewable energy sources across an Alpine transect," Renewable Energy, Elsevier, vol. 201(P1), pages 1200-1212.
    6. António Couto & Paula Costa & Teresa Simões, 2021. "Identification of Extreme Wind Events Using a Weather Type Classification," Energies, MDPI, vol. 14(13), pages 1-16, July.
    7. Nycander, Elis & Morales-España, Germán & Söder, Lennart, 2022. "Power-based modelling of renewable variability in dispatch models with clustered time periods," Renewable Energy, Elsevier, vol. 186(C), pages 944-956.
    8. 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).
    9. Gangopadhyay, A. & Seshadri, A.K. & Sparks, N.J. & Toumi, R., 2022. "The role of wind-solar hybrid plants in mitigating renewable energy-droughts," Renewable Energy, Elsevier, vol. 194(C), pages 926-937.
    10. Pickering, Bryn & Choudhary, Ruchi, 2021. "Quantifying resilience in energy systems with out-of-sample testing," Applied Energy, Elsevier, vol. 285(C).
    11. Nouri, Milad & Homaee, Mehdi, 2022. "Reference crop evapotranspiration for data-sparse regions using reanalysis products," Agricultural Water Management, Elsevier, vol. 262(C).

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