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

Ohlendorf, Nils, Schill, Wolf-Peter·2020·Environmental Research Letters
DOI 10.1088/1748-9326/ab91e9
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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.

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  • Wind droughts are more severe in summer than in winter.

    “Wind droughts are more frequent and severe in summer than in winter33,44,47.”

    From Long-duration electricity storage needs for coping with Dunkelflaute events in Europe (2026) · cites this paper as [44]

    Section 3.2 says there is no clear seasonal trend in the most extreme low-wind-power events; under one event definition, the longest winter events even exceed those in summer. The paper finds summer events more frequent, but that does not establish the attributed claim that they are more severe. (LLM, conf=0.86)

    Checked by a reasoning model — may be wrong.

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  • Wind droughts are more frequent in summer than in winter.

    “Wind droughts are more frequent and severe in summer than in winter33,44,47.”

    From Long-duration electricity storage needs for coping with Dunkelflaute events in Europe (2026) · cites this paper as [44]

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Research works printed but not linked: 34

  1. Archer, C.L., Jacobson, M.Z., 2007. Supplying baseload power and reducing transmission requirements by interconnecting wind farms. Journal of Applied Meteorology and Climatology 46, 1701-1717. doi:10.1175/2007JAMC1538. · DOI
  2. BMWi, 2019. Zeitreihen zur Entwicklung der erneuerbaren Energien in Deutsch- land. Bundesministerium für Wirtschaft und Energie (Federal Ministry for Economic Affairs and Energy). URL: https://www.erneuerbare-energien .de/EE/Redaktion/DE/Downloads/zeitreihen-zur-entwicklung-der-e rneuerbaren-energien-in-deutschland-1990-2018.pdf. · DOI
  3. Cannon, D., Brayshaw, D., Methven, J., Coker, P., Lenaghan, D., 2015. Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain. Renewable Energy 75, 767 -778. doi:10.1 016/j.renene.2014.10.024. · DOI · Paper page
  4. Carvalho, D., Rocha, A., Gómez-Gesteira, M., Santos, C.S., 2014. WRF wind simulation and wind energy production estimates forced by different reanal- yses: Comparison with observed data for Portugal. Applied Energy 117, 116 -126. doi:10.1016/j.apenergy.2013.12.001. · DOI
  5. Collins, S., Deane, P., Ó Gallachóir, B., Pfenninger, S., Staffell, I., 2018. Impacts of inter-annual wind and solar variations on the European power system. Joule 2, 2076-2090. doi:10.1016/j.joule.2018.06.020. · DOI · Paper page
  6. Decker, M., Brunke, M.A., Wang, Z., Sakaguchi, K., Zeng, X., Bosilovich, M.G., 2012. Evaluation of the Reanalysis Products from GSFC, NCEP, and ECMWF Using Flux Tower Observations. Journal of Climate 25, 1916-1944. doi:10.1175/JCLI-D-11-00004. · DOI
  7. Deutscher Bundestag (Ed.), 2019a. Plenarprotokoll 19/98 Stenografischer Bericht 98. Sitzung. Plenarprotokoll 19/98. URL: http://dip21.bundes tag.de/dip21/btp/19/19098.pdf. 09.05.2019. · DOI
  8. Deutscher Bundestag (Ed.), 2019b. Unterrichtung durch die Bundesregierung Zweiter Fortschrittsbericht zur Energiewende 2019. Drucksache 19/10760. Drucksache 19/10760 19. Wahlperiode. URL: http://dip21.bundestag. de/dip21/btd/19/107/1910760.pdf. 07.06.2019.
  9. Gelaro, R., McCarty, W., Surez, M.J., Todling, R., Molod, A., Takacs, L., Randles, C.A., Darmenov, A., Bosilovich, M.G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A.M., Gu, W., Kim, G.K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J.E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S.D., Sienkiewicz, M., Zhao, B., 2017. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate 30, 5419-5454. doi:10.1175/JCLI-D-16-0758. · DOI
  10. Germer, S., Kleidon, A., 2019. Have wind turbines in germany generated elec- tricity as would be expected from the prevailing wind conditions in 2000-2014? PLOS ONE 14, 1-16. doi:10.1371/journal.pone.0211028. · DOI
  11. González-Aparicio, I., Monforti, F., Volker, P., Zucker, A., Careri, F., Huld, T., Badger, J., 2017. Simulating European wind power generation applying statistical downscaling to reanalysis data. Applied Energy 199, 155 -168. doi:10.1016/j.apenergy.2017.04.066. · DOI
  12. Grams, C.M., Beerli, R., Pfenninger, S., Staffell, I., Wernli, H., 2017. Balancing Europe's wind-power output through spatial deployment informed by weather regimes. Nature Climate Change 7, 557-562. doi:10.1038/nclimate3338. · DOI
  13. Handschy, M.A., Rose, S., Apt, J., 2017. Is it always windy somewhere? Oc- currence of low-wind-power events over large areas. Renewable Energy 101, 1124 -1130. doi:10.1016/j.renene.2016.10.004. · DOI
  14. Kahn, E., 1979. The reliability of distributed wind generators. Electric Power Systems Research 2, 1 -14. doi:10.1016/0378-7796(79)90021-X. · DOI
  15. Kruyt, B., Lehning, M., Kahl, A., 2017. Potential contributions of wind power to a stable and highly renewable Swiss power supply. Applied Energy 192, 1 -11. doi:10.1016/j.apenergy.2017.01.085. · DOI
  16. Kumler, A., Carreño, I.L., Craig, M.T., Hodge, B.M., Cole, W., Brancucci, C., 2019. Inter-annual variability of wind and solar electricity generation and capacity values in Texas. Environmental Research Letters 14, 044032. doi:10.1088/1748-9326/aaf935. · DOI
  17. Leahy, P.G., McKeogh, E.J., 2013. Persistence of low wind speed conditions and implications for wind power variability. Wind Energy 16, 575-586. doi:10.1 002/we.1509. · DOI
  18. Liléo, S., Petrik, O., 2011. Investigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis, in: European Wind Energy Conference and Exhibition 2011, EWEC 2011.
  19. Moemken, J., Reyers, M., Feldmann, H., Pinto, J.G., 2018. Future changes of wind speed and wind energy potentials in EURO-CORDEX ensemble sim- ulations. Journal of Geophysical Research: Atmospheres 123, 6373-6389. doi:10.1029/2018JD028473. · DOI
  20. Molod, A., Takacs, L., Suarez, M., Bacmeister, J., 2015. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geoscientific Model Development 8, 1339-1356. doi:10.5194/gm d-8-1339-2015. · DOI
  21. Olauson, J., Bergkvist, M., 2015. Modelling the Swedish wind power production using MERRA reanalysis data. Renewable Energy 76, 717 -725. doi:10.101 6/j.renene.2014.11.085. · DOI
  22. Patlakas, P., Galanis, G., Diamantis, D., Kallos, G., 2017. Low wind speed events: persistence and frequency. Wind Energy 20, 1033-1047. doi:10.100 2/we.2078. · DOI
  23. Raynaud, D., Hingray, B., François, B., Creutin, J., 2018. Energy droughts from variable renewable energy sources in European climates. Renewable Energy 125, 578 -589. doi:10.1016/j.renene.2018.02.130. · DOI · Paper page
  24. Rose, S., Apt, J., 2015. What can reanalysis data tell us about wind power? Renewable Energy 83, 963 -969. doi:10.1016/j.renene.2015.05.027. · DOI
  25. Santos-Alamillos, F.J., Brayshaw, D.J., Methven, J., Thomaidis, N.S., Ruiz- Arias, J.A., Pozo-Vázquez, D., 2017. Exploring the meteorological potential for planning a high performance European electricity super-grid: optimal power capacity distribution among countries. Environmental Research Letters 12, 114030. doi:10.1088/1748-9326/aa8f18. · DOI
  26. Schill, W.P., Zerrahn, A., 2018. Long-run power storage requirements for high shares of renewables: Results and sensitivities. Renewable and Sustainable Energy Reviews 83, 156 -171. doi:10.1016/j.rser.2017.05.205. · DOI
  27. Schlott, M., Kies, A., Brown, T., Schramm, S., Greiner, M., 2018. The impact of climate change on a cost-optimal highly renewable European electricity network. Applied Energy 230, 1645 -1659. doi:10.1016/j.apenergy.2018. 09.084. · DOI
  28. Shaner, M.R., Davis, S.J., Lewis, N.S., Caldeira, K., 2018. Geophysical con- straints on the reliability of solar and wind power in the United States. Energy and Environmental Science 11, 914-925. doi:10.1039/C7EE03029K. · DOI · Paper page
  29. Sharp, E., Dodds, P., Barrett, M., Spataru, C., 2015. Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information. Renewable Energy 77, 527 - 538. doi:10.1016/j.renene.2014.12.025. · DOI
  30. Staffell, I., Green, R., 2014. How does wind farm performance decline with age? Renewable Energy 66, 775 -786. doi:10.1016/j.renene.2013.10.041. · DOI
  31. Staffell, I., Pfenninger, S., 2016. Using bias-corrected reanalysis to simulate current and future wind power output. Energy 114, 1224 -1239. doi:10.101 6/j.energy.2016.08.068. · DOI
  32. Tobin, I., Jerez, S., Vautard, R., Thais, F., van Meijgaard, E., Prein, A., Déqué, M., Kotlarski, S., Maule, C.F., Nikulin, G., Nol, T., Teichmann, C., 2016. Climate change impacts on the power generation potential of a European mid-century wind farms scenario. Environmental Research Letters 11, 034013. doi:10.1088/1748-9326/11/3/034013. · DOI
  33. Weber, J., Reyers, M., Beck, C., Timme, M., Pinto, J.G., Witthaut, D., Schäfer, B., 2019. Wind power persistence characterized by superstatistics. Scientific Reports 9, 19971-. doi:10.1038/s41598-019-56286-1. · DOI
  34. Wiese, F., Schlecht, I., Bunke, W.D., Gerbaulet, C., Hirth, L., Jahn, M., Kunz, F., Lorenz, C., Mhlenpfordt, J., Reimann, J., Schill, W.P., 2019. Open Power System Data Frictionless data for electricity system modelling. Applied Energy 236, 401 -409. doi:10.1016/j.apenergy.2018.11.097. · DOI

Cited by

Sorted by citations

Authors
Ohlendorf, N., & Schill, W.-P.
Venue
Environmental Research Letters
Publisher
IOP Publishing
Published
2020-08-01
Volume
15
Issue
8
Pages
084045
ISSN
1748-9326
DOI
10.1088/1748-9326/ab91e9
Cited by
98
References
0
Licence
Funders
Bundesministerium für Bildung und Forschung (FKZ 03SFK2B1)

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