Multi-decadal offshore wind power variability can be mitigated through optimized European allocation
Charlotte Neubacher
CORRESPONDING AUTHOR
Forschungszentrum Jülich, Institute for Energy and Climate Research – Systems Analysis and Technology Evaluation (IEK-STE), 52428 Jülich, Germany
Forschungszentrum Jülich, Institute for Energy and Climate Research – Troposphere (IEK-8), 52428 Jülich, Germany
Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany
Dirk Witthaut
Forschungszentrum Jülich, Institute for Energy and Climate Research – Systems Analysis and Technology Evaluation (IEK-STE), 52428 Jülich, Germany
Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany
Jan Wohland
Climate Policy Group, Department of Environmental Systems Science, ETH Zürich, 8092 Zürich, Switzerland
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Wind park planning and power system design require robust wind resource information. While most assessments are restricted to the last four decades, we use centennial reanalyses to study wind energy generation variability in Germany. We find that statistically significant multi-decadal variability exists. These long-term effects must be considered when planning future highly renewable power systems. Otherwise, there is a risk of inefficient system design and ill-informed investments.
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Cited articles
Anvari, M., Lohmann, G., Wächter, M., Milan, P., Lorenz, E., Heinemann, D., Tabar, M. R. R., and Peinke, J.: Short term fluctuations of wind and solar power systems, New J. Phys., 18, 063027,
https://doi.org/10.1088/1367-2630/18/6/063027, 2016. a
Bett, P. E., Thornton, H. E., and Clark, R. T.: European wind variability over 140 yr, Adv. Sci. Res., 10, 51–58, https://doi.org/10.5194/asr-10-51-2013, 2013. a
Bett, P. E., Thornton, H. E., and Clark, R. T.: Using the Twentieth Century
Reanalysis to assess climate variability for the European wind industry,
Theor. Appl. Clim., 127, 61–80,
https://doi.org/10.1007/s00704-015-1591-y, 2017. a
Bloomfield, H., Brayshaw, D. J., Shaffrey, L., Coker, P. J., and Thornton,
H. E.: The changing sensitivity of power systems to meteorological drivers: a case study of Great Britain, Environ. Res. Lett., 13, 054028,
https://doi.org/10.1088/1748-9326/aabff9, 2018. a
BMWi, Bundesministerium f. E. u. W.: Mehr Strom vom Meer – 20 Gigawatt
Offshore-Windenergie bis 2030 realisieren, available at:
https://www.bmwi.de/Redaktion/DE/Downloads/M-O/offshore-vereinbarung-mehr-strom-vom-meer.pdf?__blob=publicationFile&v=6,
last access: 15 May 2020. a
Brayshaw, D. J., Troccoli, A., Fordham, R., and Methven, J.: The impact of
large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK, Renew. Energ., 36, 2087–2096, https://doi.org/10.1016/j.renene.2011.01.025, 2011. a
Broomhead, D. S. and King, G. P.: Extracting qualitative dynamics from
experimental data, Physica D, 20, 217–236,
https://doi.org/10.1016/0167-2789(86)90031-X, 1986. a, b
Collins, S., Deane, P., Gallachóir, B. Ó., Pfenninger, S., and
Staffell, I.: Impacts of inter-annual wind and solar variations on the
European power system, Joule, 2, 2076–2090,
https://doi.org/10.1016/j.joule.2018.06.020, 2018. a
ECMWF: CERA-20C Data Set,
available at: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/cera-20c,
last access: 4 September 2019. a
Ely, C. R., Brayshaw, D. J., Methven, J., Cox, J., and Pearce, O.: Implications
of the North Atlantic Oscillation for a UK–Norway renewable power system,
Energ. Policy, 62, 1420–1427, https://doi.org/10.1016/j.enpol.2013.06.037, 2013. a
Faulwasser, T., Engelmann, A., Mühlpfordt, T., and Hagenmeyer, V.: Optimal
power flow: an introduction to predictive, distributed and stochastic control challenges, At.-Autom., 66, 573–589,
https://doi.org/10.1515/auto-2018-0040, 2018. a
Foley, A. M., Leahy, P. G., Marvuglia, A., and McKeogh, E. J.: Current methods and advances in forecasting of wind power generation, Renew. Energ., 37, 1–8, https://doi.org/10.1016/j.renene.2011.05.033, 2012. a
Ghil, M.: The SSA-MTM Toolkit: Applications to analysis and prediction of time series, Proc. SPIE, 3165, 216–230, https://doi.org/10.1117/12.279594,
1997. a
Ghil, M., Allen, M., Dettinger, M., Ide, K., Kondrashov, D., Mann, M.,
Robertson, A. W., Saunders, A., Tian, Y., Varadi, F., and Yiou, P.: Advanced
spectral methods for climatic time series, Rev. Geophys, 40, 3–1,
https://doi.org/10.1029/2000RG000092, 2002. a, b, c
Gorjão, L. R., Anvari, M., Kantz, H., Beck, C., Witthaut, D., Timme, M.,
and Schäfer, B.: Data-driven model of the power-grid frequency dynamics, IEEE Access, 8, 43082–43097, https://doi.org/10.1109/ACCESS.2020.2967834, 2020. a
Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I., and Wernli, H.:
Balancing Europe’s wind-power output through spatial deployment informed by
weather regimes, Nat. Clim. Change, 7, 557–562,
https://doi.org/10.1038/nclimate3338, 2017. a
Haehne, H., Schottler, J., Waechter, M., Peinke, J., and Kamps, O.: The
footprint of atmospheric turbulence in power grid frequency measurements, Europhys. Lett., 121, 30001, https://doi.org/10.1209/0295-5075/121/30001, 2018. a
Heide, D., Von Bremen, L., Greiner, M., Hoffmann, C., Speckmann, M., and
Bofinger, S.: Seasonal optimal mix of wind and solar power in a future,
highly renewable Europe, Renew. Energ., 35, 2483–2489,
https://doi.org/10.1016/j.renene.2010.03.012, 2010. a
IRENA: Renewable Power Generation Costs in 2018, available at:
https://www.irena.org/publications/2019/May/Renewable-power-generation-costs-in-2018 (last access: 15 May 2020),
2019. a
Kirchgässner, G., Wolters, J., and Hassler, U.: Introduction to modern time series analysis, Springer Science & Business Media, Heidelberg,
https://doi.org/10.1007/978-3-540-73291-4, 2012. a, b
Laloyaux, P., de Boisseson, E., Balmaseda, M., Bidlot, J.-R., Broennimann, S.,
Buizza, R., Dalhgren, P., Dee, D., Haimberger, L., Hersbach, H., Kosaka, Y.,
Martin, M.,
Poli, P.,
Rayner, N.,
Rustemeier, E., and
Schepers, D.:
CERA-20C: A coupled reanalysis of the Twentieth Century, J. Adv.
Model. Earth Sy., 10, 1172–1195, https://doi.org/10.1029/2018MS001273, 2018. a
Mann, M. E. and Lees, J. M.: Robust estimation of background noise and signal
detection in climatic time series, Clim. Change, 33, 409–445,
https://doi.org/10.1007/BF00142586, 1996. a
Marshall, J., Kushnir, Y., Battisti, D., Chang, P., Czaja, A., Dickson, R.,
Hurrell, J., McCartney, M., Saravanan, R., and Visbeck, M.: North Atlantic
climate variability: phenomena, impacts and mechanisms, Int. J. Climatol., 21, 1863–1898, https://doi.org/10.1002/joc.693, 2001. a
Milan, P., Wächter, M., and Peinke, J.: Turbulent Character of Wind Energy,
Phys. Rev. Lett., 110, 138701, https://doi.org/10.1103/PhysRevLett.110.138701, 2013. a
Omrani, N.-E., Bader, J., Keenlyside, N. S., and Manzini, E.:
Troposphere–stratosphere response to large-scale North Atlantic Ocean
variability in an atmosphere/ocean coupled model, Clim. Dynam., 46,
1397–1415, https://doi.org/10.1007/s00382-015-2654-6, 2016. a, b
Percival, D. B. and Walden, A. T.: Parametric Spectral Estimation,
Cambridge University Press, Cambridge, 391–455, https://doi.org/10.1017/CBO9780511622762.012,
1993.
a
Rayner, N., Parker, D. E., Horton, E., Folland, C. K., Alexander, L. V.,
Rowell, D., Kent, E., and Kaplan, A.: Global analyses of sea surface
temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res.-Atmos., 108, 4407,
https://doi.org/10.1029/2002JD002670, 2003. a
Rodriguez, R. A., Becker, S., Andresen, G. B., Heide, D., and Greiner, M.:
Transmission needs across a fully renewable European power system, Renew. Energ., 63, 467–476, https://doi.org/10.1016/j.renene.2013.10.005, 2014. a
Rogelj, J., Luderer, G., Pietzcker, R. C., Kriegler, E., Schaeffer, M., Krey,
V., and Riahi, K.: Energy system transformations for limiting end-of-century warming to below 1.5 ∘C, Nat. Clim. Change, 5, 519,
https://doi.org/10.1038/nclimate2572, 2015. a
Staffell, I. and Pfenninger, S.: The increasing impact of weather on
electricity supply and demand, Energy, 145, 65–78,
https://doi.org/10.1016/j.energy.2017.12.051, 2018. a
Tobin, I., Jerez, S., Vautard, R., Thais, F., van Meijgaard, E., Prein, A.,
Déqué, M., Kotlarski, S., Maule, C. F., Nikulin, G., Noël, T.,
and Teichmann, C.: Climate change impacts on the power generation potential
of a European mid-century wind farms scenario, Environ. Res.
Lett., 11, 034013, https://doi.org/10.1088/1748-9326/11/3/034013, 2016. a, b
Tröndle, T., Pfenninger, S., and Lilliestam, J.: Home-made or imported: On the possibility for renewable electricity autarky on all scales in Europe,
Energy Strateg. Rev., 26, 100388, https://doi.org/10.1016/j.esr.2019.100388, 2019. a
Vaughan, S., Bailey, R., and Smith, D.: Detecting cycles in stratigraphic data: Spectral analysis in the presence of red noise, Paleoceanography, 26, PA4211,
https://doi.org/10.1029/2011PA002195, 2011. a
Vautard, R., Yiou, P., and Ghil, M.: Singular-spectrum analysis: A toolkit for short, noisy chaotic signals, Physica D, 58, 95–126,
https://doi.org/10.1016/0167-2789(92)90103-T, 1992. a
WindGuard: Windenergie-Statistik: 1. Halbjahr, available at:
https://www.windguard.de/jahr-2019.html, last access: 18 November 2019. a
Wohland, J., Reyers, M., Märker, C., and Witthaut, D.: Natural wind
variability triggered drop in German redispatch volume and costs from 2015 to
2016, Plos One, 13, e0190707, https://doi.org/10.1371/journal.pone.0190707, 2018. a
Wohland, J., Omrani, N.-E., Witthaut, D., and Keenlyside, N. S.: Inconsistent
Wind Speed Trends in Current Twentieth Century Reanalyses, J.
Geophys. Res.-Atmos., 124, 1931–1940,
https://doi.org/10.1029/2018JD030083, 2019b. a
Short summary
In our study, we investigate the variability of potential offshore wind power over Europe on time scales of more than 10 years. Detailed spectral analysis of potential offshore wind power capacities over the last century indicates a strong coupling to large climate patterns such as the NAO. Furthermore, combining the wind power potential at the German North Sea and the Portuguese Atlantic coast shows that the variability can be mitigated.
In our study, we investigate the variability of potential offshore wind power over Europe on...