EGU2020-19913
https://doi.org/10.5194/egusphere-egu2020-19913
EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Modeling of the power generation from wind turbines with high spatial and temporal resolution

Reinhold Lehneis1, David Manske1, Björn Schinkel1, and Daniela Thrän1,2
Reinhold Lehneis et al.
  • 1Department of Bioenergy, Helmholtz Centre for Environmental Research GmbH - UFZ, Permoserstraße 15, 04318 Leipzig, Germany
  • 2Bioenergy Systems Department, DBFZ Deutsches Biomasseforschungszentrum gGmbH, Torgauer Str. 116, 04347 Leipzig, Germany

The share of wind power in the generation of electricity has increased significantly in recent years and, despite its volatility, variable energy from wind turbines has become an essential pillar for the power supply in many countries around the world. To investigate the effects of increasing variable renewables on power grids, the environment or electricity markets, detailed power generation data from wind turbines with high spatial and temporal resolution are often mandatory. The lack of freely accessible feed-in time series, for example due to data protection regulations, makes it necessary to determine the wind power feed-in for a required region and period with the help of numerical simulations. Our contribution demonstrates how such a numerical simulation can be developed using publicly available wind turbine and weather data. Herein, a novel model approach will be presented for the wind-to-power conversion, which utilizes a sixth-order polynomial for the specific power curve of a wind turbine. After such an analytical representation is derived for a certain turbine, its output power can be easily calculated using the wind speed and air temperature at its hub height. For proof of concept and model validation, measured feed-in time series of a geographically and technically known wind turbine are compared with the simulated time series at a high temporal resolution of 10 minutes. In order to determine the power generation for larger regions or an entire country the derived numerical simulation is also carried out for an ensemble of almost 26 thousand onshore wind turbines in Germany with a total capacity of about 44 GW. With this ensemble, first simulation results with municipal and hourly resolution can be presented for an annual period.

How to cite: Lehneis, R., Manske, D., Schinkel, B., and Thrän, D.: Modeling of the power generation from wind turbines with high spatial and temporal resolution, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19913, https://doi.org/10.5194/egusphere-egu2020-19913, 2020

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Display material version 1 – uploaded on 06 May 2020
  • CC1: Comment on EGU2020-19913, Wolf-Gerrit Fruh, 07 May 2020

    Thank you very much for the clear poster - very interesting and useful work.

    In addition to the straight output of your model, it would be interesting to explore how sensitive the results are to some of the (fairly severe but necessary) assumptions are.  I suspect that the largest uncertainties would come from the wind speed extrapolation (how did you do that?), and then the temperature extrapolation and the 6th-order polynomial.   Why did you choose a 6th-order polynomial instead of interpolation?  Was that to speed up the calculations? 

    Knowing the sensitivity of the model to those assumptions might also give us a good handle of which spatial and temporal resolution is commensurate with the model's accuracy and reliability.

    • AC1: Reply to CC1, Reinhold Lehneis, 08 May 2020

      Dear Wolf-Gerrit Fruh,

      Thank you for the kind comments on our poster and your questions about the simulation model.

      In our simulation model we use a 6th-order polynomial for the nonlinear section of a power curve, which is usually given by the manufacturer as a table of discrete values. This 6th-order polynomial is a very accurate approximation for the nonlinear part (R² > 0.99). The other parts of the power curve are described with constant values, e.g. the rated power or zero output (for wind speeds below/above the cut-in/cut-out speed). An advantage of our model approach is, that no complex and, therefore, runtime extending interpolation routines are required to calculate the output power for arbitrary wind speeds. The extrapolation of the wind speed to the hub height is performed with the help of the Hellmann’s law.

      As in many simulation models that use weather data as input, you have various uncertainties, e.g.:
       -  the rough estimation of the wind speed at the hub height using the Hellmann's exponential law;
       -  uncertainties of the provided weather data and, of course, the fact of hourly averaged values;
       -  power switch-offs due to energy surpluses in the power grids, wake effects, bat protection, ...

      We use a constant factor (the same value over an entire simulation year) in our simulations to take such effects into account and to bring the measured and simulated annual amounts of wind power production to the same value. 

      If you have further questions, don't hesitate to ask.

      Have a nice weekend and best regards

      Reinhold Lehneis