4.6 Very Short-Term Wind Power Forecasting Using Remote Sensing Data: Experiments with a LIDAR Simulator

Tuesday, 9 January 2018: 9:45 AM
Room 15 (ACC) (Austin, Texas)
Laura Valldecabres Sanmartin, Forwind, Univ. of Oldenburg, Oldenburg, Germany; and G. Steinfeld, L. von Bremen, and M. Kühn

The increasing integration of offshore wind power into the grid implies new challenges for transmission system operators, since they have to deal with short-term power fluctuations. To improve the flexibility of the grid, accurate very short-term predictions of wind energy are necessary. Over the last few years much interest has been put on using remote sensing technologies such as long-range dual-Doppler LIDARs to better understand the inflow characteristics of a wind farm. LIDARs are powerful devices to measure the wind speed and direction and have been proven to be relevant for forecasting wind speed ([1] and [2]). In this contribution we use a LIDAR simulator [3], combined with an advection forecasting technique to predict hub height wind speeds and power output of an offshore wind turbine in a very short term horizon of 10 minutes. Flow fields are obtained from two Large Eddy Simulations (LES) with unstable and neutral conditions. Those simulations have nearly the same wind speed at hub height, which is below the wind turbine rated wind speed. An enhanced Actuator Disc Model with Rotation (ADM-R) [4] is used to simulate the effects of the wind turbine on the flow. The characteristics of the wind turbine are based on the NREL offshore 5-MW reference turbine. The LIDAR simulator, which is located at the nacelle of the wind turbine, scans the inflow in the so-called Plan Position Indicator trajectory. With this trajectory wind conditions at various predetermined ranges, i.e up to several kilometers from the wind turbine can be measured. These upstream simulated LIDAR observations will be combined with an advection forecasting technique to predict the wind speed at hub height. The influence of the inherent LIDAR spatial and temporal averaging effects on the retrieval data will be analyzed. Thus, a parameter study of the PPI trajectories (scanning sector, accumulation time, azimuthal increment) will be conducted. The turbine power curve will be used to transform the wind speed forecasts into power forecasts. To evaluate the accuracy of the forecast, the power generated by the wind turbine model in the LES will be used as a reference. Results on the forecast error for the two stability conditions and different forecasting horizons, ranging from 1 to 10 minutes will be presented, along with comparisons of the forecast to the baseline persistence model.

[1] R. Frehlich, “Scanning doppler lidar for input into short-term wind power forecasts,” J. Atmos. Ocean. Technol., 30, 2, (2013).

[2] L. Valldecabres, A. Peña, M. Courtney, L. von Bremen and M. Kühn. “Very short-term wind speed forecast of coastal flow by dual-Doppler scanning lidar.” In WESC2017, 26-29 June 2017, DTU, Copenhaguen, Denmark.

[3] M. F. van Dooren, D. Trabucchi, and M. Kühn, “A methodology for the reconstruction of 2D horizontal wind fields of wind turbinewakes based on dual-Doppler lidar measurements,” Remote Sens., 8(10), 809; 2016.

[4] L. Vollmer, G. Steinfeld, D. Heinemann, and M. Kühn, “Estimating the wake deflection downstream of a wind turbine in different atmospheric stabilities: An LES study,” Wind Energy Sci. 1, 129–141., 2016.

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