Lidar-based minute-scale offshore wind speed forecasts analysed under different atmospheric conditions

Kurzfassung

In recent years, the potential of remote sensing-based minute-scale forecasts to improve the integration of wind power into our energy system has been shown. In lidar-based forecasts, the wind speed is extrapolated from the measuring to the forecast height, i.e. the wind turbines hub height, by assuming a stability-corrected logarithmic wind profile. The objective of this paper is the significant reduction of large forecasting errors associated with the height extrapolation. Hence, we introduce two new approaches and characterise their skill under different atmospheric conditions. The first one is based on an empirical set of parameters derived from lidar data and operational wind turbine data. The second approach derives the wind speed tendency of two consecutive forecasts at the measuring height and applies this to operational wind speed data at hub height. We identified the uncertainty in stability estimates and measurement height as the main cause for large extrapolation errors of the existing lidar-based forecast. Monte Carlo simulations revealed the new approaches low sensitivity to uncertainty in lidar data processing, propagation and height extrapolation. Forecasting errors of a 5-minute-ahead wind speed forecast of free flow turbines at an offshore wind farm were significantly reduced for the two newly developed methods as compared to the existing forecast during stable atmospheric conditions. Persistence could be outperformed during unstable and neutral atmospheric conditions and for situations with higher turbulence intensity. Overall, we found lidar-based forecasts to be less sensitive to atmospheric conditions than persistence. We discuss the importance of accurate vertical wind speed profile estimation, the advantages and shortcomings of the two newly introduced methods and their skill compared to persistence. In conclusion, the additional use of wind turbine operational data can significantly improve minute-scale lidar-based forecasts. We further conclude that the characterisation of forecast skill dependent on atmospheric conditions can be valuable for decision-making processes.