Observer-based power forecast of individual and aggregated offshore wind turbines

Abstract

Abstract. Due to the increasing share of wind energy in the power system, minute-scale wind power forecasts have gained importance. Remote sensing-based approaches have proven to be a promising alternative to statistical methods and thus need to be further developed towards an operational use, aiming to increase their forecast availability and skill. Therefore, the contribution of this paper is to extend lidar-based forecasts to a methodology for observer-based probabilistic power forecasts of individual wind turbines and aggregated wind farm power. To do so, lidar-based forecasts are combined with SCADA-based forecasts that advect wind vectors derived from wind turbine operational data. After a calibration, forecasts of individual turbines are aggregated to a probabilistic power forecast of turbine subsets by means of a copula approach. We found that combining the lidar- and SCADA-based forecasts significantly improved both forecast skill and forecast availability of a 5-minute ahead probabilistic power forecast at an offshore wind farm. Calibration further increased the forecast skill. Calibrated observer-based forecasts outperformed the benchmark persistence for unstable atmospheric conditions. The aggregation of probabilistic forecasts of turbine subsets revealed the potential of the copula approach. We discuss the skill, robustness and dependency on atmospheric conditions of the individual forecasts, the value of the observer-based forecast, its calibration and aggregation and more generally the value of minute-scale power forecasts of offshore wind. In conclusion, combining different data sources to an observer-based forecast is beneficial in all regarded cases. For an operational use one should distinguish between and adapt to atmospheric stability.

Hybrid use of an observer-based minute-scale power forecast and persistence

“…The weighting method shows an improved skill compared to the OF, while the binary method shows scores similar to the OF, outperforming it in few cases. Similar as observed in previous work persistence outperforms the OF during stable stratification [6,10]. For most turbines, the skill of the binary approach is lower than that of persistence but higher than that of the OF.…”

“…Here, we additionally propagated high-frequency wind speed and wind direction information obtained from GT I's wind turbine operational data as suggested by [9]. Wind vectors based on SCADA data were weighted according to their age and bias-corrected to account for wake effects [10]. Lidar and SCADA contributions were resampled to the same number of wind vectors and weighted equally.…”

“…We will refer to this approach as observer-based forecast in the following. Both the data set and the forecasting methodology are described in more detail in [6] and [10].…”

“…As the atmospheric conditions were found to have a strong impact on the skill of the LF and OF and their performance in comparison to persistence [6,10], we developed a hybrid methodology that aims to consider these different conditions. We used the Obukhov length L, the wind speed at 100 m height u 100 m and the wind direction at 100 m height χ 100 m extracted with 10 minute resolution from a WRF (Weather Research and Forecasting Model) simulation at the location of the offshore wind farm Global Tech I [13].…”

“…However, also lower forecast skills were observed for other conditions [6]. A recent study that has extended lidar-based forecasts to observer-based forecasts (OF) by embedding also wind turbine operational data confirmed the superiority of persistence in particular during stable stratification [9,10]. This suggests using observer-based methods as a complement to persistence rather than a substitute.…”