Decreasing gate closure times on the electricity stock exchange market and the rising share of renewables in today's energy system causes an increasing demand for very short-term power forecasts. While the potential of dual-Doppler radar data for that purpose was recently shown, the utilization of single-Doppler lidar measurements needs to be explored further to make remote-sensing-based very short-term forecasts more feasible for offshore sites. The aim of this work was to develop a lidar-based forecasting methodology, which addresses a lidar's comparatively low scanning speed. We developed a lidar-based forecast methodology using horizontal plan position indicator (PPI) lidar scans. It comprises a filtering methodology to recover data at far ranges, a wind field reconstruction, a time synchronization to account for time shifts within the lidar scans and a wind speed extrapolation to hub height. Applying the methodology to seven free-flow turbines in the offshore wind farm Global Tech I revealed the model's ability to outperform the benchmark persistence during unstable stratification, in terms of deterministic as well as probabilistic scores. The performance during stable and neutral situations was significantly lower, which we attribute mainly to errors in the extrapolation of wind speed to hub height.

With the increasing penetration of renewable energies in the power system, the demand for very short-term power forecasts is continuously rising. Transmission system operators (TSOs) need to ensure grid stability by balancing supply and demand of power at all times. In this regard, very short-term forecasts are an important tool to support power system management and reduce curtailment costs

While for forecast horizons of several hours or days physical models such as numerical weather prediction (NWP) models are typically used, on
shorter timescales, i.e. lead times ranging from minutes to several hours, statistical models are applied

As a promising alternative to statistical methods, recently very short-term forecasts based on remote sensing measurements have gained attention

While Doppler radars are capable of measuring in distances of up to 32 km

Our objective in this paper is to investigate whether and how one can use long-range single-Doppler lidar measurements to forecast the power of offshore wind turbines on short time horizons in a probabilistic manner. We adapt a remote-sensing-based forecast methodology to meet the requirements of single-Doppler lidar measurements. We especially implement adjustments to account for (i) low data availability in far ranges, (ii) time shifts within the lidar scans and (iii) deviations between measuring height and hub height. We validate the method by means of a case study based on measurements at an offshore wind farm and by distinguishing between different atmospheric conditions. To address their performance, we compare lidar-based forecasts against the benchmark persistence.

The paper is structured as follows: Sect.

For the purpose of forecasting, typically horizontal plan position indicator (PPI) lidar scans, i.e. with an elevation angle of

For the case study presented in Sect.

Besides horizontal PPI lidar scans (

Figure

Schematics of the lidar-based forecast methodology. Line-of-sight wind speed measurements

When performing lidar measurements several factors such as meteorological conditions, hard targets and device limitations can lead to invalid measurements. Typically, the carrier-to-noise ratio (CNR) is used as an indicator for the backscattered signal's quality. Low CNR values hereby indicate low data quality and are commonly neglected by means of threshold filters

After filtering, the global wind direction was determined by performing a VAD-like fit individually for each range gate in a certain scan. To do so, homogeneity across range gates was assumed and the vertical wind speed component neglected

Time synchronization and wind speed forecast of an exemplary scan. Panels

Time synchronization of the lidar scan initialized at

When the time shift within a lidar scan is larger than the averaging time (1 min) of the forecasted values, one cannot assume the scan to be quasi-instantaneous, which is commonly done when considering wind speed averages from lidar scans. Several approaches to account for the time shift within the scan have been tested, all aiming to synchronize the scan in time before applying the propagation methodology (Sect.

To generate a wind speed forecast the methodology developed by

Vectors arriving within a previously defined area of influence around the turbine of interest and within a time interval of

At this point, two orders of the methodological steps are possible, i.e. propagating wind vectors at varying heights that are different from the height of interest to the target turbines before extrapolating to hub height or performing the extrapolation prior to the wind vector propagation.
Each of the two possibilities is associated with specific errors. In this case study, we chose to propagate wind vectors before the wind speed extrapolation as this yielded more accurate results. The consequences of this approach will be discussed in Sect.

As the lidar was positioned at TP height, an extrapolation to the hub height was needed. A logarithmic wind profile including a stability correction

The wind profile includes the horizontal wind speed

The atmospheric stability for each lidar scan was determined using the methodology described by

For the calculation of the stability correction term

With the height of the measurement

The forecasted wind speed distribution was finally transformed into a wind power distribution. To do so, a probabilistic power curve constructed using high-elevation lidar scans (Sect.

Normalized probabilistic power curve of wind turbine T2 with average power and its standard deviation in black for each wind speed interval.

In the following, we will first introduce the case study at the offshore wind farm Global Tech I, then analyse the method's advantages and limitations, and afterwards assess the quality of a 5 min ahead lidar-based deterministic as well as probabilistic wind power forecast of the free-flow turbines T1–T7, based on the mentioned case study.

Power forecasts at Global Tech I were analysed as a case study. The wind farm consists of 80 wind turbines of the type Adwen 5-116 with a rotor diameter of

A forecast was generated for each lidar scan, thus with a temporal resolution of approximately 2.5 min. Forecasts within the interval from 8 March to 31 May 2019 were evaluated. Here, we only considered situations in power production mode below rated wind speed. For further analysis, only scans with a total spatial availability of at least 80 % after applying the filtering algorithms (Sect.

Wind speed and direction distribution of the used data set at the wind farm Global Tech I. Shown are mean wind speed and direction values for all lidar scans with a data availability of at least 80 % within the period from 8 March to 31 May 2019. The grey shaded area indicates wind directions that were considered for further analysis.

To perform the time synchronization an interpolation time step

During the measurement campaign, a slight elevation misalignment of the lidar was detected. Using a so-called sea surface levelling method, the magnitude of pitch and roll of the lidar, i.e. the tilt of the geographical coordinate system, was determined as proposed by

The measuring height

Wind vectors extrapolated to hub height were transformed in a final step to wind power values using the methodology and power curve introduced in Sect.

Here, we aim to present the results of the individual methodical steps introduced previously. We assessed how the use of single-Doppler lidar measurements and the low scanning speed affected the lead time, availability and skill of the forecast. Further, the impact of the extrapolation to hub height was analysed

The data availability of all valid lidar scans dependent on range gate, applying different filtering methodologies, is compared in Fig.

Data availability dependent on range gate when applying the threshold filter (blue) and the density filter (red). In green the availability after applying the density filter and neglecting the critical angles and wind speed outliers is depicted.

The number of observations at each measurement point in the polar coordinate system of the lidar before filtering is shown in Fig.

Distribution of measurements across the measurement domain as a result of varying lidar trajectories. The number of observations at each measurement point in the polar coordinate system of the lidar is visualized

Number of valid forecasts

Also for wind directions ranging from 160 to 200

For wind directions larger than 310

The application of the time synchronization method introduced in Sect.

Based on the data availability at different range gates, the maximal possible lead time of the forecast was determined. Even though we only considered partial load situations, i.e. situations with mean wind speeds up to 12 m s

The wind speed extrapolation to hub height is, following the method introduced in Sect.

Number of valid forecasts

Dependence of the height extrapolation factor

Wind speed point forecasts were calculated as the mean of the predicted wind speed distributions. Forecasts (fc) were verified with 1 min mean SCADA data (obs) and using the root-mean-squared error (RMSE), mean absolute error (MAE) and bias.

As a reference, the benchmark persistence was used, which assumes the future value at

Figure

Comparison of 5 min ahead power forecasts at turbine T3 with 1 min mean SCADA data for

Comparison of 5 min ahead power forecasts at turbine T3 with 1 min mean SCADA data for

Table

Number of valid scans

Figure

The quality of persistence is much better compared to unstable situations, due to lower wind speed fluctuations characteristic in stable situations

Probabilistic forecasts are generally evaluated by means of their sharpness and calibration. Sharpness describes the broadness of its distribution, while calibration estimates the consistency between the statistics of forecasts and observations

To assess the forecast's calibration, quantile–quantile reliability diagrams

Also, for the evaluation of probabilistic forecasts, persistence was used as a reference. Here, we generated a probabilistic persistence forecast by adding the errors of the 19 previous time steps to the forecast, as suggested by

In Table

Number of valid forecasts

An exemplary time series of the lidar forecast for unstable stratification is shown in Fig.

An example 1.5 h time series of the 5 min ahead lidar power forecast for unstable stratification at turbine T3 shown in red. Confidence intervals are visualized as shaded grey areas from 5 % to 95 % in 10 % intervals. The blue curve shows 1 min mean SCADA data of T3 and the green curve persistence.

In Fig.

Reliability diagrams of all free-flow turbines of

We compare the average crps for stable and neutral conditions of persistence and the LF in Table

Reliability diagrams of all free-flow turbines of

Number of valid forecasts

We introduced a minute-scale forecasting methodology for long-range single-Doppler lidar measurements and used it to predict the power of seven free-flow turbines of the offshore wind farm Global Tech I. The proposed model was developed as an extension of and an alternative to existing methods and is applicable to far-offshore sites. Emphasis was hereby put onto the use of single-Doppler lidar measurements compared to dual-Doppler radar set-ups. In the following, we discuss the model's ability to skilfully predict power under different atmospheric conditions. Moreover, limitations, possibilities and necessary adjustments concerning the forecast horizon are assessed. Finally, we qualitatively analyse the forecast uncertainty.

While the LF was able to predict wind power more reliably than persistence for unstable situations, the methodology failed when applied during stable stratification. Generally, we would expect the assumptions of a homogeneous wind field and negligible vertical wind speed component, which are the basis of the wind field reconstruction, to be less applicable during unstable situations when high amounts of thermal buoyancy cause strong vertical mixing

Further, the height difference causes errors in wind vector advection. We chose to propagate wind vectors to the target turbines prior to the wind speed extrapolation. Vectors were thus propagated with lower wind speed at measuring height compared to that at hub height. This suggests wind vectors arrive at the turbines slightly delayed, with the extent of the delay related to the measuring height. We assume this reduces the forecast skill.
As the increase in wind speed with height is larger during stable stratification, we expect the effect to be more distinct for those cases. In this case study, the alternative, i.e. extrapolating wind speed before propagation, caused even larger errors compared to the ones presented in Sect.

For future applications, a more accurate description of the wind profile, especially in stable situations, is required to further improve the forecast skill. That also includes a more accurate estimation of stability and therefore demands reliable meteorological measurements. We thus suggest using buoy measurements for future applications instead of relying on OSTIA SST data (Sect.

While the benchmark persistence yields good forecasts for stable and neutral situations, it has obvious shortcomings for strongly fluctuating situations and ramp events. Its comparison to the LF model has shown the latter's ability to predict such situations better (Fig.

In this work, we developed a 5 min ahead power forecast. In order for remote-sensing-based forecasts to be useful for power grid balancing and electricity trading, the forecast horizon needs to be extended further

Small lidar systems suitable for offshore campaigns typically reach measurement distances from 8 km to a maximum of 10 km

A large disadvantage of single-Doppler lidar data is the need to exclude a critical region with

Furthermore, the long duration of the scans in this analysis caused the need for a time synchronization and reduced the achievable forecast horizon after the end time of the scan significantly (Sect.

We already mentioned the errors attributed to the extrapolation of wind speeds to hub height, namely uncertainties in stability and wind profile estimation as well as an inaccurate determination of measuring height. While measuring at hub height would reduce the need for a wind speed extrapolation, it would introduce new challenges such as the correction for significant stationary and dynamic inclination of the scan plane due to the flexibility of the wind turbine tower and its dynamic excitation. In our case study, we had to correct for wind-speed-dependent platform inclination despite the fact that the lidar was positioned on a comparably stiff platform on a tripod foundation of the offshore turbine at GT I.

We further have to consider errors during the wind field reconstruction, including the estimation of global wind direction by means of a VAD fit, assuming a homogeneous flow and neglecting the vertical wind component. Those wind direction errors; uncertainties in azimuth, elevation and range gate of the lidar system; and errors of the measured line-of-sight velocities all contribute to the uncertainties in the estimation of the horizontal wind field. The use of dual-Doppler instead of single-Doppler lidar data would allow for a more accurate estimation of horizontal wind speed components and would likely decrease the associated errors significantly. We further expect the propagation of wind vectors by means of their local wind speed and direction, both assumed constant along the entire trajectory, to introduce some uncertainties, enhanced by the errors assigned to its input parameters. Especially in situations where wind vectors were partially propagated through the wind farm area and turbines might have been affected by wakes, large errors were observed. Moreover, recent studies suggest the existence of a wind farm blockage effect

The variety of uncertainties associated with the model emphasize the importance of the probabilistic approach as it allows us to account for some of them. The area of influence hereby plays a crucial part to determine the probabilistic forecast. The AoI estimated in this case study is 5 times smaller than the one

As already mentioned, the VAD fit caused the estimated wind direction to be constant across azimuth angles and only vary with range gates. This likely had an impact on the individual wind vectors reaching the area of influence. The uniform wind direction across range gates restricted the area from which vectors could be propagated to the target turbines. We assume this led to a mis-estimation, most likely an underestimation, of the spread of the observed wind speeds. Consequently, it is anticipated that the spread of the forecasted wind power distribution is too small. When using dual-Doppler lidar measurements, wind directions could be determined individually for each measurement point and the forecast's distribution represented more accurately.

Another limitation of the LF is its need for high data availability. Lidars send out laser pulses and use the backscattered signal to estimate wind speed. If not enough or too many aerosols are in the air, the signal becomes noisy

We developed a methodology to forecast wind power of individual wind turbines on very short time horizons based on long-range single-Doppler lidar scans as a feasible alternative to existing remote-sensing-based forecasts that is applicable to far-offshore sites. The work is based on a probabilistic forecasting model developed for dual-Doppler radar measurements. It was extended to include a dynamic filtering approach, a time synchronization of the lidar scans and an extrapolation of wind speeds to hub height. The model was tested in a case study at the offshore wind farm Global Tech I. Here, we predicted wind power of seven free-flow wind turbines with a 5 min horizon. The lidar-based forecast was able to predict wind turbine power skilfully compared to the benchmark persistence during unstable atmospheric conditions, as long as sufficient wind field information was available in the region from which the wind vectors were propagated to the turbine of interest. During stable and neutral conditions the forecast quality was reduced. We mainly attribute this to higher uncertainties in the wind speed extrapolation to hub height during stable conditions, as a consequence of the nature of the stability-corrected logarithmic wind profile. To outperform persistence for stable situations, a more accurate description of the wind profile, e.g. using reliable meteorological information, is required.

Future work aims to include the modelling of wake effects in the forecast, allowing one to forecast power not only for free-flow turbines.

Lidar data could be made available on request. GT I SCADA data are confidential and therefore not available to the public.

FT performed the main research and wrote the paper. MFvD contributed to the scientific discussion, the outline and the review of the manuscript. LvB and MK supervised the research, contributed to the scientific discussion, the research concept, and the outline and thorough review of the manuscript.

The authors declare that they have no conflict of interest.

We acknowledge the wind farm operator Global Tech I Offshore Wind GmbH for providing SCADA data and thank them for supporting our work. We thank the Met Office for making the OSTIA data set available.

We thank Jörge Schneemann and Stephan Voß for conducting the measurement campaign and supporting our lidar data analysis, Andreas Rott for his help characterizing the lidar misalignment, and Laura Valldecabres for her input regarding the forecasting methodology.

This research has been supported by the Deutsche Bundesstiftung Umwelt (grant no. 20018/582) and the Bundesministerium für Wirtschaft und Energie on the basis of a decision by the German Bundestag (OWP Control, grant no. 0324131A, and WIMS-Cluster, grant no. 0324005).

This paper was edited by Jakob Mann and reviewed by three anonymous referees.