The rapid growth of offshore wind energy has brought both environmental opportunities and challenges. Offshore wind farms (OWFs) can significantly impact the surrounding marine ecosystem through their wind wakes—regions of reduced wind speed downwind of the turbine array. These extensive wind wakes, which can stretch for kilometers, trigger pronounced vertical ocean movements like upwelling and downwelling. Such artificial ocean responses play a vital role in regulating marine stratification and ecosystems.
Scanning Doppler lidar (light detection and ranging) technology has emerged as a powerful tool for studying wind farm wake characteristics. By spatially resolving the inflow wind field, these remote sensing instruments can provide detailed insights into the formation and evolution of wake structures. However, accurately measuring the relatively small wind speed deficits associated with global wind farm blockage remains a significant challenge, requiring careful lidar calibration and data analysis.
This article explores the use of scanning Doppler lidar to detect offshore wind farm-induced wind wake effects. We present findings from a comprehensive measurement campaign at the 400 MW Global Tech I (GT I) wind farm in the German North Sea, analyzing the impact of atmospheric stability and turbine operating conditions on the observed wind speed reductions upwind of the facility. Our results demonstrate the potential of this advanced remote sensing technology to quantify the global blockage phenomenon, which has important implications for wind resource assessment and energy production optimization.
Scanning Doppler lidar for wind farm wake measurements
Operational offshore wind farms can create extensive wind wakes that stretch for tens of kilometers downwind, significantly impacting the local atmospheric and marine environments. While the characteristics of individual turbine wakes have been well-studied, the aggregated effect of an entire wind farm—known as global wind farm blockage—is less understood.
Global blockage refers to the upstream deceleration of wind speeds caused by the collective thrust of all turbines within a wind farm. This phenomenon results in a reverse pressure gradient that reduces wind speeds in front of the facility, in contrast to the localized velocity deficits observed directly behind individual turbines. Accurately quantifying global blockage is crucial for improving wind resource assessments and enhancing the reliability of energy production forecasts.
Scanning Doppler lidar systems offer a powerful solution for studying these wind farm wake effects. By scanning the inflow region in front of a wind farm, these remote sensing instruments can provide high-resolution, spatially-resolved wind data—a significant advantage over traditional meteorological mast measurements, which are limited to discrete points.
However, detecting the relatively small wind speed deficits associated with global blockage, typically in the low single-digit percentage range, poses a significant challenge. Careful lidar calibration, data analysis techniques, and consideration of environmental factors are required to separate this effect from background wind field variations.
Measurement campaign at the Global Tech I wind farm
To investigate the presence and characteristics of global wind farm blockage, we conducted a comprehensive measurement campaign at the 400 MW Global Tech I (GT I) offshore wind farm in the German North Sea. Equipped with a scanning long-range Doppler lidar, we analyzed the inflow wind field upwind of the facility under various atmospheric conditions and turbine operating states.
The GT I wind farm, which became fully operational in 2015, features 80 Adwen AD 5-116 turbines with a rotor diameter of 116 m and a hub height of 92 m. We installed the lidar system on the transition piece of turbine GT58, measuring at a height of approximately 24.6 m above mean sea level—9 m below the lower blade tip.
By performing plan position indicator (PPI) scans with a 0° elevation angle, we were able to capture the horizontal wind field up to a distance of 8 km (approximately 69 rotor diameters) upwind of the wind farm. To account for variations in atmospheric stability and turbine operating conditions, we analyzed four distinct scenarios:
- Unstable atmospheric stratification with high turbine thrust coefficients (above 0.8).
- Stable atmospheric stratification with low thrust coefficients (turbines not operating).
- Stable stratification with low thrust coefficients (turbines operating above rated wind speed).
- Stable stratification with high thrust coefficients (partial load operation).
For each scenario, we performed a detailed uncertainty analysis, considering factors such as lidar orientation, measurement height variations, and wind profile extrapolation, to reliably distinguish the global blockage effect from background wind field fluctuations.
Observing wind speed deficits upwind of the wind farm
Our analysis of the lidar measurements revealed distinct patterns in the inflow wind field that were dependent on atmospheric stability and turbine operating conditions.
In the case of unstable atmospheric stratification (Scenario 1), we did not observe any significant wind speed reductions upwind of the GT I wind farm. This is likely due to the enhanced vertical mixing in the atmospheric boundary layer, which counteracts the formation of a coherent global blockage effect.
Similarly, under stable stratification with low turbine thrust coefficients (Scenarios 2 and 3), no clear wind speed deficits were detected in front of the wind farm. This can be attributed to the reduced thrust of the turbines, either due to idling or operation at high wind speeds above the rated condition.
However, in Scenario 4—stable atmospheric stratification with high turbine thrust coefficients (partial load operation)—we observed a systematic decrease in wind speeds upwind of the GT I facility. Specifically, the normalized wind speeds decreased by approximately 4% (within an uncertainty range of 2% to 6%) over a distance of 25 rotor diameters (2.9 km) from the wind farm.
These findings provide strong evidence for the existence of a global blockage effect, wherein the collective thrust of the wind turbines creates a reverse pressure gradient that decelerates the incoming wind. Importantly, our analysis demonstrates that this phenomenon is highly dependent on atmospheric stability, with the global blockage effect being most pronounced under stable stratification conditions.
Implications for wind resource assessment and energy production
The accurate measurement and characterization of global wind farm blockage have important implications for the wind energy industry. Underestimating this effect can lead to biases in wind resource assessments and, consequently, inaccuracies in energy production forecasts and financial projections.
For the GT I wind farm, we estimate that the observed 4% wind speed deficit could translate to a non-negligible reduction in annual energy production. This highlights the need to incorporate global blockage considerations into wind farm planning and optimization models.
By leveraging the spatial measurement capabilities of scanning Doppler lidar, wind farm operators and developers can gain deeper insights into the formation and evolution of wind wakes. This knowledge can inform the strategic placement of turbines within a wind farm, as well as the design of wind farm clusters, to mitigate the impacts of global blockage and optimize energy harvesting.
Furthermore, the experimental validation of global blockage effects using advanced remote sensing techniques can contribute to the refinement of numerical models and engineering tools used for wind resource assessment and power prediction. Bridging the gap between field measurements and computational simulations will be crucial for enhancing the reliability and accuracy of wind energy forecasts.
Conclusion
This study demonstrates the potential of scanning Doppler lidar technology to quantify the global wind farm blockage effect, a critical phenomenon influencing the performance of offshore wind energy facilities. By analyzing high-resolution measurements at the GT I wind farm, we have provided strong evidence for the existence of wind speed deficits upwind of the facility under specific atmospheric and operational conditions.
The ability to spatially resolve the inflow wind field, coupled with a rigorous uncertainty analysis, has enabled us to distinguish the global blockage effect from background wind field variations. These insights have important implications for wind resource assessment, energy production optimization, and the continued development of numerical models and engineering tools to support the growth of the offshore wind industry.
As the world transitions towards a sustainable energy future, the accurate characterization of wind farm wake effects will be essential for maximizing the efficiency and environmental benefits of offshore wind power. The integration of advanced remote sensing techniques, like scanning Doppler lidar, with comprehensive field measurements and modeling efforts will be crucial in addressing this challenge.
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