Abstract
Remote sensing-based wind power forecasts are nowadays being increasingly investigated. Long-range lidar scans are hereby often performed at low heights, causing the need for a wind speed extrapolation to hub height. In this work we analysed the accuracy of the stability corrected logarithmic wind profile and its sensitivity to atmospheric stability, wind speed and extrapolation height by means of a theoretical error estimation using error propagation. Emphasis was given to analyse the contributions of the profile’s individual variables but also considering the measurement campaign framework. We further used lidar measurements at the offshore wind farm Global Tech I to support the theoretical analysis. The logarithmic profile was found to be able to describe profiles during most situations, however, decreasing wind speeds with height cannot be represented. Results showed that due to the nature of the stability correction term extrapolation errors are largest during very stable atmospheric conditions. Here, stability estimation errors were dominant. Under near neutral and neutral atmospheric conditions the wind speed error contributed most to the overall error. We conclude that extrapolation errors can mainly be reduced by optimising the estimation of atmospheric stability using accurate measurement devices. Furthermore, the precise horizontal alignment of the lidar device is important.
Highlights
Wind speed and power forecasts are gaining increasing importance due to the rising share of wind energy in our energy system
In this work we analysed the accuracy of a stability corrected logarithmic wind profile for wind speed extrapolation in the scope of very short-term power forecasts based on long-range scanning lidar data
A theoretical error estimation was performed and disclosed that large errors mainly occur under stable atmospheric conditions due to the definition of the stability correction term
Summary
Wind speed and power forecasts are gaining increasing importance due to the rising share of wind energy in our energy system. Inflow regions of the wind farm or wind turbine are typically measured by means of horizontal Plan Position Indicator (PPI) scans, allowing to retrieve wind field information with high temporal and spatial resolution. With the growing amount of offshore wind energy, especially compact scanning lidar devices become more suitable for that purpose, as they are comparably cheap, easy to set up and can be positioned on transition pieces of turbines, nacelles or platforms [7]. When aiming to forecast power, knowledge about the wind speed at hub height is crucial. As PPI lidar scans are typically performed at lower and in most cases varying measuring heights, due to misalignments and tilts of the devices [8], the need for skilful vertical wind speed extrapolation methodologies arises. A common way of extrapolation is the stability corrected logarithmic wind
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