Advancing Wind Resource Assessment in Complex Terrain with Scanning Lidar Measurements

Abstract

The planning and realization of wind energy projects requires an as accurate and precise wind resource estimation as possible. Standard procedures combine shorter on-site measurements with the application of numerical models. The uncertainties of the numerical data generated from these models are, particularly in complex onshore terrain, not just rather high but typically not well quantified. In this article we propose a methodology for using a single scanning Doppler wind lidar device to calibrate the output data of a numerical flow model and with this not just quantify but potentially also reduce the uncertainties of the final wind resource estimate. The scanning lidar is configured to perform Plan Position Indicator (PPI) scans and the numerical flow data are projected onto this geometry. Deviations of the derived from the recorded line-of-sight wind speeds are used to identify deficiencies of the model and as starting point for an improvement and tuning. The developed methodology is demonstrated based on a study for a site in moderately complex terrain in central Germany and using two rather different types of numerical flow models. The findings suggest that the use of the methodology and the introduced scanning wind lidar technology offers a promising opportunity to control the uncertainty of the applied flow models, which can otherwise only be estimated very roughly.

A comprehensive procedure to process scanning lidar data for engineering wake model validation

“…We also added a component regarding the wind direction uncertainty (u β ) [12]. This term is dominant in our case study, under the premise that the inflow wind direction dictates the wake direction.…”

“…The model inputs are defined as mean inflow quantities taken from a selected bin in the capture matrix. The comparison between observations and simulation is made on a radial wind speed basis, i.e., by directly projecting the model output (assuming zero mean vertical wind speed) onto the lidar beams, following the method proposed by [12].…”

“…However, the wake is by definition heterogeneous, with large spatial gradients at the wake boundary and a velocity deficit. Nevertheless, the projection method comes with the downside of sensitivity of wind direction variability, which plays a dominant role in the overall uncertainty [12]. The next section will present the final comparison between observations and simulation, along with an estimation of associated uncertainties.…”

The Importance of Wind Resource Assessment in Plant Factories’ Siting

“…This completely new category of studies should be devoted to the detailed wind resource assessment of the area used to identify those zones to try and optimise their operation. Especially in complex terrain, this is more needed [7,8]. Since wind turbines appeared, wind resource analysis has been mostly linked to the performance of wind turbines [9,10].…”

Desarrollo y prueba de un sistema para sensar de manera remota la velocidad del viento

“…Existen sistemas complejos y de alto costo para mapear el recurso eólico, como el LIDAR [5], un sistema que mediante el efecto Doppler aplicado a señales ópticas permite caracterizar la velocidad del viento en un área, estas mediciones se realizan usualmente desde tierra. Una opción que se ha explorado recientemente para el monitoreo de la velocidad del viento de manera remota es mediante la integración del Internet de las Cosas (IoT), algunos trabajos se han enfocado al recurso eólico [6] y otros al monitoreo de turbinas, donde entre sus parámetros se encuentra la velocidad del viento [7,8].…”