Posters

Abstract

An efficient method to simulate time series of wind speed and wind turbine electricity generation on a microscale grid resolution is described. Speed-up factors are simulated by the microscale model Meteodyn for 12 wind direction sectors and 10 stability classes. The speed-up factors on the microscale grid are combined with wind speed time series simulated with the mesoscale model WRF (Weather Research & Forecasting Model) resulting in long-term wind speed time series on the microscale grid. Thermal effects are considered by translating the near-surface Monin-Obukhov-Length L which is a WRF output parameter into the stability classes defined by the CFD model Meteodyn.  Wind simulations are compared to LiDAR (Light Detection and Ranging) measurements at two sites during a summer and winter period showing an under- and overestimation, respectively. The vertical wind shear and the temporal variability are reasonably well simulated. The bias differs for the summer and winter period and depends on the meso-cell option selected for the microscale model forcing. The effect of this “representativeness error” is shown by comparing the vertical wind profile simulated with different mesoscale grid cell forcings.  The particular winter and summer case show a different behavior. In summer wind speed is underestimated which results in an electricity production close to the one recorded by a nearby wind farm. The winter case for a different site shows an overestimation of the wind speed which would lead to unrealistic production data. Therefore, we correct for this bias by scaling the modelled wind speed at hub height to match on-site measurements by a LiDAR device.