Energy scenarios envision installation of up to 230 GW of wind capacity over available areas within the German onshore by 2050. The associated technical wind energy potential is typically derived assuming that the electricity generated by the wind turbines does not affect the wind resource. Consequently, future capacity factors, the ratio of generation to installed capacity, are implicitly assumed to be independent of the extent to which the wind resource is depleted. However, capacity factors reduce as wind capacity is increased. This is because kinetic energy (KE) removal lowers wind speeds that result in lower generation from the turbines. To assess the relevance of this resource depletion effect on capacity factors, we simulated electricity generation by wind turbines with a range of hypothetical and planned deployment scenarios using the Weather Research and Forecasting (WRF) model that incorporates the effects of atmosphere - turbine interactions and compared these to estimates derived from a simple, momentum-balance approach (VKE). Despite potential biases in modelled wind speeds, we find that for a typical planned scenario of ~200 GW deployed over 13.8% of land area, mean annual wind speeds reduce by an average of 0.4 m s-1 compared to the case where the impact of atmospheric - turbine interactions is excluded. Associated reductions in capacity factor are up to 20% in regions of high installed capacities. To isolate the key atmospheric influence, we compare the simulated range of wind speeds and capacity factors with those from the VKE model that only accounts for KE removal effects. We find that the KE removal effects play the dominant role in shaping the reductions in wind speeds and capacity factors, thus providing a simple tool to capture these effects.  We conclude that with increased deployment of wind energy in the context of the energy transition, these wind resource depletion effects need to be taken into account, but this can be done in a comparatively simple and physical way.
The primary case study within the paper is Hereford; United Kingdom – an ancient Norman city within rural Herefordshire. Significant research has previously been conducted as to the transport problems within the city and such research is summarised and built on in the current paper by proposing potential solutions to the problems.
Abstract. Wind farm parameterizations (WFPs) are used in mesoscale models for predicting wind farm power production and its impact on wind resources while considering the variability of the regional wind climate. However, the performance of WFPs is influenced by various factors including atmospheric stability. In this study, we compared two widely used WFPs in the Weather Research and Forecasting (WRF) model to large-eddy simulations (LES) of turbine wakes performed with the same model. The Fitch scheme and the Explicit Wake Parameterization were evaluated for their ability to represent wind speed and turbulent kinetic energy (TKE) in a two-turbine wind farm layout under neutral, unstable, and stable atmospheric stability conditions. To ensure a fair comparison, the inflow conditions were kept as close as possible between the LES and mesoscale simulations for each type of stability condition, and the LES results were spatially aggregated to align with the mesoscale grid spacing. Our findings indicate that the performance of WFPs varies depending on the specific variable (wind speed or TKE) and the area of interest downwind of the turbine when compared to the LES reference. The WFPs can accurately depict the vertical profiles of the wind speed deficit for either the grid cell containing the wind turbines or the grid cells in the far wake, but not both simultaneously. The WFPs with an explicit source of TKE overestimate TKE values at the first grid cell containing the wind turbine; however, for downwind grid cells, agreement improves. On the other hand, WFPs without a TKE source underestimate TKE in all downwind grid cells. These agreement patterns between the WFPs and the LES reference are consistent under the three atmospheric stability conditions. However, the WFPs resemble less the wind speed and TKE from the LES reference under stable conditions than under neutral or unstable conditions.
Offshore wind farm cluster effects between neighboring wind farms increase rapidly with the large-scale deployment of offshore wind turbines. The wind farm wakes observed from Synthetic Aperture Radar (SAR) are sometimes visible and atmospheric and wake models are here shown to convincingly reproduce the observed very long wind farm wakes. The present study mainly focuses on wind farm wake climatology based on Envisat ASAR. The available SAR data archive covering the large offshore wind farms at Horns Rev has been used for geo-located wind farm wake studies. However, the results are difficult to interpret due to mainly three issues: the limited number of samples per wind directional sector, the coastal wind speed gradient, and oceanic bathymetry effects in the SAR retrievals. A new methodology is developed and presented. This method overcomes effectively the first issue and in most cases, but not always, the second. In the new method all wind field maps are rotated such that the wind is always coming from the same relative direction. By applying the new method to the SAR wind maps, mesoscale and microscale model wake aggregated wind-fields results are compared. The SAR-based findings strongly support the model results at Horns Rev 1.
Abstract This paper demonstrates that a statistical–dynamical method can be used to accurately estimate the wind climate at a wind farm site. In particular, postprocessing of mesoscale model output allows an efficient calculation of the local wind climate required for wind resource estimation at a wind turbine site. The method is divided into two parts: 1) preprocessing, in which the configurations for the mesoscale model simulations are determined, and 2) postprocessing, in which the data from the mesoscale simulations are prepared for wind energy application. Results from idealized mesoscale modeling experiments for a challenging wind farm site in northern Spain are presented to support the preprocessing method. Comparisons of modeling results with measurements from the same wind farm site are presented to support the postprocessing method. The crucial element in postprocessing is the bridging of mesoscale modeling data to microscale modeling input data, via a so-called generalization method. With this method, very high-resolution wind resource mapping can be achieved.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
