<p>Recent studies have shown that the increasing sizes of offshore wind farms can cause a reduced energy production through mesoscale interactions with the atmosphere. Therefore, accurate nowcasting of the energy yields of large offshore wind farms depend on accurate predictions of the large synoptic weather systems as well as accurate predictions of the smaller mesoscale weather systems. In general, global or regional forecasting models are very well suited to predict synoptic-scale weather systems. However, satellite or radar data can support the nowcasting of shorter, smaller-scale systems.&#160;</p><p>In this work, a first step towards nowcasting of the mesoscale wind using satellite images has been taken, namely the coupling of the mesoscale wind component to cloud properties that are available from satellite images using a deep learning framework. To achieve this, a high-resolution regional atmospheric model (COSMO-CLM) was used to generate one year of high resolution cloud en hub-height wind data. From this wind data the mesoscale component was filtered out and used as target images for the deep learning model. The input images of the model were several cloud-related fields from the atmospheric model. The model itself was a Deep Convolutional Neural Network (a U-Net) which was trained to minimize the mean squared error.&#160;</p><p>This analysis indicates that cloud information can be used to extract information about the mesoscale weather systems and could be used for nowcasting by using the trained U-Net as a basis for a temporal deep learning model. However, future validation with real-world data is still needed to determine the added value of such an approach.</p>
<p>Before an off-shore wind farm is built a thorough resource assessment of all available locations for the farm needs to be performed. Since the power extraction of a wind farm depends on the cube of the wind speed even the mesoscale variability in the wind speed plays an important role in the resource assessment of a wind farm. In order to study mesoscale systems that occur in the vicinity of off-shore wind farms we've set up a convection permitting simulation in COSMO-CLM for the Kattegat sea strait. The Kattegat is particularly interesting since it is an area which features a very irregularly shaped coastline and pronounced coastal effects. Centrally located in the Kattegat lays the 400 MW Anholt wind farm. Operational data of the Anholt wind farm and scatterometer data of the Kattegat are used to validate our simulation. A relatively good agreement between observations and the model output has been found. A variety of mesoscale systems has been identified, both in unstable (e.g. a downburst) as in stable (e.g. gravity waves) conditions. The wind speed variability on temporal scales and on spatial scales over the Kattegat has been investigated. The interactions of the Anholt wind farm with these systems have been investigated using the COSMO-CLM model which incorporates the Fitch wind farm parametrisation. This research is part of a larger project aiming at developing a fast and accurate resource planning and forecasting platform for off-shore wind farms. More information about this project can be found on freewind-project.eu.</p>
Abstract. As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller with a tendency to install larger turbines at high capacity densities. It is however not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that at a capacity density of 8.1 MW km-2 and for SW-winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 55 % and 40 % depending on the proximity, size and number of the upwind farms. However, the long-term impact of wake losses in and between wind farms is mitigated by adopting next-generation, 15 MW wind turbines instead of 5 MW turbines, as the annual energy generation over all wind farms in the simulation is increased by 24 % at the same capacity density. In contrast, the impact of wake losses is exacerbated with increasing capacity density, as the layout-integrated, annual capacity factor varies between 54.4 % and 44.3 % over the considered range of 3.5 to 10 MW km-2. Overall, wind farm characteristics and inter-farm distances play an essential role in cluster-scale wake losses, which should be taken into account in future wind farm planning.
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