ABSTRACT
High‐quality tall mast and wind lidar measurements over the North and Baltic Seas are used to validate the wind climatology produced from winds simulated by the Weather, Research and ...Forecasting (WRF) model in analysis mode. Biases in annual mean wind speed between model and observations at heights around 100 m are smaller than 3.2% at offshore sites, except for those that are affected by the wake of a wind farm or the coastline. These biases are smaller than those obtained by using winds directly from the reanalysis. We study the sensitivity of the WRF‐simulated wind climatology to various model setup parameters. The results of the year‐long sensitivity simulations show that the long‐term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin‐up to produce a wind climatology. It is found that the spin‐up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast.
Grid and spectral nudging are effective ways of preventing drift from large-scale weather patterns in regional climate models. However, the effect of nudging on the wind speed variance is unclear. In ...this study, the impact of grid and spectral nudging on near-surface and upper boundary layer wind variance in the Weather Research and Forecasting Model is analyzed. Simulations are run on nested domains with horizontal grid spacing of 15 and 5 km over the Baltic Sea region. For the 15-km domain, 36-h simulations initialized each day are compared with 11-day simulations with either grid or spectral nudging at and above 1150m above ground level (AGL). Nested 5-km simulations are not nudged directly but inherit boundary conditions from the 15-km experiments. Spatial and temporal spectra show that grid nudging causes smoothing of the wind in the 15-km domain at all wavenumbers, both at 1150 m AGL and near the surface where nudging is not applied directly, while spectral nudging mainly affects longer wavenumbers. Maps of mesoscale variance show spatial smoothing for both grid and spectral nudging, although the effect is less pronounced for spectral nudging. On the inner, 5-km domain, an indirect smoothing impact of nudging is seen up to 200 km inward from the dominant inflow boundary at 1150 m AGL, but there is minimal smoothing from the nudging near the surface, indicating that nudging an outer domain is an appropriate configuration for wind-resource modeling.
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.
This is the first of two papers that document the creation of the New European Wind Atlas (NEWA). It describes the sensitivity analysis and evaluation procedures that formed the basis for choosing ...the final setup of the mesoscale model simulations of the wind atlas. The suitable combination of model setup and parameterizations, bound by practical constraints, was found for simulating the climatology of the wind field at turbine-relevant heights with the Weather Research and Forecasting (WRF) model. Initial WRF model sensitivity experiments compared the wind climate generated by using two commonly used planetary boundary layer schemes and were carried out over several regions in Europe. They confirmed that the most significant differences in annual mean wind speed at 100 m a.g.l. (above ground level) mostly coincide with areas of high surface roughness length and not with the location of the domains or maximum wind speed. Then an ensemble of more than 50 simulations with different setups for a single year was carried out for one domain covering northern Europe for which tall mast observations were available. We varied many different parameters across the simulations, e.g. model version, forcing data, various physical parameterizations, and the size of the model domain. These simulations showed that although virtually every parameter change affects the results in some way, significant changes in the wind climate in the boundary layer are mostly due to using different physical parameterizations, especially the planetary boundary layer scheme, the representation of the land surface, and the prescribed surface roughness length. Also, the setup of the simulations, such as the integration length and the domain size, can considerably influence the results. We assessed the degree of similarity between winds simulated by the WRF ensemble members and the observations using a suite of metrics, including the Earth Mover's Distance (EMD), a statistic that measures the distance between two probability distributions. The EMD was used to diagnose the performance of each ensemble member using the full wind speed and direction distribution, which is essential for wind resource assessment. We identified the most realistic ensemble members to determine the most suitable configuration to be used in the final production run, which is fully described and evaluated in the second part of this study (Dörenkämper et al., 2020).
The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of ...their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments of wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, constraints due to regulation or public opposition, expert and stakeholder workshops, willingness to pay/accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.
•Review of over 300 studies on large-scale onshore wind potential assessments.•Analysis of geographic, technical, economic and ‘feasible’ potentials.•Identify weaknesses in methods and best practice examples.•Methods could improve transparency, validation, and sensitivities.•Further interdisciplinary research required on ‘feasible’ potentials.
We evaluate the performance of six planetary boundary layer (PBL) schemes and a large-eddy simulation (LES) for the characterization of the neutral PBL through idealized simulations using the Weather ...Research and Forecasting model. The evaluation is performed by comparison with sonic anemometer measurements from a 250-m tall meteorological tower that observes close-to-homogeneous winds under the predominant westerlies. All simulations show similar behavior for the vertical temperature profile except for one that uses a non-local PBL scheme, which is known to produce excessive vertical mixing. Within the range of measurements of the tower and except for a PBL scheme that produces a too low jet, the simulations using PBL schemes, which were developed to simulate the nighttime atmosphere, generally show the largest deviations from the observed wind speeds. As expected within the surface layer, the LES shows excessive vertical shear. More importantly, we find that two PBL schemes that use their own surface-layer scheme are effectively reducing the surface roughness. When looking at the vertical profile of momentum exchange coefficient, we find very good agreement between a local and a non-local PBL scheme within the bulk of the PBL, the highest values for the simulation with the PBL scheme showing excessive vertical mixing and the lowest values for that most suitable for nighttime conditions. The comparison with the observed turbulent kinetic energy reveals a good match between the LES and a simulation using a local PBL scheme and a general underestimation (overestimation) of turbulence within the range of measurements by the PBL schemes (LES).
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). In Part 1, we described the sensitivity experiments and accompanying evaluation done to arrive at ...the final mesoscale model setup used to produce the mesoscale wind atlas. In this paper, Part 2, we document how we made the final wind atlas product, covering both the production of the mesoscale climatology generated with the Weather Research and Forecasting (WRF) model and the microscale climatology generated with the Wind Atlas Analysis and Applications Program (WAsP). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the downscaling using WAsP. We show the main results from the final wind atlas and present a comprehensive evaluation of each component of the NEWA model chain using observations from a large set of tall masts located all over Europe. The added value of the WRF and WAsP downscaling of wind climatologies is evaluated relative to the performance of the driving ERA5 reanalysis and shows that the WRF downscaling reduces the mean wind speed bias and spread relative to that of ERA5 from -1.50±1.30 to 0.02±0.78 m s−1. The WAsP downscaling has an added positive impact relative to that of the WRF model in simple terrain. In complex terrain, where the assumptions of the linearized flow model break down, both the mean bias and spread in wind speed are worse than those from the raw mesoscale results.
The decarbonisation of energy sources requires additional investments in renewable technologies, including the installation of onshore and offshore wind farms. For wind energy to remain competitive, ...wind farms must continue to provide low-cost power even when covering larger areas. Inside very large wind farms, winds can decrease considerably from their free-stream values to a point where an equilibrium wind speed is reached. The magnitude of this equilibrium wind speed is primarily dependent on the balance between turbine drag force and the downward momentum influx from above the wind farm. We have simulated for neutral atmospheric conditions, the wind speed field inside different wind farms that range from small (25 km2) to very large (105 km2) in three regions with distinct wind speed and roughness conditions. Our results show that the power density of very large wind farms depends on the local free-stream wind speed, the surface characteristics, and the turbine density. In onshore regions with moderate winds the power density of very large wind farms reaches 1 W m−2, whereas in offshore regions with very strong winds it exceeds 3 W m−2. Despite a relatively low power density, onshore regions with moderate winds offer potential locations for very large wind farms. In offshore regions, clusters of smaller wind farms are generally preferable; under very strong winds also very large offshore wind farms become efficient.
“Wind Theft” from Onshore Wind Turbine Arrays Pryor, Sara C.; Shepherd, Tristan J.; Volker, Patrick J. H. ...
Journal of applied meteorology and climatology,
01/2020, Volume:
59, Issue:
1
Journal Article
Peer reviewed
Open access
High-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations the ...commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP) to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.
PDF
