The influence of humidity fluxes on offshore wind speed profiles

Wind energy developments offshore focus on larger turbines to keep the relative cost of the foundation per MW of installed capacity low. Hence typical wind turbine hub-heights are extending to 100 m and potentially beyond. However, measurements to these heights are not usually available, requiring extrapolation from lower measurements. With humid conditions and low mechanical turbulence offshore, deviations from the traditional logarithmic wind speed profile become significant and stability corrections are required. This research focuses on quantifying the effect of humidity fluxes on stability corrected wind speed profiles. The effect on wind speed profiles is found to be important in stable conditions where including humidity fluxes forces conditions towards neutral. Our results show that excluding humidity fluxes leads to average predicted wind speeds at 150 m from 10 m which are up to 4% higher than if humidity fluxes are included, and the results are not very sensitive to the method selected to estimate humidity fluxes

Coupled wake boundary layer model of wind-farms

We present and test the coupled wake boundary layer (CWBL) model that describes the distribution of the power output in a wind-farm. The model couples the traditional, industry-standard wake model approach with a "top-down" model for the overall wind-farm boundary layer structure. This wake model captures the effect of turbine positioning, while the "top-down" portion of the model adds the interactions between the wind-turbine wakes and the atmospheric boundary layer. Each portion of the model requires specification of a parameter that is not known a-priori. For the wake model, the wake expansion coefficient is required, while the "top-down" model requires an effective spanwise turbine spacing within which the model's momentum balance is relevant. The wake expansion coefficient is obtained by matching the predicted mean velocity at the turbine from both approaches, while the effective spanwise turbine spacing depends on turbine positioning and thus can be determined from the wake model. Coupling of the constitutive components of the CWBL model is achieved by iterating these parameters until convergence is reached. We illustrate the performance of the model by applying it to both developing wind-farms including entrance effects and to fully developed (deep-array) conditions. Comparisons of the CWBL model predictions with results from a suite of large eddy simulations (LES) shows that the model closely represents the results obtained in these high-fidelity numerical simulations. A comparison with measured power degradation at the Horns Rev and Nysted wind-farms shows that the model can also be successfully applied to real wind-farms.Comment: 25 pages, 21 figures, submitted to Journal of Renewable and Sustainable Energy on July 18, 201

Interannual variability of wind climates and wind turbine annual energy production

The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6&thinsp;%, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10&thinsp;min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ = 6&thinsp;%. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6&thinsp;%, the results presented herein indicate that in over 90&thinsp;% of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.</p

Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States

A major issue in quantifying potential power generation from prospective wind energy sites is the lack of observations from heights relevant to modern wind turbines, particularly for offshore where blade tip heights are projected to increase beyond 250 m. We present analyses of uniquely detailed data sets from lidar (light detection and ranging) deployments in New York State and on two buoys in the adjacent New York Bight to examine the relative power generation potential and power quality at these on- and offshore locations. Time series of 10 min wind power production are computed from these wind speeds using the power curve from the International Energy Agency 15 MW reference wind turbine. Given the relatively close proximity of these lidar deployments, they share a common synoptic-scale meteorology and seasonal variability with lowest wind speeds in July and August. Time series of power production from the on- and offshore location are highly spatially correlated with the Spearman rank correlation coefficient dropping below 0.4 for separation distances of approximately 350 km. Hence careful planning of on- and offshore wind farms (i.e., separation of major plants by &gt; 350 km) can be used reduce the system-wide probability of low wind energy power production. Energy density at 150 m height at the offshore buoys is more than 40 % higher, and the Weibull scale parameter is 2 m s−1 higher than at all but one of the land sites. Analyses of power production time series indicate annual energy production is almost twice as high for the two offshore locations. Further, electrical power production quality is higher from the offshore sites that exhibit a lower amplitude of diurnal variability, plus a lower probability of wind speeds below the cut-in and of ramp events of any magnitude. Despite this and the higher resource, the estimated levelized cost of energy (LCoE) is higher from the offshore sites mainly due to the higher infrastructure costs. Nonetheless, the projected LCoE is highly competitive from all sites considered.</p

Wind speed trends over the contiguous United States

A comprehensive intercomparison of historical wind speed trends over the contiguous United States is presented based on two observational data sets, four reanalysis data sets, and output from two regional climate models (RCMs). This research thus contributes to detection, quantification, and attribution of temporal trends in wind speeds within the historical/contemporary climate and provides an evaluation of the RCMs being used to develop future wind speed scenarios. Under the assumption that changes in wind climates are partly driven by variability and evolution of the global climate system, such changes should be manifest in direct observations, reanalysis products, and RCMs. However, there are substantial differences in temporal trends derived from observational wind speed data, reanalysis products, and RCMs. The two observational data sets both exhibit an overwhelming dominance of trends toward declining values of the 50th and 90th percentile and annual mean wind speeds, which is also the case for simulations conducted using MM5 with NCEP-2 boundary conditions. However, converse trends are seen in output from the North American Regional Reanalysis, other global reanalyses (NCEP-1 and ERA-40), and the Regional Spectral Model. Equally, the relationship between changing annual mean wind speed and interannual variability is not consistent among the different data sets. NCEP-1 and NARR exhibit some tendency toward declining (increasing) annual mean wind speeds being associated with decreased (increased) interannual variability, but this is not the case for the other data sets considered. Possible causes of the differences in temporal trends from the eight data sources analyzed are provided

Modeling the impact of sea-spray on particle concentrations in a coastal city

Abstract 18 An atmospheric chemistry-transport model is used to assess the impacts of sea-spray chemistry 19 on the particle composition in and downwind of a coastal city -Vancouver, British Columbia. 20 Reactions in/on sea-spray affect the entire particle ensemble and particularly the size distribution 21 of particle nitrate. 2

Modeling the impact of sea-spray on particle concentrations in a coastal city

An atmospheric chemistry-transport model is used to assess the impacts of sea-spray chemistry on the particle composition in and downwind of a coastal city--Vancouver, British Columbia. Reactions in/on sea-spray affect the entire particle ensemble and particularly the size distribution of particle nitrate. Urban air quality, and particularly airborne particles, is a major concern in terms of human health impacts. Sea-spray is known to be a major component of the particle ensemble at coastal sites yet relatively few air quality models include the interaction of gases with sea-spray and the fate of the particles produced. Sea-spray is not an inert addition to the particle ensemble because heterogeneous chemistry in/on sea-spray droplets changes the droplets composition and the particle size distribution, which impacts deposition and the ion balance in different particle size fractions. It is shown that the ISOPART model is capable of simulating gas and particle concentrations in the coastal metropolis of Vancouver and the surrounding valley. It is also demonstrated that to accurately simulate ambient concentrations of particles and reactive/soluble gases in a coastal valley it is absolutely critical to include heterogeneous chemistry in/on sea-spray. Partitioning of total particle-NO{sub 3}{sup -} between sea-spray and NH{sub 4}NO{sub 3} is highly sensitive to the amount of sea-spray present, and hence the initial vertical profile, sea-spray source functions [48] and the wind speed. When a fixed wind speed is used to initialize the sea-spray vertical profiles, as expected, the sea-spray concentration decays with distance inland, but the particle-NO{sub 3}{sup -} concentration decays more slowly because it is also a function of the uptake rate for HNO{sub 3}. The simulation results imply model analyses of air quality in coastal cities conducted without inclusion of sea-spray interactions may yield highly misleading results in terms of emission sensitivities of the PM size distribution. The sensitivity of the model results to the initial sea spray profile further suggests there would be great benefit in better definition of the vertical profile of size resolved sea-spray for use in such model studies

Characterizing wind gusts in complex terrain

Wind gusts are a key driver of aerodynamic loading, especially for tall structures such a bridges and wind turbines. However, gust characteristics in complex terrain are not well understood and common approximations used to describe wind gust behavior may not be appropriate at heights relevant to wind turbines and other structures. Data collected in the Perdigão experiment are analyzed herein to provide a foundation for improved wind gust characterization and process-level understanding of flow intermittency in complex terrain. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars are used to conduct a detailed study of gust characteristics with a specific focus on the parent distributions of nine gust parameters (that describe velocity, time, and length scales), their joint distributions, height variation, and coherence in the vertical and horizontal planes. Best-fit distributional forms for varying gust properties show good agreement with those from previous experiments in moderately complex terrain but generate nonconservative estimates of the gust properties that are of key importance to structural loading. Probability distributions of gust magnitude derived from vertically pointing Doppler lidars exhibit good agreement with estimates from sonic anemometers despite differences arising from volumetric averaging and the terrain complexity. Wind speed coherence functions during gusty periods (which are important to structural wind loading) are similar to less complex sites for small vertical displacements (10 to 40&thinsp;m), but do not exhibit an exponential form for larger horizontal displacements (800 to 1500&thinsp;m).</p