The climate of the northeast Atlantic region comprises substantial decadal variability in storminess. It also exhibits strong inter- and intra-annual variability in extreme high and low wind speed ...episodes. Here the authors quantify and discuss causes of the variability seen in the U.K. wind climate over the recent period 1980–2010. Variations in U.K. hourly mean (HM) wind speeds, in daily maximum gust speeds and in associated wind direction measurements, made at standard 10-m height and recorded across a network of 40 stations, are considered. The Weibull distribution is shown to generally provide a good fit to the hourly wind data, albeit with the shape parameterkspatially varying from 1.4 to 2.1, highlighting that the commonly assumedk= 2 Rayleigh distribution is not universal. It is found that the 10th and 50th percentile HM wind speeds have declined significantly over this specific period, while still incorporating a peak in the early 1990s. The authors’ analyses place the particularly ‘‘low wind’’ year of 2010 into longer-term context and their findings are compared with other recent international studies. Wind variability is also quantified and discussed in terms of variations in the exceedance of key wind speed thresholds of relevance to the insurance and wind energy industries. Associated interannual variability in energy density and potential wind power output of the order of ±20% around the mean is revealed. While 40% of network average winds are in the southwest quadrant, 51% of energy in the wind is associated with this sector. The findings are discussed in the context of current existing challenges to improve predictability in the Euro-Atlantic sector over all time scales.
Wind speed fluctuation at wind farms leads to intermittent and unstable power generation with diverse amplitudes and frequencies. Compressed air energy storage (CAES) is an energy storage technology ...which not only copes with the stochastic power output of wind farms, but it also assists in peak shaving and provision of other ancillary grid services. In this paper, a CAES facility is proposed for two adjacent wind farms, Abhar and Kahak sites in Iran, with a total nominal power of 162.5 MW. To assess site peak profiles and storage potential, annual thermodynamic and wind assessments are carried out predominantly for the three critical months of the year requiring mitigation of electricity scarcity (July, August, and September). Results indicate that the sustained wind speed in July at both the Abhar and Kahak sites is higher than the other two critical months. Around 93, 74 and 60 MW stored power in the CAES facility are added to the grid during 5 h of peak demand in July, August and September months with round trip efficiencies of 52, 47, and 43%, respectively.
•An environmentally-friendly CAES system integrated with two adjacent wind farms for peak shaving applications.•Wind assessment of the Abhar and Kahak sites in three critical months and annually.•Thermodynamic analysis of the CAES system in critical months and annually.•Drawing wind roses for the Abhar and Kahak wind sites.
Wind-Turbine and Wind-Farm Flows: A Review Porté-Agel, Fernando; Bastankhah, Majid; Shamsoddin, Sina
Boundary-layer meteorology,
01/2020, Letnik:
174, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Wind energy, together with other renewable energy sources, are expected to grow substantially in the coming decades and play a key role in mitigating climate change and achieving energy ...sustainability. One of the main challenges in optimizing the design, operation, control, and grid integration of wind farms is the prediction of their performance, owing to the complex multiscale two-way interactions between wind farms and the turbulent atmospheric boundary layer (ABL). From a fluid mechanical perspective, these interactions are complicated by the high Reynolds number of the ABL flow, its inherent unsteadiness due to the diurnal cycle and synoptic-forcing variability, the ubiquitous nature of thermal effects, and the heterogeneity of the terrain. Particularly important is the effect of ABL turbulence on wind-turbine wake flows and their superposition, as they are responsible for considerable turbine power losses and fatigue loads in wind farms. These flow interactions affect, in turn, the structure of the ABL and the turbulent fluxes of momentum and scalars. This review summarizes recent experimental, computational, and theoretical research efforts that have contributed to improving our understanding and ability to predict the interactions of ABL flow with wind turbines and wind farms.
The rapid growth of offshore wind energy requires accurate modeling of the wind resource, which can be depleted by wind farm wakes. Turbulence dissipation rate (ϵ) governs the accuracy of model ...predictions of hub‐height wind speed and the development and erosion of wakes. Here we assess the variability of turbulence kinetic energy and ϵ using 13 months of observations from a profiling lidar deployed on a platform off the Massachusetts coast. Offshore, ϵ is 2 orders of magnitude smaller than onshore, with a subtle diurnal cycle. Wind direction influences the annual cycle of turbulence, with larger values in winter when the wind flows from the land, and smaller values in summer, when the wind flows from open ocean. Because of the weak turbulence, wind plant wakes will be stronger and persist farther downwind in summer.
Key Points
Strength and peturbulence dissipation rate offshore is smaller than onshore, with a weak diurnal cycle
Dissipation rate is larger when flow is from the land, usually in wintertime at this site
Large wind veer is often associated with low turbulence at the site
•Framework for preliminary assessment of urban wind energy potential at large scales.•Extension of the knowledge on small-scale wind power potential assessment.•Focus on the roof-mounted wind ...turbines.•1st and 2nd step: collecting potential building data and respective wind speed characteristics.•3rd step: obtaining turbine characteristics from manufacturer, modeling or experiment.•Last step: estimating the annual urban wind energy production (AEP).
Urban wind energy can provide a decentralized local source of energy for residential areas and reduce the cost of energy by avoiding the losses/costs of long-distance energy transmission. In this perspective, a preliminary assessment of urban wind energy is highly desired by turbine developers, investors and policy makers. However, given the large number of parameters involved, predictions of the wind energy potential in urban areas are very challenging. The present paper, therefore, intends to present a straightforward framework to provide a preliminary and large-scale assessment of the urban wind energy potential, i.e. at city or country scales, for roof-mounted turbines. The framework is based on four main steps: (i) collecting the building data, i.e. the number of potential candidate high-rise buildings and their height and rooftop surface area; (ii) obtaining the annual mean wind speed statistics at the height of these buildings and sorting the building data based on these statistics; (iii) obtaining the turbine characteristics and determining the average number of turbines per building roof; (iv) calculating the annual energy production (AEP). The application of the framework is then illustrated at the country scale for the Netherlands. In this case, the urban wind energy potential is assessed by considering the installation of 18,156 small wind turbines on the roofs of 1513 existing high-rise buildings in 12 major cities in the Netherlands, yielding an annual energy production of 150.1 GWh.
Wind farms affect local weather and microclimates;
hence, parameterizations of their effects have been developed for numerical weather prediction models.
While most wind farm parameterizations (WFPs) ...include drag effects of wind farms, models differ on whether or not an additional
turbulent kinetic energy (TKE) source should be included in these
parameterizations to simulate the impact of wind farms on the boundary layer.
Therefore, we use aircraft measurements above large offshore wind farms in stable conditions to evaluate
WFP choices. Of the three case studies we examine, we find the simulated ambient background
flow to agree with observations of temperature stratification and winds. This agreement allows us to
explore the sensitivity of simulated wind farm effects with respect to modeling choices
such as whether or not to include a TKE source, horizontal resolution, vertical resolution
and advection of TKE. For a stably stratified marine atmospheric boundary layer (MABL), a TKE source and a horizontal resolution
on the order of 5 km or finer are necessary to represent the impact of offshore wind farms on the MABL.
Additionally, TKE advection results in excessively reduced TKE over the wind farms, which in turn causes
an underestimation of the wind speed deficit above the wind farm. Furthermore, using fine vertical
resolution increases the agreement of the simulated wind speed with satellite observations of surface wind speed.
The wind power input to near‐inertial (NI) motions is studied using a global eddy‐permitting ocean general circulation model. The model is forced by high‐ (1‐hourly, at 0.35° resolution) and ...low‐resolution (6‐hourly, at 1.875° resolution) wind data. A change from low‐ to high‐resolution forcing results in an increase in NI kinetic energy by a factor three and raises the wind‐generated power input to NI motions from 0.3 TW to 1.1 TW. Time and space filtering of the wind fields yield less kinetic energy, with a larger drop from time filtering. This strong sensitivity to wind forcing points to a possible underestimation of the wind‐generated energy available for deep ocean mixing in previous studies based on low‐resolution winds.
Key Points
Near‐inertial motions in the ocean are generated by hourly wind forcingTemporal resolution of the winds has a stronger effect than spatial resolutionThe total wind‐induced power input to near‐inertial motions is 1.1 TW
Many solar wind parameters correlate with one another, which complicates the causal‐effect studies of solar wind driving of the magnetosphere. Conditional mutual information (CMI) is used to untangle ...and isolate the effect of individual solar wind and magnetospheric drivers of the radiation belt electrons. The solar wind density (nsw) negatively correlates with electron phase space density (PSD; average energy ∼1.6 MeV) with time lag (τ) = 15 hr. The effect of nsw has been attributed to magnetopause shadowing or other loss mechanisms, but when the effect of solar wind velocity (Vsw) is removed, τ shifts to 7–11 hr, which is a more accurate time scale for this process. The peak correlation between Vsw and PSD shifts from τ = 30–50 to 44–56 hr, when the effect of nsw is removed. This suggests that the time scale for electron acceleration to 1–2 MeV is about 44–56 hr following Vsw enhancements. The effect of nsw is significant only at L* = 4.5–6 (L* > 6 is highly variable), whereas the effect of Vsw is significant only at L* = 3.5–6.5. The peak response of PSD to Vsw is the shortest and most significant at L* = 4.5–5.5. As time progresses, the peak response broadens and shifts to higher τ at higher and lower L*, consistent with local acceleration at L* = 4.5–5.5 followed by outward and inward diffusion. The outward radial diffusion time scale at L* = 5–6 is ∼40 hr per RE.
Plain Language Summary
Many solar wind parameters correlate with one another, which complicates the causal‐effect studies of solar wind driving of the magnetosphere. We use conditional mutual information, which is part of information theory, to untangle and isolate the effect of individual solar wind and magnetospheric drivers of the radiation belt electrons. For example, the solar wind density negatively correlates with electron phase space density (PSD) (average energy ∼1.6 MeV) with the response time lag of 15 hr. This has been attributed to the electron loss process such as magnetopause shadowing. The time lag suggests the time scale for this process is 15 hr. However, when the effect of solar wind velocity is removed, the time lag is 7–11 hr, which is a more accurate time scale for this process. As another example, the time lag of the correlation between solar wind velocity and PSD shifts from 30 to 50 to 44–56 hr, when the effect of solar wind density is removed. This suggests that the time scale for electron acceleration to 1–2 MeV is about 44–56 hr following the solar wind velocity enhancements. We also show that the effects of solar wind velocity and density have dependence on radial distance.
Key Points
The effect of nsw on radiation belt electrons is significant only at L* = 4.5–6 and not significant at L* < 4.5
The effect of Vsw on radiation belt electrons is significant at L* = 3.5–6.5 and not significant at L* < 3.5
The radiation belt electron response time lag to Vsw suggests local acceleration at L* = 4–5.5 followed by outward and inward diffusion
A control-oriented large eddy simulation (LES) code is developed to predict wind turbine wake and is validated by laboratory-scale and utility-scale wind turbines. Firstly, the wind turbine control ...algorithms including torque, pitch and yaw controls are implemented in LES with Actuator Line Model (ALM). Two sets of numerical simulations under uniform inflow with time-varying wind speeds and wind directions are performed. The simulated thrust and torque forces agree well with those calculated by the aeroelastic code, FAST. The predicted mean velocity and turbulence intensity in the wake of a laboratory-scale wind turbine show favorable agreement with those measured in wind tunnel experiments. The blade rotation induced dynamic effects on wake flows and rotor loading are well reproduced by ALM. Finally, numerical simulations for a utility-scale wind turbine are conducted, in which the ambient flow filed with time-varying wind speeds and wind directions are generated based on the wind condition measured by the Met-mast and the Coriolis force effect is incorporated as well. The predicted time series of control signals and power production agree well with the wind turbine SCADA data and the predicted mean wind speed in the wake region show favorable agreement with those measured by Doppler scanning LiDAR.
New contribution of this paper to the field:•A control-oriented large eddy simulation code for wind turbine is developed.•Systematic comparison of ADM-R and ALM regarding wake and loading is performed.•The developed code is validated by field data of a utility-scale wind turbine.
•The wind loads of a moving vehicle model on a bridge installed with a solid wind barrier are tested by wind tunnel tests.•The effects of different heights of wind barriers and wind directions on the ...wind loads acting on the vehicles are analyzed.•The protective effects of wind barriers for the moving vehicle are discussed.
A wind tunnel experimental study is conducted to study the wind loads acting on a moving vehicle model on a bridge installed with a solid wind barrier. The repeatability of the tests is verified by the cases with different wind and vehicle speeds. For comparison purposes, the difference of the results between the static and moving vehicle models are discussed. The effects of different heights of wind barriers and wind directions on the wind loads acting on the vehicles are analyzed. Finally, the wind loads acting on the vehicles are further evaluated to assess the potential impact of wind barriers on traffic safety. The results show that the relative movement between the vehicle, bridge and solid wind barriers affects the aerodynamic characteristics of the vehicle. The variation patterns of aerodynamic characteristics with different yaw angles are altered by the solid wind barriers with different heights, and there is a most unfavorable yaw angle for the protective effect of solid wind barriers in terms of side force. The wind direction has some impact on the five-component coefficients of vehicles after installing a solid wind barrier. However, the solid wind barrier has similar protective efficiency in terms of the side force acting on the vehicle under different wind directions.
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