Integration of atmospheric stability in wind resource assessment through multi-scale coupling method

The mean wind speed, wind speed distribution, wind shear exponent and wind power density would be affected by terrain, atmospheric stability and equipments, are important parameters in the process of wind resource assessment. However, Conventional methods have always ignored the effects of atmospheric stability, which might make results deviate from the actual situation. The main purpose of this work is to present effects of atmospheric stability on wind resource parameters. Taking 4 years’ measured data as case study, six parameters in wind resource assessing are calculated under different atmospheric stabilities; influential rules of above assessing parameters under varying atmospheric stability are analyzed respectively in order to obtain a systemic relationship between atmospheric stability and wind resource characteristic parameters which is tested by 5th year’s data. Based on these studies, a hub height wind speed extrapolation model is proposed. This model could use different wind shear exponent under various atmospheric stabilities during wind speed extrapolating, rather than an average wind shear for the whole wind farm. In such way, much accurate extrapolating hub wind speed in wind resource assessing is employed, and errors in wind resource assessment might decline. Results validate the simplicity, accuracy and practicality of the proposed method.

Accurate wind resource assessment is essential for offshore wind energy development, particularly in nearshore sites where atmospheric stability and internal boundary layers significantly influence the horizontal wind distribution. In this study, we investigated wind estimation methods using a high-resolution, 100 m grid Weather Research and Forecasting (WRF) model and coastal onshore wind measurement data. Five estimation methods were evaluated, including a control WRF simulation without on-site measurement data (CTRL), observation nudging (NDG), two offline methods—temporal correction (TC) and the directional extrapolation method (DE)—and direct application of onshore measurement data (DA). Wind speed and direction data from four nearshore sites in Japan were used for validation. The results indicated that TC provided the most accurate wind speed estimate results with minimal bias and relatively high reproducibility of temporal variations. NDG exhibited a smaller standard deviation of bias and a slightly higher correlation with the measured time series than CTRL. DE could not reproduce temporal variations in the horizontal wind speed differences between points. These findings suggest that TC is the most effective method for assessing nearshore wind resources and is thus recommended for practical use.

Multi-scale Coupling Analysis of Partial Transverse Ventilation System in an Underground Road Tunnel

Sensitivity of simulated wind power under diverse spatial scales and multiple terrains using the weather research and forecasting model

Sensitivity analysis of WRF-CFD-based downscaling methods for evaluation of urban pedestrian-level wind

ABSTRACTDirect estimations of turbulent fluxes and atmospheric stability were performed from a sonic anemometer at 50 m height on a meteorological mast at the Horns Rev wind farm in the North Sea. The stability and flux estimations from the sonic measurements are compared with bulk results from a cup anemometer at 15 m height and potential temperature differences between the water and the air above. Surface flux estimations from the advanced weather research and forecast (WRF) model are also validated against the sonic and bulk data. The correlation between the sonic and bulk estimates of friction velocity is high and the highest among all velocity comparisons. From the sonic–bulk–WRF inter‐comparison, it is found that the atmospheric stability measures at the sonic height tend to be closer to the neutral value than the WRF and bulk estimates, which are performed within an air layer closer to the surface, not only from a systematic bulk and WRF under‐prediction of the friction velocity when compared with the sonic value but also because of the lower magnitude of the sonic heat flux compared with that from the WRF simulations. Although they are not measured but parameterized or estimated, the bulk–WRF comparisons of friction velocity and 10 m wind speed show good agreement. It is also shown that on a long‐term basis, the WRF and bulk estimates of stability are nearly equal and that a correction towards a slightly stable atmospheric condition has to be applied to the long‐term wind profile at Horns Rev and at other locations over the North Sea, the correction being larger for points close to the coast. Copyright © 2011 John Wiley & Sons, Ltd.

Accurate forecast and simulation of near-surface wind is a great challenge for numerical weather prediction models due to the significant transient and intermittent nature of near-surface wind. Based on the analyses of the impact of assimilating in situ and Advanced Tiros Operational Vertical Sounder (ATOVS) satellite radiance data on the simulation of near-surface wind during a severe wind event, using the new generation mesoscale Weather Research and Forecasting (WRF) model and its three-dimensional variational (3DVAR) data assimilation system, the dynamic downscaling of near-surface wind is further investigated by coupling the microscale California Meteorological (CALMET) model with the WRF and its 3DVAR system. Results indicate that assimilating in situ and ATOVS radiance observations strengthens the airflow across the Alataw valley and triggers the downward transport of momentum from the upper atmosphere in the downstream area of the valley in the initial conditions, thus improving near-surface wind simulations. Further investigations indicate that the CALMET model provides more refined microtopographic structures than the WRF model in the vicinity of the wind towers. Although using the CALMET model achieves the best simulation of near-surface wind through dynamic downscaling of the output from the WRF and its 3DVAR assimilation, the simulation improvements of near-surface wind speed are mainly within 1 m s−1. Specifically, the mean improvement proportions of near-surface wind speed are 64.8% for the whole simulation period, 58.7% for the severe wind period, 68.3% for the severe wind decay period, and 75.4% for the weak wind period. The observed near-surface wind directions in the weak wind conditions are better simulated in the coupled model with CALMET downscaling than in the WRF and its 3DVAR system. It is concluded that the simulation improvements of CALMET downscaling are distinct when near-surface winds are weak, and the downscaling effects are mainly manifested in the simulation of near-surface wind directions.

Urban distributed energy systems play a crucial role in the development of sustainable and low-carbon cities. Evaluating urban wind resources is essential for effective wind energy harvesting, which requires detailed information about the urban flow field. Computational fluid dynamics (CFD) has emerged as a viable and scalable method for assessing urban wind resources. This review paper synthesizes the characteristics of the urban wind environment and resources, outlines the general framework for CFD-aided wind resource assessment, and addresses future challenges and perspectives. It highlights the critical need to optimize wind energy harvesting in complex built environments. The paper discusses the conditions for urban wind resource assessment, particularly the extraction of boundary conditions and the performance of small wind turbines (SWTs). Additionally, it notes that while large eddy simulation (LES) is a high-fidelity model, it is still less commonly used compared to Reynolds-averaged Navier–Stokes (RANS) models. Several challenges remain, including the broader adoption of high-fidelity LES models, the integration of wake models and extreme conditions, and the application of these methods at larger scales in real urban environments. The potential of multi-scale modeling approaches to enhance the feasibility and scalability of these methods is also emphasized. The findings are intended to promote the utilization and further development of CFD methods to accelerate the creation of resilient and energy-efficient cities, as well as to foster interdisciplinary innovation in wind energy systems.

To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.

A comprehensive review of wind resource assessment

This study is intended to simulate and better understand mean and extreme precipitation over the Loess Plateau (LP) in China using the Weather Research and Forecasting (WRF) model. We performed a long-term (1980–2011) simulation with the WRF model at a 10 km horizontal resolution, forced by the ERA-Interim reanalysis. A series of sensitivity experiments were conducted to investigate the influence of resolution and physical parameterization schemes on the simulation ability of the WRF model over the LP. The region has a dominant semi-arid climate and is adversely affected by extreme precipitation. Results show that the WRF model produced better simulations at a 10 km resolution for the LP with complex terrain than it did at coarser resolutions. WRF simulations for precipitation over the LP were most sensitive to cumulus schemes, moderately sensitive to planetary boundary layer schemes, and less sensitive to microphysics schemes. The WRF model not only adequately captured the spatial distribution of precipitation in the LP but also reproduced its magnitude and variability at different time scales. Moreover, the WRF model captured 73% of the observed extreme precipitation events with an intensity of more than 12 mm day− 1, which cause substantial soil erosion in the LP. Although it was unable to capture 27% of the observed events, the WRF model reasonably reproduced ERA-Interim extreme precipitation and its dynamic processes. These analyses strongly suggest that the biased extreme precipitation generated with the WRF model most likely resulted from inaccurate ERA-Interim forcing data. This study provides a better understanding of extreme precipitation simulations as well as insight into predictions of soil erosion in the LP.

In this paper, we compare and verify the prediction accuracy and feasibility for wind resources on a wind farm using the Weather Research and Forecasting (WRF) model, which is a numerical weather-prediction model. This model is not only able to simulate local weather phenomena, but also does not require automatic weather station (AWS), satellite, or meteorological mast data. To verify the feasibility of WRF to predict the wind resources required from a wind farm pre-feasibility study, we compare and verify measured wind data and the results predicted by WAsP. To do this, we use the Pyeongdae and Udo sites, which are located on the northeastern part of Jeju island. Together with the measured data, we use the results of annual and monthly mean wind speed, the Weibull distribution, the annual energy production (AEP), and a wind rose. The WRF results are shown to have a higher accuracy than the WAsP results. We therefore confirmed that WRF wind resources can be used in wind farm pre-feasibility studies.

Performance evaluation of the Weather Research and Forecasting (WRF) model for assessing wind resource in Greece

This study investigates the performance of two commonly used planetary boundary layer (PBL) schemes, the local turbulence closure (Mellor-Yamada-Janjić: MYJ) and the nonlocal turbulence closure (Yonsei University: YSU), over the tropical coastal region of Shenzhen, China, using the Weather Research and Forecasting (WRF) model. The simulation results are evaluated by comparing them to various measurements. Additionally, we also investigate the performance of the PBL schemes over different land cover types and their ability to reproduce the physical relationships between several pairs of interrelated near-surface atmospheric parameters. The results show that both PBL schemes underestimate temperature and PBL height and overestimate wind speed and turbulent fluxes throughout the whole day, but this can be improved by increasing horizontal resolution. We find that the overestimation of wind speed and turbulent heat fluxes is mainly caused by the strong mechanical mixing in the model. Some improvements are required such as more accurate surface morphology parameters and momentum transport coefficients. Overall, the YSU scheme outperforms the MYJ scheme at all horizontal resolutions over complex terrain in terms of near-surface variables, PBL vertical structures, and the internal relations between turbulent heat fluxes and atmospheric instability. However, the difference between the two PBL schemes, varying over different land covers and with time periods and altitudes within the PBL, is generally greater over the land than over the sea for thermal-related variables (especially during daytime), while for variables associated with water vapor is the opposite. This highlights the importance of the underlying surface for the performance of PBL schemes and the need to select appropriate PBL schemes for simulating the evolution of PBL processes over different land covers.

[Introduction] Accurate wind resource data is of great significance to wind resource evaluation and power generation calculation of wind farm. Due to mechanical failure, weather factors and human influence, there are many problems in wind speed data acquisition, such as short collection time, many discontinuities, data distortion and so on, which bring a lot of trouble to the evaluation of wind resources. [Method] At present, the traditional interpolation method based on MCP method for discontinuous data interpolation and fitting in the wind power industry is not accurate enough. In this paper, the wind resource data prediction scheme based on neural network algorithm was proposed for wind speed interpolation of wind turbine and wind speed interpolation of wind measurement mast. [Result] The results show that the interpolation effect of BP neural network is better than the traditional interpolation method, and the wind speed interpolation of anemometer tower in flat terrain is better than that in complex terrain. [Conclusion] The research shows that the wind speed interpolation technology based on BP neural network method is suitable for wind speed interpolation application of wind farm, and the accuracy of wind resource assessment is significantly improved.