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This investigation presents a modelling strategy for wind-energy studies in complex terrains using computational fluid dynamics (CFD). A model, based on an unsteady Reynolds Averaged Navier-Stokes (URANS) approach with a modified version of the standard k-ε model, is applied. A validation study based on the Leipzig experiment shows the ability of the model to simulate atmospheric boundary layer characteristics such as the Coriolis force and shallow boundary layer. By combining the results of the model and a design of experiments (DoE) method, we could determine the degree to which the slope, the leaf area index, and the forest height of an escarpment have an effect on the horizontal velocity, the flow inclination angle, and the turbulent kinetic energy at critical positions. The DoE study shows that the primary contributor at a turbine-relevant height is the slope of the escarpment. In the second step, the method is extended to the WINSENT test site. The model is compared with measurements from an unmanned aircraft system (UAS). We show the potential of the methodology and the satisfactory results of our model in depicting some interesting flow features. The results indicate that the wakes with high turbulence levels downstream of the escarpment are likely to impact the rotor blade of future wind turbines.

Abstract. Understanding the uncertainties of Wind Resource Assessments (WRA) is key to reducing project risks, and this is particularly challenging in mountainous terrain. In the academic literature, many complex flow sites have been investigated, but they all focus on comparing wind speeds from selected wind directions, and do not focus on the overall AEP. In this work, a range of simulations are carried out with seven different wind modelling tools at five different complex terrain sites and the results compared to wind speed measurements at validation locations. This is then extended to AEP estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. This is firstly because there is a surprisingly large variation in energy production calculation techniques between tools, and secondly because the AEP depends strongly upon the relative strength and occurrence of the wind speed in the most commonly-occurring wind direction sectors. This means that the wind model that produces the most accurate wind predictions for a certain wind direction over a certain time period does not always result in the most suitable model for the AEP estimation of a given complex terrain site. In fact, the large number of steps within the WRA process often lead to the choice of wind model being less important for the overall WRA accuracy than would suggest by only looking at wind speeds. It is therefore concluded that it is vitally important for researchers to consider overall AEP – and all the steps towards calculating it – when evaluating simulation accuracies of flow over complex terrain.

Abstract. Understanding the uncertainties of wind resource assessments (WRAs) is key to reducing project risks, and this is particularly challenging in mountainous terrain. In the academic literature, many complex flow sites have been investigated, but they all focus on comparing wind speeds from selected wind directions and do not focus on the overall annual energy production (AEP). In this work, the importance of converting wind speed errors into AEP errors when evaluating wind energy projects is highlighted by comparing the results of seven different WRA workflows at five complex terrain sites. Although a systematic study involving the investigation of all possible varying parameters is not within the scope of this study, the results allow some of the different factors that could lead to this discrepancy being identified. The wind speed errors are assessed by comparing simulation results to wind speed measurements at validation locations. This is then extended to AEP estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. This is due to the specific conditions at the site, to differences in workflow set-ups between the sites and to differences in workflow AEP calculation methods. The results demonstrate the complexity of the combined factors contributing to WRA errors – even without including wake effects and other losses. This means that the wind model that produces the most accurate wind predictions for a certain wind direction over a certain time period does not always result in the most suitable model for the AEP estimation of a given complex terrain site. In fact, the large number of steps within the WRA process often lead to the choice of wind model being less important for the overall WRA accuracy than would be suggested by only looking at wind speeds. It is therefore concluded that it is vitally important for researchers to consider overall AEP – and all the steps towards calculating it – when evaluating simulation accuracies of flow over complex terrain. Future work will involve a systematic study of all the factors that could contribute to this effect.