Search Results (12)

Large-scale wind farms operate under highly unsteady atmospheric inflows, where transient turbulence, dynamic wake interactions, and inflow-wake coupling reduce energy production and exacerbate turbine loads. Over the past five years, advances in high-fidelity computational fluid dynamics (CFDs), large eddy simulation (LES), machine learning (ML)-based wake modeling, and multi-objective optimization have reshaped wind farm layout optimization under dynamic inflow conditions. This review synthesizes recent progress in five key areas: dynamic inflow and high-fidelity wake modeling (including LES-driven transient wake evolution and turbulence-resolved inflow generation), data-driven wake prediction, multi-objective layout optimization (considering the annual energy production (AEP), fatigue load constraints, and the levelized cost of energy (LCOE)), blockage modeling for complex terrain and yaw misalignment, and real-time optimization addressing inflow, turbine performance, and modeling uncertainties. Coupling transient wake models with surrogate-assisted multi-objective optimization enables a computationally efficient and physically consistent layout design. Key open challenges (dynamic wake controllability, real-time optimization under uncertainty, and integration with next-generation farm-level control systems) and future directions for enhancing large-scale wind farm resilience and cost-competitiveness are also identified. However, despite significant progress, existing models still face fundamental limitations, such as oversimplified treatment of complex turbulence structures, poor generalization under extreme or atypical conditions, and inadequate capture of long-timescale dynamic responses, which constrain their reliability in practical optimization settings. Full article

Static axial induction control and tilt control are two strategies that have the potential to increase power production in wind farms, mitigating wake effects and increasing the available power for downstream turbines. In this study, wind tunnel experiments are performed to evaluate the efficiency of these two techniques. First, the axial induction of upstream turbines in wind farms comprising two, three, and five turbines is modified through the tip-speed ratio. This strategy is found to be ineffective in increasing power extraction. Next, the power extraction and flow through a two-turbine wind farm are evaluated, considering different tilt angles for the upstream turbine, under two levels of incoming flow turbulence intensities and turbine spacing distances. It is shown that forward tilting increases the overall power extraction by deflecting the wake downwards and promoting the entrainment of high-speed fluid in the upper shear layer, regardless of the turbine spacing distance and turbulence intensity level. Also, the wake is seen to recover faster due to the increased shear between the wake and the outer flow. Tilting a turbine backward deflects the wake upwards and pulls low-speed flow from under the turbine into the wake space, increasing the available power for downstream turbines, but it is not enough to increase global power extraction. Moreover, since the wake deflection under backward tilting is not limited by ground blockage, it leads to larger secondary steering compared with forward tilting. Finally, it is demonstrated that the secondary steering of the downstream turbine’s wake influences the flow encountered by a turbine positioned farther downstream. Full article

Fog conditions at the offshore wind farm Horns Rev 2 were photographed on 16 April 2018. In this study, we present the results of an analysis of the meteorological conditions on the day of the photographs. The aim of the study was to examine satellite images, meteorological observations, wind turbine data, lidar data, reanalysis data, and wake and blockage model results to assess whether wind farm blockage was a likely cause for the formation of fog upstream of the wind farm. The analysis indicated the advection of warm and moist air mass from the southwest over a cool ocean, causing cold sea fog. Wind speeds at hub height were slightly above cut-in, and there was a strong veer in the shallow stable boundary layer. The most important finding is that the wake and blockage model indicated stagnant air mass arcs to the south and west of the wind farm. In the photographs, sea fog is visible in approximately the same area. Therefore, it is likely that the reduced wind triggered the sea fog condensation due to blockage in this area. A discrepancy between the blockage model and sea fog in the photographs appears in the southwest direction. Slightly higher winds might have occurred locally in a southwesterly direction, which may have dissolved sea fog. The wake model predicted long and narrow wind turbine wakes similar to those observed in the photographs. The novelty of the study is new evidence of wind farm blockage. It fills the gap in knowledge about flow in wind farms. Implications for future research include advanced modeling of flow phenomena near large offshore wind farms relevant to wind farm operators. Full article

By taking into account the turbine type, terrain, wind climate and layout, the effects of wind turbine wakes and other losses, engineering models enable the rapid estimation of energy yields for prospective and existing wind farms. We extend the capability of engineering models, such as the existing deep-array wake model, to account for additional losses that may arise due to the presence of clusters of wind farms, such as the global blockage effect and large-scale wake effects, which become more significant with increasing thermal stratification. The extended strategies include an enhanced wind-farm-roughness approach which assumes an infinite wind farm, and recent developments account for the upstream flow blockage. To test the plausibility of such models in capturing the additional blockage and wake losses in real wind farm clusters, the extended strategies are compared with large-eddy simulations of the flow through a cluster of three wind farms located in the German sector of the North Sea, as well as real measurements of wind power within these wind farms. Large-eddy simulations and wind farm measurements together suggest that the extensions of the Openwind model help capture the different flow features arising from flow blockage and cluster effects, but further model refinement is needed to account for higher-order effects, such as the effect of the boundary-layer height, which is not currently included in standard engineering models. Full article

The engineering wind farm models currently used in industry can assess power losses due to turbine wake effects, but the prediction of power losses due to farm blockage is still a challenge. In this study we demonstrate a new prediction method of farm blockage losses and a possible strategy to mitigate them for a large offshore wind farm in the North Sea, by combining a common engineering wind farm model ’FLORIS’ with the ’two-scale momentum theory’ of Nishino and Dunstan (2020). Results show that the farm blockage losses depend significantly on the ’wind extractability’ factor, which reflects the strength of mesoscale atmospheric response. For a typical range of the extractability factor (assessed using a numerical weather prediction model) the farm blockage losses are shown to vary between about 5% and 15% of the annual energy production (AEP). However, these losses may be mitigated by adjusting turbine operating points taking into account the wind extractability. It is shown that a simple adjustment of the blade pitch angle and tip-speed ratio used below the rated wind speed may increase the AEP by up to about 2%. Full article

A fully resolved (FR) NREL 5 MW turbine model is employed in two unsteady Reynolds-averaged Navier–Stokes (URANS) simulations (one with and one without the turbine tower) of a periodic atmospheric boundary layer (ABL) to study the performance of an infinitely large wind farm. The results show that the power reduction due to the tower drag is about 5% under the assumption that the driving force of the ABL is unchanged. Two additional simulations using an actuator disc (AD) model are also conducted. The AD and FR results show nearly identical tower-induced reductions of the wind speed above the wind farm, supporting the argument that the AD model is sufficient to predict the wind farm blockage effect. We also investigate the feasibility of performing delayed-detached-eddy simulations (DDES) using the same FR turbine model and periodic domain setup. The results show complex turbulent flow characteristics within the farm, such as the interaction of large-scale hairpin-like vortices with smaller-scale blade-tip vortices. The computational cost of the DDES required for a given number of rotor revolutions is found to be similar to the corresponding URANS simulation, but the sampling period required to obtain meaningful time-averaged results seems much longer due to the existence of long-timescale fluctuations. Full article

A prototype of a new physics-based wind resource assessment method is presented, which allows the prediction of upper limits to the performance of large wind farms (including the power loss due to wind farm blockage) in a site-specific and time-dependent manner. The new method combines the two-scale momentum theory with a numerical weather prediction (NWP) model to assess the “extractability” of wind, i.e., how high the wind speed at a given site can be maintained as we increase the number of turbines installed. The new method is applied to an offshore wind farm site in the North Sea to demonstrate that: (1) Only a pair of NWP simulations (one without wind farm and the other with wind farm with an arbitrary level of flow resistance) are required to predict the extractability. (2) The extractability varies significantly from time to time, which may cause more than 30% of change in the upper limit of the performance of medium-to-high-density offshore wind farms. These results suggest the importance of considering not only the natural wind speed but also its extractability in the prediction of (both long- and short-term) power production of large wind farms. Full article

The paper provides novel insights into the physics behind the wind turbine and wind farm blockages as well as their effects on the energy yield based on the momentum and energy balance. The current work presents blockage effects at two scales: the local scale and the wind farm scale. We clarify the combined effect of local blockages when more than one turbine is present. The work demonstrates why two turbines, which are positioned one behind the other, induce a mutual decrease in energy yield. When the turbines are placed in a row, there is an increase of energy from the end to the middle of the row because of the restriction of the expansion flow. As in the case of two turbines placed behind each other, back rows induce a power decrease for the rows in front of them and the effect increases from the edge to the center. The work also elucidates for the first time how the power output of an isolated row has a maximum in the center, whereas, in a wind farm, wind turbines on the edge of the first row could have maximum power. The findings are supported by CFD. Full article

Based on data from 2083 wind turbines installed in Sweden from 1988 onwards, the accuracy of the predictions of the annual energy production (AEP) from the project planning phases has been compared to the actual wind-index-corrected production. Both the electricity production and the predicted AEP come from Vindstat, a database that collects information directly from wind turbine owners. The mean error for all analyzed wind turbines was 13.0%, which means that, overall, the predicted AEP has been overestimated. There has been an improvement of accuracy with time with an overestimation of 8.2% for wind turbines installed in the 2010s, however, the continuous improvement seems to have stagnated around 2005 despite better data availability and continuous refinement of methods. Dividing the results by terrain, the error is larger for wind turbines in open and flat terrain than in forest areas, indicating that the reason behind the error is not the higher complexity of the forest terrain. Also, there is no apparent increase of error with wind farm size which could have been expected if wind farm blockage effect was a main reason for the overestimations. Besides inaccurate AEP predictions, a higher-than-expected performance decline due to inadequate maintenance of the wind turbines may be a reason behind the AEP overestimations. The main sources of error are insecurity regarding the source of AEP predictions and the omission of mid-life alterations of rated power. Full article

The effect of separation distance between turbines on overall cluster performance were simulated using computational fluid dynamics software and we found that at a distance equivalent to two rotors, there was an improvement of +8.06% in the average performance of the cluster compared to a single, isolated turbine. A very small improvement in performance was noted at the equivalent distance of 12 rotor diameters. The performances of three individual turbines in pyramid- and inverted pyramid-shaped vertical axis wind turbine clustered farm configurations with varying oblique angles at a fixed spacing of two equivalent rotor diameters were also investigated. The design experiment involves the simulation of test cases with oblique angles from 15° to 165° at an interval of 15° and the turbines were allowed to rotate through 18 full rotations. The results show that the left and right turbines increase in performance as the angle with respect to the streamline axis increases, with the exception of the 165° angle. The center turbine, meanwhile, attained its maximum performance at a 45° oblique angle. The maximum cluster performance was found to be in the configuration where the turbines were oriented in a line (i.e., side by side) and perpendicular to the free-stream wind velocity, exhibiting an overall performance improvement of 9.78% compared to the isolated turbine. Other array configurations show improvements ranging from 6.58% to 9.57% compared to the isolated turbine, except in the extreme cases of 15° and 165°, where a decrease in the cluster performance was noted due to blockage induced by the left and right turbines, and the center turbines, respectively. Full article

Measurements taken before and after the commissioning of three wind farms reveal that the wind speeds just upstream of each wind farm decrease relative to locations farther away after the turbines are turned on. At a distance of two rotor diameters upstream, the average derived relative slowdown is 3.4%; at seven to ten rotor diameters upstream, the average slowdown is 1.9%. Reynolds-Averaged Navier-Stokes (RANS) simulations point to wind-farm-scale blockage as the primary cause of these slowdowns. Blockage effects also cause front row turbines to produce less energy than they each would operating in isolation. Wind energy prediction procedures in use today ignore this effect, resulting in an overprediction bias that pervades the entire wind farm. Full article

In this study, large-eddy simulations are performed to investigate the flow inside and around large finite-size wind farms in conventionally-neutral atmospheric boundary layers. Special emphasis is placed on characterizing the different farm-induced flow regions, including the induction, entrance and development, fully-developed, exit and farm wake regions. The wind farms extend 20 km in the streamwise direction and comprise 36 wind turbine rows arranged in aligned and staggered configurations. Results show that, under weak free-atmosphere stratification ( $\Gamma =1$ K/km), the flow inside and above both wind farms, and thus the turbine power, do not reach the fully-developed regime even though the farm length is two orders of magnitude larger than the boundary layer height. In that case, the wind farm induction region, affected by flow blockage, extends upwind about 0.8 km and leads to a power reduction of 1.3% and 3% at the first row of turbines for the aligned and staggered layouts, respectively. The wind farm wake leads to velocity deficits at hub height of around 3.5% at a downwind distance of 10 km for both farm layouts. Under stronger stratification ( $\Gamma =5$ K/km), the vertical deflection of the subcritical flow induced by the wind farm at its entrance and exit regions triggers standing gravity waves whose effects propagate upwind. They, in turn, induce a large decelerating induction region upwind of the farm leading edge, and an accelerating exit region upwind of the trailing edge, both extending about 7 km. As a result, the turbine power output in the entrance region decreases more than 35% with respect to the weakly stratified case. It increases downwind as the flow adjusts, reaching the fully-developed regime only for the staggered layout at a distance of about 8.5 km from the farm edge. The flow acceleration in the exit region leads to an increase of the turbine power with downwind distance in that region, and a relatively fast (compared with the weakly stratified case) recovery of the farm wake, which attains its inflow hub height speed at a downwind distance of 5 km. Full article