Large‐eddy simulations of the Lillgrund wind farm

Abstract

The power production of the Lillgrund wind farm is determined numerically using large-eddy simulations and compared with measurements. In order to simulate realistic atmospheric conditions, pre-generated turbulence and wind shear are imposed in the computational domain. The atmospheric conditions are determined from data extracted from a met mast, which was erected prior to the establishment of the farm. In order to allocate most of the computational power to the simulations of the wake flow, the turbines are modeled using an actuator disc method where the discs are imposed in the computational domain as body forces which for every time step are calculated from tabulated airfoil data. A study of the influence of imposed upstream ambient turbulence is performed and shows that higher levels of turbulence results in slightly increased total power production and that it is of great importance to include ambient turbulence in the simulations. By introducing ambient atmospheric turbulence, the simulations compare very well with measurements at the studied inflow angles. A final study aiming at increasing the farm production by curtailing the power output of the front row turbines and thus letting more kinetic energy pass downstream is performed. The results, however, show that manipulating only the front row turbines has no positive effect on the farm production, and therefore, more complex curtailment strategies are needed to be tested.

Towards practical dynamic induction control of wind farms: analysis of optimally controlled wind-farm boundary layers and sinusoidal induction control of first-row turbines

“…This is not surprising as R1 produces the most power of all rows and typically leaves the deepest wakes, causing second-row turbines to perform poorly in aligned wind-farm layouts (see, e.g., Porté-Agel et al, 2013;Nilsson et al, 2015;and Stevens et al, 2016). At the other end of the spectrum, the last row (R12) is the least useful.…”

“…In contrast, control strategies at the farm level allow the wake interaction to be influenced and promise to improve overall wind-farm performance by improving wind conditions for downstream turbines. This can be achieved by redirecting propagating wakes (yaw control; see, e.g., Fleming et al, 2014;Gebraad et al, 2016;Campagnolo et al, 2016) or by affecting the induced wake velocity deficits (axial induction control; see, e.g., Nilsson et al, 2015;Annoni et al, 2016;Bartl and Saetran, 2016). A more exhaustive survey of wind-farm control in a broader context can be found in Knudsen et al (2015) and Boersma et al (2017).…”

Simulation of wind turbine wakes using the actuator line technique

“…Other recent simulations of full wind farms include the work by Ivanell et al [23], Nilsson et al [24] and Yang et al [25,26]. In spite of the important insights already obtained from the use of the AL model, there is a need for further simulations of wind turbine wakes for studying basic features of wakes in order to develop less complex engineering models for design purposes.…”

Comparative CFD study of the effect of the presence of downstream turbines on upstream ones using a rotational speed control system

“…1(left), and it begins at a z value of 6.4R. The resolution of 0.1R was found to be satisfactory in a recently performed study, see Nilsson et al [9]. In the latter, a grid sensitivity study showed that the use of a finer resolution and larger simulation domain had negligible impact on the production results.…”

“…An incoming velocity of 8±0.5m/s is considered, while the turbulence intensity was measured to be equal to approximately 5% for both inflow sectors. For more information about layout of the farm as well as the method used to filter the experimental data in the Lillgrund wind farm, the reader is referred to [9].…”