Modelling cluster wakes and wind farm blockage

Abstract

We present two new models for wind turbine interaction effects and a recipe for combining them. The first model is an extension of the Park model, which explicitly incorporates turbulence, both the ambient atmospheric turbulence and the turbulence generated in the wake itself. This Turbulence Optimized Park model is better equipped to describe wake recovery over long distances such as between wind farms, where the wake expansion slows down as the turbine-generated turbulence decays. The second model is a first version of a full engineering wind farm blockage model. In the same vein as the wake model it adds blockage contributions from the individual wind turbines to form an aggregated wind farm scale blockage effect that can be incorporated directly into the park power curve and annual energy calculations. The wake model and the blockage model describe downstream and upstream turbine interaction effects, respectively. They are coupled as the outputs of one model are the inputs to the other model and vice versa. We describe how this coupling is achieved through an iterative process. We give early stage examples of the validation of the two models and discuss how they might be further validated and improved in the future.

A Study of Blockage Effects at the Wind Turbine and Wind Farm Scales

“…A wind farm blockage model that does not consider the two effects could not reproduce well the variation introduced by the presence of multiple turbines. A full engineering wind farm blockage model was proposed by Nygaard et al [7]. The model is validated against a farm SCADA data for two directions, 80 • and 110 • .…”

“…Segalini [6] creates a new model that includes the blockage effect, which is based on the linearized RANS equation. Nygaard et al [7] present two new models for wind turbine interaction effects and a recipe for combining them: the Optimized Park model and a full engineering wind farm blockage model. The authors signaled the idea that a better model could not be created without an understanding of the relevant physics behind blockage effects.…”

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

“…Despite these differences and sensitivities, the literature shows that current mesoscale models offer capabilities to capture atmospheric processes at the relevant scales for the prediction of farm-to-farm wake losses, which are not captured by the simple extrapolation of current farm-level engineering-wake-loss models to farm-to-farm scenarios. Whilst it may be possible to retune engineering-wake-loss models to address farm-to-farm scenarios (e.g., Nygaard and Newcombe 2018;Larsén et al 2019;Nygaard et al 2020), they would still not capture the physics of atmospheric effects, temporal behaviours and resource inhomogeneities that are increasingly important at the mesoscale. Especially if not only farm-to-farm interactions in the direct vicinity are considered but also those on the range of offshore wind-farm wakes (> 60 km, Sect.…”

“…On the other hand, CFD and LES models that capture the variability of wakes are computationally expensive and cannot be applied to large areas of more than 100 km 2 . In addition, wind-farm wakes are influenced by synoptic phenomena such as horizontal wind-speed gradients in coastal areas (Platis et al 2018;Nygaard et al 2020) or low-level jets (Miller et al 2015) that often cannot be captured in engineering-type wake models and CFD models. Finally, wind farms influence not only wind speed but also the turbulence kinetic energy (TKE), temperature, humidity, clouds, and other meteorological or atmospheric parameters (Fitch 2015;Siedersleben et al 2018a).…”

Grand Challenges in Wind Energy Research

“…One important reason is that upwind turbines reduce the kinetic energy available to the downwind ones and thereby lower their power production while increasing the fluctuating loads and reducing their fatigue life. This interference can extend from one wind farm to another many kilometres away, especially for offshore installations (Nygaard et al, 2020). One of the biggest uses of computer resources in wind energy research is the simulation of interference and associated loads and the development of improved control strategies to mitigate them.…”