Verification of induction zone models for wind farm annual energy production estimation

Abstract

Fast engineering wake models are the backbone of wind farm annual energy production (AEP) estimators, whereas the addition of induction zone models in existing tools is a more recent response to rising concerns over wind farm blockage associated losses. Here, “blockage” describes the combined induction fields of all wind turbines inside a farm. Unlike the term might suggest, blockage not only reduces flow speeds, but also increases them; for instance along the outer edges of wind farms. Evaluating the overall impact on AEP of these gains and losses necessitates accurate wind farm induction models. Whilst engineering wake models are tuned to predict wind farm performance, existing induction zone models are all derived for accurately predicting the near field induction not the far field value—important for wind farm simulations. This paper presents the induction models implemented in the wind farm simulation tool PyWake, as well as results from novel analytical models and compares their far field predictions to RANS-AD simulations of different turbines. We demonstrate that when including blockage in AEP simulations, the downstream speed-ups need to be included to avoid an unrealistic bias toward AEP loss and that wake expansion significantly impacts induction at rated thrust levels.

Farm blockage model validation using pre and post construction LiDAR measurements

“…There is no consensus today on how to model those effects, although, several approaches were developed with that objective [10]- [14]. Different modelling directly impacts the estimate for induction (and speed-up), as detailed in [15], [16], and the resulting magnitude blockage losses can differ by a factor ten depending on the selected approach [17]. Validation material remains limited, on the other hand.…”

Blockage effects in a single row of wind turbines

“…Although this study is limited to RANS simulations, the results also have implications for engineering models designed to predict blockage effects. These models are often potential flow-type models [11], which do not account for shear or atmospheric stability effects. As demonstrated in the Results section, the neglect of these effects, particularly atmospheric stability, can lead to material errors and likely bias in turbine interaction predictions.…”

“…Most engineering tools used to predict blockage (see [11] and references within) neglect the potential impact of shear on the results. To assess the effect of this simplification, Case 4 was repeated with a slip wall replacing the no-slip ground and uniform inflow replacing the Richards-Hoxey profiles.…”

“…However, the strength of any conclusions on this point is limited by the lack of directly relevant field observations, as well as the difficulty in generalizing from a relatively small number of simulated conditions. So this research also explores the degree to which commonly used modelling simplifications impact the calculation of blockage effects-these simplifications include neutral stability and the neglect of effects related to shear [11].…”

Gradient-based Wind Farm Layout Optimization With Inclusion And Exclusion Zones

“…For the AEP calculation and the wake modeling involved in the simulations, TOPFARM relies in PyWake (Pedersen et al, 2019), another DTU open source Python library that offers fast AEP evaluation from a range of engineering wake models. Recent works using TOPFARM (Ciavarra et al, 2022;Riva et al, 2020), and PyWake (Rodrigues et al, 2022;Fischereit et al, 2021;Forsting et al, 2021;Pedersen et al, 2021;Quick et al, 2022) can be found in the literature.…”