Review of Mesoscale Wind-Farm Parametrizations and Their Applications

Fischereit, Jana; Brown, R.A.; Larsén, Xiaoli Guo; Badger, Jake; Hawkes, Graham

doi:10.1007/s10546-021-00652-y

Cited by 46 publications

(41 citation statements)

References 118 publications

(334 reference statements)

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“…Second, only FIT considers wind farms as explicit source of Turbulent Kinetic Energy (TKE), while EWP assumes that TKE develops solely through shear production in the wind farm wake. More details on their comparison can be found in (Fischereit et al, 2021a). FIT is applied here including the bug fix provided by Archer et al (2020) with the recommended TKE coefficient of 0.25.…”

Section: Mesoscale Wake Modelling Using Wrfmentioning

confidence: 99%

Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

Fischereit¹,

Hansen²,

Larsén³

et al. 2021

Preprint

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Abstract. Numerical wind resource modelling across scales from mesoscale to turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore, wind farm wakes can last several tens kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed and it remains unclear, how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that average intra-farm variability can be captured reasonable well with WRF+FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF+EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared to PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

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Section: Mesoscale Wake Modelling Using Wrfmentioning

confidence: 99%

Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

Fischereit¹,

Hansen²,

Larsén³

et al. 2021

Preprint

Self Cite

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“…While wake model sensitivity studies have not examined the impact of PBL schemes on mesoscale wakes, they have shown that NWP-modeled wakes can be sensitive to a number of other inputs (see Sec 1.2 for a more detailed discussion). Turbines are modeled in NWP simulations with wind farm parameterizations (WFPs, for a review see Fischereit et al, 2021), such as the Fitch WFP (Fitch et al, 2012), the Explicit Wake Parametrisation (EWP, Volker et al, 2015), and the Abkar WFP (Abkar and Porté-Agel, 2015). WFPs modify mesoscale flow in two manners.…”

Section: Motivation and Research Questionmentioning

confidence: 99%

“…While external wakes are more nebulous to characterize, they also show sensitivity to atmospheric stability. There is no singular standard approach that is used to characterize external wakes (Fischereit et al, 2021), so we adopt two approaches: by While extensive observations and other simulations suggest that wakes erode faster in convective conditions, we note that the wake erosion is caused by ambient TKE. The behavior here occurs both because hub-height unstable NWF MYNN winds are approximately 1.5 m s −1 stronger than they are under neutral conditions, and the unstable simulations actually have only half as much hub-height TKE than the neutral simulations (Fig.…”

Section: Hub-height Wind Speed Deficitsmentioning

confidence: 99%

“…Due to this sensitivity, we recommend that future wind energy planning studies that examine mesoscale model sensitivity consider varying the PBL scheme, along with other model inputs that have been established in literature, such as grid resolution, magnitude of explicit TKE addition, and the choice of wind farm parameterization (Fischereit et al, 2021). While it is difficult to judge the sensitivity of PBL scheme choice relative to most of the other inputs, we note that changing the PBL scheme had a larger impact than turning explicit TKE generation on or off in the idealized simulations.…”

Section: Impact On Power Productionmentioning

confidence: 99%

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The Sensitivity of the Fitch Wind Farm Parameterization to a Three-Dimensional Planetary Boundary Layer Scheme

Rybchuk

Juliano

Lundquist

et al. 2021

Preprint

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Abstract. Wind plant wake impacts can be estimated with a number of simulation methodologies, each with its own fidelity and sensitivity to model inputs. In turbine-free mesoscale simulations, hub-height wind speeds can significantly vary with the choice of a planetary boundary layer (PBL) scheme. However, the sensitivity of wind plant wakes to a PBL scheme has not been explored because, until now, wake parameterizations were only compatible with one PBL scheme. We couple the Fitch wind farm parameterization with the new NCAR 3DPBL scheme and compare the resulting wakes to those simulated with a widely used PBL scheme. First, we simulate a wind plant in a pseudo-steady state under idealized stable, neutral, and unstable conditions with two PBL schemes: MYNN and the NCAR 3DPBL. For these idealized scenarios, MYNN consistently predicts internal wakes that are 0.25–1.5 m s−1 stronger than internal 3DPBL wakes. However, because MYNN predicts stronger inflow winds than the 3DPBL, MYNN predicts average capacity factors that are as large as 13 percentage points higher than with the 3DPBL, depending on the stability. To extend this sensitivity study, we conduct a month-long case study with both PBL schemes centered on the Vineyard Wind 1 lease area in the mid-Atlantic United States. Under stable and unstable conditions averaged across the month, MYNN again predicts stronger internal waking—by about 0.25 m s−1. However, again due to stronger plant inflow wind speeds in MYNN, the 3DPBL generates 4.7 %–7.8 % less power than MYNN in August 2020, depending on the turbine build-out scenario. Differences between PBL schemes can be even larger for individual instances in time. These simulations suggest that PBL schemes represent a meaningful source of modeled wind resource uncertainty; therefore, we recommend incorporating PBL variability into future wind plant planning sensitivity studies as well as wind forecasting studies.

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“…Instead mesoscale models equipped with a wind farm parameterization (WFP) can be applied to capture these mesoscale variabilites. However, with a typical resolution of about 1-2 km used in wind energy applications according to Fischereit et al (2021a), mesoscale models cannot resolve individual turbines within a farm.…”

Review of Mesoscale Wind-Farm Parametrizations and Their Applications

Cited by 46 publications

References 118 publications

Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

The Sensitivity of the Fitch Wind Farm Parameterization to a Three-Dimensional Planetary Boundary Layer Scheme

Comment on wes-2021-106

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