OWA Wake Modelling Challenge Extended to 5 Offshore Wind Farms
The Anholt benchmark set-up is replicated with 4 additional wind farms for a multi-site assessment of array efficiency prediction by “meso-to-wake” methodologies
Status
The OWA Wake Modelling Challenge has completed the blind test. The results will be published at the WindEurope Offshore conference in Copenhaguen on the 28th of November 2019.
This initial Anholt benchmark resulted in the development of an open-source model evaluation methodology for array efficiency prediction that has been published at the WindEurope Resource Assessment Workshop (Sanz Rodrigo et al, 2019).
Figure 1 shows a diagram of the three phases that compose the Challenge. We are currently in the blind testing phase where wake modelers are provided with mesoscale input data to perform simulations that allow a multi-site assessment on the influence of layout and wind climate conditions on array efficiency prediction.
By the end of October the blind test phase will finish and the winner of the challenge will continue into the calibration phase. Here, validation data from a few wind farms (to be decided) will be shared so that models can be trained to demonstrate improvement with respect to the blind test results.
The Rødsand 2 wind farm will be subject to a more detailed analysis based on the OWA scanning lidar experiment. The objective here is to focus the validation on specific phenomena like blockage, deep-array and farm-farm effects.
Participation
Registration in the OWA challenge is closed.
Background
The OWA Wake Modeling Challenge is an Offshore Wind Accelerator (OWA) project that aims to improve confidence in wake models in the prediction of array efficiency. You can read more about the scope of this project and the benchmarking process in this document and FAQ.
The Anholt benchmark set-up holds for the other wind farms. Hence, please refer to the following guide for benchmark participants.
Scope and Objectives
A meso-to-wake approach is proposed whereby mesoscale simulation data is available as input data for the modeler to interpret in connection to the wake model under consideration. This allows a consistent framework for a multi-site assessment where we can relate array efficiency prediction errors with layout and wind climate characteristics.
Then, the objective during this phase is to gather simulation data for a wide range of models to map their performance against traditional array efficiency predictors such as atmospheric stability or turbulent kinetic energy and explore other mesoscale-derived quantities that can help understanding where the limitations of the models are coming from.
The results of the blind test will be presented at the WindEurope Offshore 2019 conference (under review).
The Sites
Table 1 presents a summary of the wind farms participating in the benchmarking study. Their situation is displayed in Figure 1. With prevailing winds from the W-SW sectors it becomes apparent that all the sites are subject to heterogeneous inflow conditions due to the presence of the coast or due to upstream wind farms.
Input Data
Mesoscale simulations using the NEWA WRF set-up are produced to generate background wind conditions for wake models that are free of (microscale) site effects. Neighbouring wind farms have also been simulated using the Fitch wind farm parameterization available in WRF. The innermost nest in the simulations has a horizontal resolution of 3 km. Following the same approach that was adopted in the EERA-DTOC project (Schepers et al, 2015), two sets of input data are generated:
- Control: Free of wake effects, to characterize the background wind resource.
- Wakes: Includes neighbor wind farms but not the target wind farm, to characterize inflow conditions for microscale wake modeling.
Generic power and thrust curves have been kindly provided by EMD.
Simulation Strategy
A fundamental question that the modeler has is how large does the microscale domain need to be. In a single wind farm approach we shall simulate the target wind farm at microscale with no explicit modeling of the shore or neighbor wind farms, since we trust that mesoscale simulations already include these effects. Then, you can assume horizontal homogeneity in the inflow conditions by using the reference virtual mast to define the inflow conditions for all the turbines (WindFarm_Wakes_Lav30km_ref.nc). Alternatively, you can use heterogeneous wind conditions at each wind turbine (WindFarm_Wakes_WindTurbines.nc, i.e. hub-height interpolated data).
The alternative, the cluster approach, will simulate the shore and wind farms situated close enough in the same microscale simulation.
We suggest that all participants submit results following the single wind farm approach to come up with a consistent database of results based on the most simple and efficient approach. In the case of Rødsand 2 we will use both methods to understand the impact when the separation between wind farms is small.
Output Data
Participants can submit their results as ensemble-averaged quantities or as time-series. Only formatted data following the benchmark guide should be submitted. You are encouraged to use the model evaluation script to test compatibility and self-assess your results by comparing with other model results that may be available. Please attach documentation that will help interpret your simulations and your self-assessment.
Input and output data is facilitated through shared folders in b2drop.
Validation
As baseline, validation results are based on bin-averaged array efficiency for 30º wind direction sectors, 9±1 m/s and three stability classes:
- Unstable (u): -0.2 < z/L < -0.02
- Neutral (n): -0.02 < z/L < 0.02
- Stable (s): 0.02 < z/L <0.2
Mesoscale and SCADA hourly data is synchronized and flagged to filter out registers in non-nominal conditions that will not participate in the validation.
Remarks
CFD modelers may consider submitting results for one wind direction sector to limit the computational effort.
- Anholt: WSW, with coastal effects.
- Dudgeon: WSW, with coastal and farm-farm effects.
- Rødsand 2: W, relatively homogeneous to characterize blockage (detailed analysis).
- Westermost Rough: WSW, with coastal effects.
- Ormonde: SW, behind a large cluster of wind farms.
Schedule
- 31 July 2019: last chance to confirm your participation.
- 30 October 2019: End of blind test.
- 28 November 2019: Presentation at WindEurope Offshore.
- December 2019: End of the OWA project.
Acknowledgements
This benchmark is organized and funded by the Offshore Wind Accelerator (OWA) Partners with support from the IEA Task 31 “Wakebench” Phase 3. The observational data is kindly provided by OWA participants as per Table 1.
References
Sanz Rodrigo J., Borbón Guillén F., Fernandes Correia P.M., Gancarski P. (2019) The “OWA Wake Modeling Challenge”: towards an open-access
model evaluation methodology for array efficiency prediction. WindEurope Resource Assessment Workshop, Brussels, 27–28 June 2019
Sanz Rodrigo J., Gancarski P. (2019) OWA-Anholt Array Efficiency Benchmark. https://github.com/CENER-EPR/OWAbench
Schepers G., et al. (2015) EERA DTOC calculation of scenarios. Deliverable D5.12 of the EERA-DTOC project, June 2015
