ETHOS.RESKit.Wind Workflow#
This example demonstrates the ETHOS.RESKit.Wind workflow.
Preparations#
In ETHOS.RESKit.Wind one can simulate each wind turbine individually with its own characteristics. If you know the turbine model and it is included in RESKit, you can use the actual power curve of the turbines. Otherwise RESKit will create a synthetic power curve for you. The following specifications need to be given by the user:
latitude
longitude
hub height [m]
capacity [kW]
rotor diameter [m] (or power curve)
Workflow:
Import required packages
Create turbine placements DataFrame with locations and specifications
Run the simulation workflow
Generate plot
import reskit as rk
import pandas as pd
import numpy as np
# Create Placements DataFrame with turbine locations and specifications
placements = pd.DataFrame(
{
"lon": [5.985195, 5.994685, 6.004750],
"lat": [50.797254, 50.794208, 50.784432],
"hub_height": [120, 120, 82],
"capacity": [4000, 4000, 4000],
"rotor_diam": [130, 150, 136],
}
)
placements
| lon | lat | hub_height | capacity | rotor_diam | |
|---|---|---|---|---|---|
| 0 | 5.985195 | 50.797254 | 120 | 4000 | 130 |
| 1 | 5.994685 | 50.794208 | 120 | 4000 | 150 |
| 2 | 6.004750 | 50.784432 | 82 | 4000 | 136 |
Run the simulation workflow#
RESKit will run the simulation and create an xarray Dataset with the simulation results for you. Apart from the capacity_factor, RESKit also includes the turbine specifications and intermediate data used to determine the capacity factor (such as wind speeds, surface pressure etc.)
reskit_xr = rk.wind.wind_era5_PenaSanchezDunkelWinklerEtAl2025(
placements=placements,
era5_path=rk.TEST_DATA["era5-like"],
gwa_100m_path=rk.TEST_DATA["gwa100-like.tif"],
height_scaling_data={
50: rk.TEST_DATA["gwa50-like.tif"],
200: rk.TEST_DATA["gwa200-like.tif"],
},
)
reskit_xr
2026-05-06 09:39:19.474409 Now extracting correction factors for a total of 3 placements from /home/docs/checkouts/readthedocs.org/user_builds/ethos-reskit/checkouts/latest/reskit/wind/core/data/cf_correction_factors_PSDW2025.tif:
2026-05-06 09:39:19.475275 Based on max_batch_size=3, the total of 3 placements were split into 1 sub batches. Proceeding with batch 1/1 (id=0) with 3 placements.
2026-05-06 09:39:19.476715 Maximum rel. deviation after initial simulation is 0.2742, Number/share of placements with deviation > tolerance (0.01): 3/3. More iterations required.
2026-05-06 09:39:19.477907 Maximum rel. deviation after 1 additional iteration(s) is 0.0771, Number/share of placements with deviation > tolerance (0.01): 3/3. More iterations required.
2026-05-06 09:39:19.479035 Maximum rel. deviation after 2 additional iteration(s) is 0.0229, Number/share of placements with deviation > tolerance (0.01): 2/3. More iterations required.
2026-05-06 09:39:19.480234 Required tolerance of 0.01 reached after 3 additional iteration(s). Maximum remaining rel. deviation: 0.0091.
<xarray.Dataset> Size: 18kB
Dimensions: (location: 3, time: 140)
Coordinates:
* location (location) int64 24B 0 1 2
* time (time) datetime64[ns] 1kB 2014-12-31T23:3...
Data variables:
lon (location) float64 24B 5.985 5.995 6.005
lat (location) float64 24B 50.8 50.79 50.78
hub_height (location) int64 24B 120 120 82
capacity (location) int64 24B 4000 4000 4000
rotor_diam (location) int64 24B 130 150 136
powerCurve (location) object 24B 'SPC:301,25' ... 'S...
LRA_factor_elevated_wind_speed (location) float64 24B 0.9531 0.9503 0.947
elevated_wind_speed (time, location) float64 3kB 4.695 ... 7.018
surface_pressure (time, location) float64 3kB 1.013e+05 .....
surface_air_temperature (time, location) float64 3kB 0.01979 ... ...
boundary_layer_height (time, location) float64 3kB 120.4 ... 346.5
capacity_factor (time, location) float64 3kB 0.0539 ... 0...RESKit will output the capacity factor of each location for every hour of the simulated year.
reskit_xr["capacity_factor"].isel(time=slice(0, 400)).plot.line(x="time")
[<matplotlib.lines.Line2D at 0x767acc9718e0>,
<matplotlib.lines.Line2D at 0x767acc971dc0>,
<matplotlib.lines.Line2D at 0x767acc9ad8e0>]
