reskit.wind

reskit.wind#

Submodules#

Attributes#

DATAFOLDER

Classes#

`PowerCurve`	Creates a wind turbine's power curve represented by a set of (wind-speed,capacity-factor) pairs.
`WindWorkflowManager`	Helps managing the logical workflow for simulations relating to wind turbines.
`WindWorkflowManager`	Helps managing the logical workflow for simulations relating to wind turbines.

Functions#

`apply_air_density_adjustment`(wind_speed, pressure, ...)	Applies air density corrections to the wind speed values following suggestions by the IEC 61400-12-1:2017 [1].
`onshore_turbine_from_avg_wind_speed`(wind_speed[, ...])	Suggest onshore turbine design characteristics (capacity, hub height, rotor diameter, specific power) for a 2050 European context based on an average wind speed value.
`apply_logarithmic_profile_projection`(...[, ...])	Estimates wind speeds at a target height based on measured wind speeds values at a known measurement height.
`roughness_from_clc`(clc_path, loc[, window_range])	Estimates a roughness factor according to suggestions by Silva et al. [1] by the prominent land cover at given locations according to the Corine Land Cover dataset [2].
`roughness_from_land_cover_classification`(classification)	Estimates roughness value from land cover classifications
`roughness_from_land_cover_source`(source, loc[, ...])	Estimate roughness value from a given land cover raster source
`roughness_from_levels`(low_wind_speed, low_height, ...)	Computes a roughness factor from two windspeed values at two distinct heights.
`compute_specific_power`(capacity, rotor_diam, **k)	Calculates the corresponding specific power for a wind turbine in W/m2 from values of capacity in kW and rotor diameter in m.
`alpha_from_levels`(low_wind_speed, low_height, ...)	Obtains the scaling factor given two wind speeds measured at two different known heights.
`apply_power_profile_projection`(measured_wind_speed, ...)	Estimates wind speed values at target height based on another measured wind speed at a known height subject to a power-law scaling factor.
`TurbineLibrary`()	A dataframe of internally configured wind turbines accessible to later simulations
`offshore_turbine_capex`(capacity, hub_height, ...[, ...])	A cost and scaling model (CSM) to calculate the total cost of a 3-bladed, direct drive offshore wind turbine according to the cost model proposed by Fingersh et al. [1] and Maples et al. [2].
`onshore_turbine_capex`(capacity, hub_height, rotor_diam)	A cost and scaling model (CSM) to calculate the total cost of a 3-bladed, direct drive onshore wind turbine according to Fingersh et al. [1] and Maples et al. [2].
`build_ws_correction_function`(type, data_dict)	type: str
`wind_era5_PenaSanchezDunkelWinklerEtAl2025`(placements, ...)	Simulates wind turbine locations onshore and offshore using ECMWF's
`onshore_wind_merra_ryberg2019_europe`(placements, ...)	Simulates onshore wind generation in Europe using NASA's MERRA2 database [1].
`offshore_wind_merra_caglayan2019`(placements, merra_path)	Simulates offshore wind generation using NASA's MERRA2 database [1].
`onshore_wind_iconlam_2023`(placements, icon_lam_path, ...)	Simulates onshore wind generation using high-resolution dynamically downscaled dataset ICON-LAM over southern Africa.
`wind_config`(placements, weather_path, ...[, ...])	A generic configuration workflow for wind simulations that allows
`wind_era5_2023`(**kwargs)	Simulates onshore and offshore (200km from shoreline) wind generation using ECMWF's ERA5 database [1].
`onshore_wind_era5`(**kwargs)	Simulates onshore wind generation using ECMWF's ERA5 database [1].
`offshore_wind_era5`(**kwargs)	Simulates offshore wind generation using NASA's ERA5 database [1].
`onshore_wind_era5_pure_2023`(**kwargs)	Simulates onshore wind generation using pure ECMWF's ERA5 database [1]

Package Contents#

reskit.wind.apply_air_density_adjustment(wind_speed, pressure, temperature, height=0)#

Applies air density corrections to the wind speed values following suggestions by the IEC 61400-12-1:2017 [1].

Parameters:

wind_speed (float or array-like) – The wind speed values in m/s.
pressure (float or array-like) – Air preassure in Pa.
temperature (float or array-like) – Air temperature in degree Celsius.
height (float or array-like, optional) – The (hub) height of that the air density is going to be adjusted to in m, by default 0

Returns:

The air density corrected wind speed in m/s at the given height.

Return type:

numeric or array-like

Notes

Ideal gas equation applied since it is nearly equivalent to a real-gas equation at humidity = 0, and humidity does not have a significant impact until high temperatures are considered. Pressure projection using barometric equation and density correction from assuming equal energy in the wind.

References

[1] International Electrotechnical Commission (ICE). (2017). IEC 61400-12-1:2017 (p. 558). https://webstore.iec.ch/publication/26603

reskit.wind.onshore_turbine_from_avg_wind_speed(wind_speed, constant_rotor_diam=None, base_capacity=None, base_hub_height=None, base_rotor_diam=None, reference_wind_speed=None, min_tip_height=None, min_specific_power=None, max_hub_height=None, tech_year=2050, baseline_turbine_fp=None)#

Suggest onshore turbine design characteristics (capacity, hub height, rotor diameter, specific power) for a 2050 European context based on an average wind speed value. The default values and the function’s normalization correspond to the baseline turbine design considered by Ryberg et al. [1] for a wind speed equal to 6.7 m/s. See notes.

Parameters:

wind_speed (numeric or array_like) – Local average wind speed close to or at the hub height.
constant_rotor_diam (bool, optional) – Whether the rotor diameter is maintained constant or not, by default True
base_capacity (numeric or array_like, optional) – Baseline turbine capacity in kW, by default 4200.
base_hub_height (numeric or array_like, optional) – Baseline turbine hub height in m, by default 120.
base_rotor_diam (numeric or array_like, optional) – Baseline turbine rotor diameter in m, by default 136.
reference_wind_speed (numeric, optional) – Average wind speed corresponding to the baseline turbine design, by default 6.7.
min_tip_height (numeric, optional.) – Minimum distance in m between the lower tip of the blades and the ground, by default 20.
min_specific_power (numeric, optional) – Minimum specific power allowed in kw/m2, by default 180.
max_hub_height (numeric, optional) – Maximum allowed hub height, any higher optimal hub height will be reduced to this value, by default 200.
tech_year (int, optional) – The year defining the baseline turbine design that shall be used.
baseline_turbine_fp (str, optional) – A json or csv file that contains baseline turbine parameters. Will replace the default data.

Returns:

Returns a the suggested values of hub height in m, specific power in W/m2, and capacity in kW as dictionary when numeric values are input or as a pandas DataFrame when array-like objects are input.

Return type:

dict or pandas DataFrame

Notes

The default baseline onshore turbine has 4200 kW capacity, 120m hub height, and 136m rotor diameter [1]

References

[1] David S. Ryberg, Dilara C. Caglayan, Sabrina Schmitt, Jochen Linssen, Detlef Stolten, Martin Robinius - The Future of European Onshore Wind Energy Potential: Detailed Distributionand Simulation of Advanced Turbine Designs, Energy, 2019, available at https://www.sciencedirect.com/science/article/abs/pii/S0360544219311818

reskit.wind.apply_logarithmic_profile_projection(measured_wind_speed, measured_height, target_height, roughness, displacement=0, stability=0)#

Estimates wind speeds at a target height based on measured wind speeds values at a known measurement height. Estimation is subject to surface roughness, displacement height, and a stability factor.

Parameters:

measured_wind_speed (numeric or array-like) – The input wind speeds in m/s that are going to be adjusted If an array is given with a single dimension, it is assumed to represent timeseries values for a single location If multidimensional array is given, the assumed dimensional context is (time, locations).
measured_height (numeric or array-like) – The measurement height in m of the input wind speeds. Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed
target_height (numeric or array-like) – The height in m to project each wind speed timeseries to. Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed
roughness (numeric or array-like) – The roughness value used to project each wind speed timeseries. Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed
displacement (numeric or array-like, optional) – The displacement value used to project each wind speed timeseries, by default 0. Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed
stability (numeric or array-like, optional) – The stability value used to project each wind speed timeseries, by default 0. Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed

Returns:

Wind speed values in m/s at target height with of the same dimensions as measured_wind_speed.

Return type:

multidimensional array-like

Notes

Roughness values can be generated by the following functions: roughness_from_levels(low_wind_speed, low_height, high_wind_speed, high_height) roughness_from_clc(clc_path, loc, window_range) roughness_from_land_cover_classification(classification, land_cover_type) roughness_from_land_cover_source(source, loc, land_cover_type)

reskit.wind.roughness_from_clc(clc_path, loc, window_range=0)#

Estimates a roughness factor according to suggestions by Silva et al. [1] by the prominent land cover at given locations according to the Corine Land Cover dataset [2].

Parameters:

clc_path (str) – The path to the Corine Land Cover (CLC) raster file on the disk. This function currently only works for CLC versions before 2018.
loc (Anything acceptable to geokit.LocationSet) – The locations for which roughness should be estimated. Arguments accepted include: str, OGR point object (must have an SRS within the object, default = 4326 (for Europe)), lat and lon coordinates tuple (lat,lon). Refers to FZJ-IEK3-VSA/geokit for more information.
window_range (int; optional) – An extra number of pixels to extract around the indicated locations, by default 0. A window_range = 0 means that only the CLC pixel value for each location is returned. A window_range of 1 means an extra pixel is extracted around each location in all directions. Leading to a 3x3 matrix of roughness values Use this if you need to do some operation on the roughnesses found around the indicated location

Returns:

Roughness lengths factors A single float is returned in the event that a single location is specified in locations. A one-dimensional Numpy array is returned in the event that multiple locations are specified in locations.

Return type:

float or numpy.ndarray

Sources#

[1] Silva, J., Ribeiro, C., & Guedes, R. (2007). Roughness length classification of corine land cover classes. European Wind Energy Conference and Exhibition 2007, EWEC 2007. [2] Copernicus (European Union’s Earth Observation Programme). (2018). Corine Land Cover (CLC) 2000, Version 2018. Copernicus. http://land.copernicus.eu/pan-european/corine-land-cover/clc-2000/view

Roughness values [1]#

Continuous urban fabric : 1.2 Broad-leaved forest : 0.75 Coniferous-leaved forest : 0.75 Mixed-leaved forest : 0.75 Green urban areas : 0.6 Transitional woodland/shrub : 0.6 Burnt areas : 0.6 Discontinuous urban fabric : 0.5 Construction sites : 0.5 Industrial or commercial units : 0.5 Sport and leisure facilities : 0.5 Port areas : 0.5 Agro-forestry areas : 0.3 Complex cultivation patterns : 0.3 Land principally occupied by agriculture, with significant areas of natural vegetation : 0.3 Annual crops associated with permanent crops : 0.1 Fruit trees and berry plantations : 0.1 Vineyard : 0.1 Olive groves : 0.1 Road and rail networks and associated land : 0.075 Non-irrigated arable land : 0.05 Permanently irrigated land : 0.05 Rice fields : 0.05 Inland marshes : 0.05 Salt marshes : 0.05 Sclerophylous vegetation : 0.03 Moors and heathland : 0.03 Natural grassland : 0.03 Pastures : 0.03 Dump sites : 0.005 Mineral extraction sites : 0.005 Airports : 0.005 Bare rock : 0.005 Sparsely vegetated areas : 0.005 Glaciers and perpetual snow : 0.001 Peatbogs : 0.0005 Salines : 0.0005 Intertidal flats : 0.0005 Beaches, dunes, and sand plains : 0.0003 Water courses # SUSPICIOUS : 0.001 Water bodies # SUSPISCIOUS : 0.0005 Costal lagoons # SUSPISCIOUS : 0.0005 Estuaries # SUSPISCIOUS : 0.0008 Sea and ocean # SUSPISCIOUS : 0.0002

reskit.wind.roughness_from_land_cover_classification(classification, land_cover_type='clc')#

Estimates roughness value from land cover classifications

Parameters:

classification (int) – land cover classification
land_cover_type (str, optional) – Accepted arguments are ‘clc’, ‘clc-code’, ‘globCover’, ‘modis’, or ‘cci’, by default ‘clc’ ‘clc’: Corine Land Cover (CLC) ‘clc-code’: Corine Land Cover (CLC) codes values ‘globCover’: Global Wind Atlas roughness lengths values ‘modis’: Modis number for “no data” points of Global Wind Atlas (mostly in areas North of 60°) ‘cci’: Climate Change Initiative land cover classification

Returns:

Roughness lengths factors A single int is returned in the event that a single location is specified in locations. A one-dimensional Numpy array is returned in the event that multiple locations are specified in locations.

Return type:

int or numpy.ndarray

reskit.wind.roughness_from_land_cover_source(source, loc, land_cover_type='clc')#

Estimate roughness value from a given land cover raster source

Parameters:

source (str) – The path to the Corine Land Cover raster file on the disk.
loc (Anything acceptable to geokit.LocationSet) – Arguments accepted include: str, OGR point object (must have an SRS within the object, default = 4326 (for Europe)), lat and lon coordinates (tuple (lat,lon)). Refers to FZJ-IEK3-VSA/geokit for more information.
land_cover_type (str, optional) – Accepted arguments are ‘clc’, ‘clc-code’, ‘globCover’, ‘modis’, or ‘cci’, by default ‘clc’ ‘clc’: Corine Land Cover (CLC) ‘clc-code’: Corine Land Cover (CLC) codes ‘globCover’: Global Wind Atlas ‘modis’: Modis number for “no data” points of Global Wind Atlas (mostly in areas North of 60°) ‘cci’: Climate Change Initiative land cover classification

Returns:

Roughness lengths factors A single int is returned in the event that a single location is specified in locations. A one-dimensional Numpy array is returned in the event that multiple locations are specified in locations.

Return type:

int or numpy.ndarray

reskit.wind.roughness_from_levels(low_wind_speed, low_height, high_wind_speed, high_height)#

Computes a roughness factor from two windspeed values at two distinct heights.

Parameters:

low_wind_speed (numeric or numpy.ndarray) – The measured wind speed in m/s at the lower height.
low_height (numeric or numpy.ndarray) – The lower height in m.
high_wind_speed (numeric or numpy.ndarray) – The measured wind speed in m/s at the higher height.
high_height (numeric or numpy.ndarray) – The higher height in m.

Returns:

Roughness factor

Return type:

numeric or arrray-like

class reskit.wind.PowerCurve(wind_speed, capacity_factor)#

Creates a wind turbine’s power curve represented by a set of (wind-speed,capacity-factor) pairs.

Initialization:

Parameters:

wind_speed (array-like) – The wind speeds values
capacity_factor (array-like) – The corresponding capacity factor

Return type:

PowerCurve object

wind_speed#

capacity_factor#

__str__()#

_repr_svg_()#

static from_specific_power(specific_power, cutout=25)#

Creates a synthetic wind turbine power curve based on observed relationships between turbine specific power and known power curves according to Ryberg et al. [1].

Parameters:

specific_power (float) – Turbines’s specific power in m/s
cutout (int, optional) – Cut out wind speed in m/s, by default 25

Returns:

PowerCurve
Sources
——-
[1] Ryberg, D. S., Caglayan, D. G., Schmitt, S., Linßen, J., Stolten, D., & Robinius, M. (2019). The future of European onshore wind energy potential (Detailed distribution and simulation of advanced turbine designs. Energy. https://doi.org/10.1016/j.energy.2019.06.052)

static from_capacity_and_rotor_diam(capacity, rotor_diam, cutout=25)#

Creates a synthetic wind turbine power curve based on observed relationships between turbine’s capacity, rotor diameter and known power curves according to Ryberg et al. [1].

Parameters:

capacity (numeric) – Baseline turbine capacity in kW.
rotor_diam (numeric) – turbine rotor diameter in m
cutout (int, optional) – Cut out wind speed in m/s, by default 25

Returns:

PowerCurve
Sources
——-
[1] Ryberg, D. S., Caglayan, D. G., Schmitt, S., Linßen, J., Stolten, D., & Robinius, M. (2019). The future of European onshore wind energy potential (Detailed distribution and simulation of advanced turbine designs. Energy. https://doi.org/10.1016/j.energy.2019.06.052)

simulate(wind_speed)#

Applies the invoking power curve to the given wind speeds.

Parameters:: wind_speed (array_like) – Local average wind speed close to or at the hub height.
Returns:: CorrespongDing capacity fators for the given wind speeds
Return type:: array_like

expected_capacity_factor_from_weibull(mean_wind_speed=5, weibull_shape=2)#

Computes the expected average capacity factor of a wind turbine based on a Weibull distribution of wind speeds.

Parameters:

mean_wind_speed (int, optional) – mean wind speed at the location in m/s, by default 5
weibull_shape (int, optional) – Weibull shape parameter, by default 2

Returns:

Average capacity factor

Return type:

numeric

expected_capacity_factor_from_distribution(wind_speed_values, wind_speed_counts)#

Computes the expected average capacity factor of a wind turbine based on an explicitly-provided wind speed distribution

Parameters:

wind_speed_values (numeric or array-like) – wind speed values in m/s
wind_speed_counts (numeric or array-like) – corresponding counts (number of occurrence) of the given wind speed values. Counts will be normalized within the function

Example

pc.expected_capacity_factor_from_distribution(: wind_speed_values=[ 1, 2, 3, 4, 5, 6], # Units of m/s wind_speed_counts=[0.1, 0.3, 0.5, 0.3, 0.1, 0.025 ] # Units of “counts” )

Returns:: Average capacity factor
Return type:: numeric

convolute_by_gaussian(scaling=0.06, base=0.1, extend_beyond_cut_out=True, _min_speed=0.01, _max_speed=40, _steps=4000)#

Convolutes a turbine power curve by a normal distribution function with wind-speed-dependent standard deviation.

Parameters:

scaling (float, optional) – scaling factor, by default 0.06
base (float, optional) – base value, by default 0.1
extend_beyond_cut_out (bool, optional) – extend the estimation beyond the turbine’s cut out wind speed, by default True
_min_speed (float, optional) – minimum wind speed value in m/s to be considered, by default 0.01
_max_speed (int, optional) – maximum wind speed value in m/s to be considered, by default 40
_steps (int, optional) – number of steps in between the wind speed range, by default 4000

Returns:

The resulting convoluted power curve

Return type:

PowerCurve

Notes

The wind-speed-dependent standard deviation is computed with: std = wind_speed * scaling + base

apply_loss_factor(loss)#

Applies a loss factor onto the power curve. It can be a single value, or a function which takes a ‘capacity factor’ array as input.

Parameters:

loss (numeric or function) –

If numeric, the value is applied at all capacity factors with:: new_capacity_factors = [1-loss] * previous_capacity_factors
If a function, it must take a numpy array representing capacity factor values as input, resulting in the equation:: new_capacity_factors = [1-loss(previous_capacity_factors)] * previous_capacity_factors

Returns:

The corrected power curve.

Return type:

PowerCurve

reskit.wind.compute_specific_power(capacity, rotor_diam, **k)#

Calculates the corresponding specific power for a wind turbine in W/m2 from values of capacity in kW and rotor diameter in m.

Parameters:

capacity (float or array-like) – Turbine’s nominal capacity in kW.
rotor_diam (float or array-like) – Turbine’s hub height in m.

Returns:

Specific power in W/m2

Return type:

float or array-like

reskit.wind.alpha_from_levels(low_wind_speed, low_height, high_wind_speed, high_height)#

Obtains the scaling factor given two wind speeds measured at two different known heights.

Parameters:

low_wind_speed (numeric or array_like) – The measured windspeed at the ‘lower height’
low_height (numeric or array_like) – The measured height at the ‘lower height’
high_wind_speed (numeric or array_like) – The measured windspeed at the ‘higher height’
high_height (numeric or array_like) – The measured height at the ‘higher height’

Returns:

The corresponding scaling factor The output dimensionality follows the broadcasting rules of Numpy

Return type:

numeric or array-like

Notes

The projection of wind speed values at a given height using the returned scaling factors can be computed using the following function:: apply_power_profile_projection(measured_wind_speed, measured_height, target_height, alpha)

reskit.wind.apply_power_profile_projection(measured_wind_speed, measured_height, target_height, alpha=1 / 7)#

Estimates wind speed values at target height based on another measured wind speed at a known height subject to a power-law scaling factor.

Parameters:

measured_wind_speed (array_like) –

The raw wind speeds to be adjusted. If a single dimension array is given, it is assumed to represent timeseries values for a single location If a multidimensional array is given, the assumed dimensional context is (time, locations), and ‘targetLoc’ must be an iterable with the same length as the ‘locations’ dimension

measured_height : array_like The measurement height of the raw windspeeds. Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed

target_height : numeric or array_like The (hub) height to project each wind speed timeseries to Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed

alpha : numeric or array_like, optional The scaling factor used to project each wind speed timeseries, by default 1/7 Must either be a single value, or an array of values with the same length as the “locations” dimension of measured_wind_speed

Notes

The default scaling factor, alpha, equal to 1/7 corresponds to neutral stability conditions. Alpha values can also be computed using the following function:

alpha_from_levels(low_wind_speed, low_height, high_wind_speed, high_height)

Returns:

array_like
projected wind speeds with the same dimensions as measured_wind_speed

reskit.wind.TurbineLibrary()#: A dataframe of internally configured wind turbines accessible to later simulations

reskit.wind.offshore_turbine_capex(capacity, hub_height, rotor_diam, depth, distance_to_shore, distance_to_bus=None, foundation=None, mooring_count=None, anchor=None, turbine_count=None, turbine_spacing=None, turbine_row_spacing=None)#

A cost and scaling model (CSM) to calculate the total cost of a 3-bladed, direct drive offshore wind turbine according to the cost model proposed by Fingersh et al. [1] and Maples et al. [2]. The CSM distinguishes between seaflor-fixed foundation types; “monopile” and “jacket” and floating foundation types; “semisubmersible” and “spar”. The total turbine cost includes the contributions of the turbine capital cost (TCC), amounting 32.9% for fixed or 23.9% for floating structures, the balance of system costs (BOS) contribution, amounting 46.2% and 60.8% respectively, as well as the finantial costs as the complementary percentage contribution (15.9% and 20.9%) in the same manner [3]. A CSM normalization is done such that a chosen baseline offshore turbine taken by Caglayan et al. [4] (see notes for details) corresponds to an expected specific cost of 2300 €/kW in a 2050 European context as suggested by the 2016 cost of wind energy review by Stehly [3].

Parameters:

capacity (numeric or array-like) – Turbine’s nominal capacity in kW.
hub_height (numeric or array-like) – Turbine’s hub height in m.
rotor_diam (numeric or array-like) – Turbine’s rotor diameter in m.
depth (numeric or array-like) – Water depth in m (absolute value) at the turbine’s location.
distance_to_shore (numeric or array-like) – Distance from the turbine’s location to the nearest shore in km.
distance_to_bus (numeric or array-like, optional) – Distance from the wind farm’s bus in km from the turbine’s location.
foundation (str or array-like of strings, optional) – Turbine’s foundation type. Accepted types are: “monopile”, “jacket”, “semisubmersible” or “spar”, by default “monopile”
mooring_count (numeric, optional) – Refers to the number of mooring lines are there attaching a turbine only applicable for floating foundation types. By default 3 assuming a triangular attachment to the seafloor.
anchor (str, optional) – Turbine’s anchor type only applicable for floating foundation types, by default as recommended by [1]. Arguments accepted are “dea” (drag embedment anchor) or “spa” (suction pile anchor).
turbine_count (numeric, optional) – Number of turbines in the offshore windpark. CSM valid for the range [3-200], by default 80
turbine_spacing (numeric, optional) – Spacing distance in a row of turbines (turbines that share the electrical connection) to the bus. The value must be a multiplier of rotor diameter. CSM valid for the range [4-9], by default 5
turbine_row_spacing (numeric, optional) – Spacing distance between rows of turbines. The value must be a multiplier of rotor diameter. CSM valid for the range [4-10], by default 9

Returns:

Offshore turbine total cost

Return type:

numeric or array-like

Notes

The baseline offshore turbine correspongs to the optimal design for Europe according to Caglayan et al. [4]: capacity = 9400 kW, hub height = 135 m, rotor diameter = 210 m, “monopile” foundation, reference water depth = 40 m, and reference distance to shore = 60 km.

Sources#

[1] Fingersh, L., Hand, M., & Laxson, A. (2006). Wind Turbine Design Cost and Scaling Model. Nrel. https://www.nrel.gov/docs/fy07osti/40566.pdf [2] Maples, B., Hand, M., & Musial, W. (2010). Comparative Assessment of Direct Drive High Temperature Superconducting Generators in Multi-Megawatt Class Wind Turbines. Energy. https://doi.org/10.2172/991560 [3] Stehly, T., Heimiller, D., & Scott, G. (2016). Cost of Wind Energy Review. Technical Report. https://www.nrel.gov/docs/fy18osti/70363.pdf [4] Caglayan, D. G., Ryberg, D. S., Heinrichs, H., Linssen, J., Stolten, D., & Robinius, M. (2019). The techno-economic potential of offshore wind energy with optimized future turbine designs in Europe. Applied Energy. https://doi.org/10.1016/j.apenergy.2019.113794 [5] Maness, M., Maples, B., & Smith, A. (2017). NREL Offshore Balance-of- System Model NREL Offshore Balance-of- System Model. https://www.nrel.gov/docs/fy17osti/66874.pdf [6] Myhr, A., Bjerkseter, C., Ågotnes, A., & Nygaard, T. A. (2014). Levelised cost of energy for offshore floating wind turbines in a life cycle perspective. Renewable Energy, 66, 714–728. https://doi.org/10.1016/j.renene.2014.01.017 [7] Bjerkseter, C., & Ågotnes, A. (2013). Levelised Costs Of Energy For Offshore Floating Wind Turbine Concepts [Norwegian University of Life Sciences]. https://nmbu.brage.unit.no/nmbu-xmlui/bitstream/handle/11250/189073/Bjerkseter%2C C. %26 Ågotnes%2C A. %282013%29 - Levelised Costs of Energy for Offshore Floating Wind Turbine Concepts.pdf?sequence=1&isAllowed=y [8] Smart, G., Smith, A., Warner, E., Sperstad, I. B., Prinsen, B., & Lacal-Arantegui, R. (2016). IEA Wind Task 26: Offshore Wind Farm Baseline Documentation. https://doi.org/10.2172/1259255 [9] RPG CABLES, & KEC International limited. (n.d.). EXTRA HIGH VOLTAGE cables. RPG CABLES. www.rpgcables.com/images/product/EHV-catalogue.pdf

reskit.wind.onshore_turbine_capex(capacity, hub_height, rotor_diam, base_capex=None, base_capacity=None, base_hub_height=None, base_rotor_diam=None, tcc_share=None, bos_share=None)#

A cost and scaling model (CSM) to calculate the total cost of a 3-bladed, direct drive onshore wind turbine according to Fingersh et al. [1] and Maples et al. [2]. A CSM normalization is done such that a chosen baseline turbine, with a capacity of 4200 kW, hub height of 120 m, and rotor diameter of 136 m, corresponds to a expected typical specific cost of 1100 Eur/kW in a 2050 European context according to Ryberg et al. [4] The turbine cost includes the turbine capital cost (TCC) and balance of system costs (BOS), amounting to 67.3% and 22.9% respectively [3], as well as finantial costs equivalent to the the complementary percentage.

Parameters:

capacity (numeric or array-like) – Turbine’s nominal capacity in kW.
hub_height (numeric or array-like) – Turbine’s hub height in m.
rotor_diam (numeric or array-like) – Turbine’s hub height in m.
base_capex (numeric, optional) – The baseline turbine’s capital costs in €, by default 1100*4200 [€/kW * kW]
base_capacity (int, optional) – The baseline turbine’s capacity in kW, by default 4200
base_hub_height (int, optional) – The baseline turbine’s hub height in m, by default 120
base_rotor_diam (int, optional) – The baseline turbine’s rotor diameter in m, by default 136
tcc_share (float, optional) – The baseline turbine’s turbine capital cost (TCC) percentage contribution in the total cost, by default 0.673
bos_share (float, optional) – The baseline turbine’s balance of system costs (BOS) percentage contribution in the total cost, by default 0.229

Returns:

Onshore turbine total cost

Return type:

numeric or array-like

Notes

The expected turbine cost shares by Stehly et al. [3] are claimed to be derived from real cost data and valid until 10 MW capacity.

Sources#

[1] Fingersh, L., Hand, M., & Laxson, A. (2006). Wind Turbine Design Cost and Scaling Model. NREL. https://www.nrel.gov/docs/fy07osti/40566.pdf [2] Maples, B., Hand, M., & Musial, W. (2010). Comparative Assessment of Direct Drive High Temperature Superconducting Generators in Multi-Megawatt Class Wind Turbines. Energy. https://doi.org/10.2172/991560 [3] Stehly, T., Heimiller, D., & Scott, G. (2016). Cost of Wind Energy Review. Technical Report. https://www.nrel.gov/docs/fy18osti/70363.pdf [4] Ryberg, D. S., Caglayan, D. G., Schmitt, S., Linßen, J., Stolten, D., & Robinius, M. (2019). The future of European onshore wind energy potential: Detailed distribution and simulation of advanced turbine designs. Energy. https://doi.org/10.1016/j.energy.2019.06.052

class reskit.wind.WindWorkflowManager(placements, synthetic_power_curve_cut_out=25, synthetic_power_curve_rounding=1)#

Bases: reskit.workflow_manager.WorkflowManager

Helps managing the logical workflow for simulations relating to wind turbines.

Initialization:

Parameters:

placements (pandas.DataFrame) –
A Pandas DataFrame describing the wind turbine placements to be simulated. It must include the following columns:

’geom’ or ‘lat’ and ‘lon’ ‘hub_height’ ‘capacity’ ‘rotor_diam’ or ‘powerCurve’
synthetic_power_curve_cut_out (int, optional) – cut out wind speed, by default 25
synthetic_power_curve_rounding (int, optional) – rounding floor, by default 1

Returns:

A corrected power curve.

Return type:

numpy array

powerCurveLibrary#

project_windspeeds_to_hub_height(height_scaling_method, height_scaling_data, consider_boundary_layer_height=True, allow_extrapolation=True)#

Projects wind speeds to hub heights for a given projection/scaling method.

Parameters:

height_scaling_method (tuple) –
The method to project the windspeeds from the default height (here 100m in ERA-5/GWA3) to hub height (possibly affected by the planetary boundary layer height). First tuple entry (str) describes the general approach (e.g. logarithmic scaling or based on long-run-average windspeeds). No height scaling will be applied when None. Options are: (“lra”, [vertical method]) : Calculation based on the long-run average

wind speeds (e.g. GWA) of the 2 nearest available height levels. [vertical method] (str) describes the form of interpolation, e.g. “linear”.

(“log”, [landcover])Logarithmic height scaling based on surface
roughness defined via a mapping of the land cover category. [landcover] (str) defines the landcover data used for roughness mapping. All landcover types accepted as land_cover_type in logarithmic_profile.roughness_from_land_cover_classification() are allowed, by default “cci” (ESA CCI raster).
height_scaling_data (str, dict) –
The data required for the selected height_scaling_method (see above). The expected data formats are, depending on height_scaling_method: (“log”, [landcover]) : str

Path to the respective “landcover” raster file.

(“lra”, [vertical method]){intstr}
Dict with heights as keys and paths to the LRA-windspeeds at the respective heights as values. Must contain at least one higher and one lower height than the reference height of real_lra_ws_path.
consider_boundary_layer_height (bool, optional) – Corrects the given hub heights for locations by planetary boundary layer (PBL) effects if True, see consider_boundary_height() for details. By default True.
allow_extrapolation (bool, optional) – Takes effect only in interpolating methods, will then allow to extrapolate beyond the min/max height range, by default True.

Return type:

A reference to the invoking WindWorkflowManager

set_roughness(roughness)#

Sets the ‘roughness’ column in the placements DataFrame.

Parameters:: roughness (numeric, iterable) – If a numeric is given, sets the same roughness values to all placements. If an iterable is given, sets the corresponding roughness value in the iterable to the placements. The length of the iterable must match the number of placements
Return type:: A reference to the invoking WindWorkflowManager

estimate_roughness_from_land_cover(path, source_type)#

Estimates the ‘roughness’ value column in the placements DataFrame from a given land cover classification raster file.

Parameters:

path (str) – path to the raster file
source_type (str) – string value to get the corresponding key-value pairs. Accepted types ‘clc’, ‘clc-code’, ‘globCover’, ‘modis’, or ‘cci’, by default ‘clc’

Return type:

A reference to the invoking WindWorkflowManager

consider_boundary_height()#

Corrects the given target heights for locations by planetary boundary layer (PBL) effects, by limiting the target height either to the PBL height when elevated (starting) height < PBL < target height or avoiding scaling altogether when PBL <= elevated (startig) height (by setting target height to elevated starting height).

Returns:: The adapted target heights.
Return type:: numpy array

logarithmic_projection_of_wind_speeds_to_hub_height(consider_boundary_layer_height=False)#

Projects the wind speed values to the hub height.

consider_boundary_layer_heightbool, optional: If True, the wind speed will be scaled only to max. the boundary layer height. By default False.

Return type:: A reference to the invoking WindWorkflowManager

wind_shear_projection_of_wind_speeds_to_hub_height(alternative_wind_speed_rasters, consider_boundary_layer_height=False, allow_extrapolation=True)#

Projects the wind speed values to the hub height.

consider_boundary_layer_heightbool, optional: If True, the wind speed will be scaled only to max. the boundary layer height. By default False.
allow_extrapolation . BOOL; OPTIONAL: If False, target heights must be between minimum and maximum height keys provided in alternative_wind_speed_rasters. By default True.

Return type:: A reference to the invoking WindWorkflowManager

apply_air_density_correction_to_wind_speeds()#

Applies air density corrections to the wind speeds at the hub height.

Return type:: A reference to the invoking WindWorkflowManager

apply_wake_correction_of_wind_speeds(wake_curve='dena_mean')#

Applies a wind-speed dependent reduction factor to the wind speeds at elevated height, based on

Parameters:: wake_curve (str, optional) – string value to describe the wake reduction method. None will cause no reduction. Location-sepcific values can also be given in a ‘wake_curve’ column of the placements dataframe, the latter will be overridden by the ‘wake_curve’ argument. By default “dena_mean”. Choose from (see more information here under wind_efficiency_curve_name[1]): * “dena_mean”, * “knorr_mean”, * “dena_extreme1”, * “dena_extreme2”, * “knorr_extreme1”, * “knorr_extreme2”, * “knorr_extreme3”,
Return type:: A reference to the invoking WindWorkflowManager

convolute_power_curves(scaling, base, **kwargs)#

Convolutes a turbine power curve from a normal distribution function with wind-speed-dependent standard deviation.

Parameters:

scaling (float, optional) – scaling factor, by default 0.06
base (float, optional) – base value, by default 0.1

Return type:

A reference to the invoking WindWorkflowManager

simulate(max_batch_size=None, cf_correction_factor=1.0, tolerance=0.01, max_iterations=10, verbose=True)#

Applies the invoking power curve to the given wind speeds. A max_batch_size can be set, splitting the simulation in batches. If set, cf_correction_factor is applied iteratively to adjust avreage cf output. Capacity factors are calculated in the subfunction _sim(), which is called iteratively.

max_batch_sizeint, optional: The maximum number of locations to be simulated simultaneously. If None, no limits will be applied, by default None.
cf_correction_factorfloat, optional: The average cf output will be adjusted by this ratio via wind speed adaptations (no linear scaling). By default 1.0.
tolerancefloat, optional: The max. deviation of the simulated average cf from the enforced corrected value, by default 0.03, i.e. 3% absolute.
max_iterationsint, optional: The max. No. of simulation iterations allowed for iterative simulation of one batch until the tolerance is met, else a TimeOutError will be raised. By default 10 iterations.
verbosebool, optional: If True, additional status information will be printed, by default True.

Return type:: A reference to the invoking WindWorkflowManager

apply_availability_factor(availability_factor)#

Applies a relative reduction factor to the energy output (capacity factor) time series to statistically account for non-availabilities.

Parameters:: availability_factor (float) – Factor that will be applied to the output time series.
Return type:: A reference to the invoking WindWorkflowManager

interpolate_raster_vals_to_hub_height(name, height_to_raster_dict, **kwargs)#

Given several raster datasets which correspond to a desired value (e.g. average wind speed) at different altitudes, this function will read values for each placement location from each of these datasets, and will then linearly interpolate them to the hub height of each turbine

Parameters:

name (str) – The name of the variable to create (will be placed in the self.placements member)
height_to_raster_dict (dict) – A dictionary which maps altitude values to raster datasets

Return type:

A reference to the invoking WindWorkflowManager

set_correction_factors(correction_factors, verbose=True)#

Gets the correction factors if necessary and sets them as class attribute.

Parameters:: correction_factors (str, float) – correction factor as float or path to the correction factor raster file
Return type:: A reference to the invoking WindWorkflowManager

reskit.wind.DATAFOLDER#

reskit.wind.build_ws_correction_function(type, data_dict)#

type: str: type of correction function
data_dict: dict, str: dictionary or json file containing the data needed to build the correction function

class reskit.wind.WindWorkflowManager(placements, synthetic_power_curve_cut_out=25, synthetic_power_curve_rounding=1)#

Bases: reskit.workflow_manager.WorkflowManager

Helps managing the logical workflow for simulations relating to wind turbines.

Initialization:

Parameters:

placements (pandas.DataFrame) –
A Pandas DataFrame describing the wind turbine placements to be simulated. It must include the following columns:

’geom’ or ‘lat’ and ‘lon’ ‘hub_height’ ‘capacity’ ‘rotor_diam’ or ‘powerCurve’
synthetic_power_curve_cut_out (int, optional) – cut out wind speed, by default 25
synthetic_power_curve_rounding (int, optional) – rounding floor, by default 1

Returns:

A corrected power curve.

Return type:

numpy array

powerCurveLibrary#

project_windspeeds_to_hub_height(height_scaling_method, height_scaling_data, consider_boundary_layer_height=True, allow_extrapolation=True)#

Projects wind speeds to hub heights for a given projection/scaling method.

Parameters:

height_scaling_method (tuple) –
The method to project the windspeeds from the default height (here 100m in ERA-5/GWA3) to hub height (possibly affected by the planetary boundary layer height). First tuple entry (str) describes the general approach (e.g. logarithmic scaling or based on long-run-average windspeeds). No height scaling will be applied when None. Options are: (“lra”, [vertical method]) : Calculation based on the long-run average

wind speeds (e.g. GWA) of the 2 nearest available height levels. [vertical method] (str) describes the form of interpolation, e.g. “linear”.

(“log”, [landcover])Logarithmic height scaling based on surface
roughness defined via a mapping of the land cover category. [landcover] (str) defines the landcover data used for roughness mapping. All landcover types accepted as land_cover_type in logarithmic_profile.roughness_from_land_cover_classification() are allowed, by default “cci” (ESA CCI raster).
height_scaling_data (str, dict) –
The data required for the selected height_scaling_method (see above). The expected data formats are, depending on height_scaling_method: (“log”, [landcover]) : str

Path to the respective “landcover” raster file.

(“lra”, [vertical method]){intstr}
Dict with heights as keys and paths to the LRA-windspeeds at the respective heights as values. Must contain at least one higher and one lower height than the reference height of real_lra_ws_path.
consider_boundary_layer_height (bool, optional) – Corrects the given hub heights for locations by planetary boundary layer (PBL) effects if True, see consider_boundary_height() for details. By default True.
allow_extrapolation (bool, optional) – Takes effect only in interpolating methods, will then allow to extrapolate beyond the min/max height range, by default True.

Return type:

A reference to the invoking WindWorkflowManager

set_roughness(roughness)#

Sets the ‘roughness’ column in the placements DataFrame.

Parameters:: roughness (numeric, iterable) – If a numeric is given, sets the same roughness values to all placements. If an iterable is given, sets the corresponding roughness value in the iterable to the placements. The length of the iterable must match the number of placements
Return type:: A reference to the invoking WindWorkflowManager

estimate_roughness_from_land_cover(path, source_type)#

Estimates the ‘roughness’ value column in the placements DataFrame from a given land cover classification raster file.

Parameters:

path (str) – path to the raster file
source_type (str) – string value to get the corresponding key-value pairs. Accepted types ‘clc’, ‘clc-code’, ‘globCover’, ‘modis’, or ‘cci’, by default ‘clc’

Return type:

A reference to the invoking WindWorkflowManager

consider_boundary_height()#

Corrects the given target heights for locations by planetary boundary layer (PBL) effects, by limiting the target height either to the PBL height when elevated (starting) height < PBL < target height or avoiding scaling altogether when PBL <= elevated (startig) height (by setting target height to elevated starting height).

Returns:: The adapted target heights.
Return type:: numpy array

logarithmic_projection_of_wind_speeds_to_hub_height(consider_boundary_layer_height=False)#

Projects the wind speed values to the hub height.

consider_boundary_layer_heightbool, optional: If True, the wind speed will be scaled only to max. the boundary layer height. By default False.

Return type:: A reference to the invoking WindWorkflowManager

wind_shear_projection_of_wind_speeds_to_hub_height(alternative_wind_speed_rasters, consider_boundary_layer_height=False, allow_extrapolation=True)#

Projects the wind speed values to the hub height.

consider_boundary_layer_heightbool, optional: If True, the wind speed will be scaled only to max. the boundary layer height. By default False.
allow_extrapolation . BOOL; OPTIONAL: If False, target heights must be between minimum and maximum height keys provided in alternative_wind_speed_rasters. By default True.

Return type:: A reference to the invoking WindWorkflowManager

apply_air_density_correction_to_wind_speeds()#

Applies air density corrections to the wind speeds at the hub height.

Return type:: A reference to the invoking WindWorkflowManager

apply_wake_correction_of_wind_speeds(wake_curve='dena_mean')#

Applies a wind-speed dependent reduction factor to the wind speeds at elevated height, based on

Parameters:: wake_curve (str, optional) – string value to describe the wake reduction method. None will cause no reduction. Location-sepcific values can also be given in a ‘wake_curve’ column of the placements dataframe, the latter will be overridden by the ‘wake_curve’ argument. By default “dena_mean”. Choose from (see more information here under wind_efficiency_curve_name[1]): * “dena_mean”, * “knorr_mean”, * “dena_extreme1”, * “dena_extreme2”, * “knorr_extreme1”, * “knorr_extreme2”, * “knorr_extreme3”,
Return type:: A reference to the invoking WindWorkflowManager

convolute_power_curves(scaling, base, **kwargs)#

Convolutes a turbine power curve from a normal distribution function with wind-speed-dependent standard deviation.

Parameters:

scaling (float, optional) – scaling factor, by default 0.06
base (float, optional) – base value, by default 0.1

Return type:

A reference to the invoking WindWorkflowManager

simulate(max_batch_size=None, cf_correction_factor=1.0, tolerance=0.01, max_iterations=10, verbose=True)#

Applies the invoking power curve to the given wind speeds. A max_batch_size can be set, splitting the simulation in batches. If set, cf_correction_factor is applied iteratively to adjust avreage cf output. Capacity factors are calculated in the subfunction _sim(), which is called iteratively.

max_batch_sizeint, optional: The maximum number of locations to be simulated simultaneously. If None, no limits will be applied, by default None.
cf_correction_factorfloat, optional: The average cf output will be adjusted by this ratio via wind speed adaptations (no linear scaling). By default 1.0.
tolerancefloat, optional: The max. deviation of the simulated average cf from the enforced corrected value, by default 0.03, i.e. 3% absolute.
max_iterationsint, optional: The max. No. of simulation iterations allowed for iterative simulation of one batch until the tolerance is met, else a TimeOutError will be raised. By default 10 iterations.
verbosebool, optional: If True, additional status information will be printed, by default True.

Return type:: A reference to the invoking WindWorkflowManager

apply_availability_factor(availability_factor)#

Applies a relative reduction factor to the energy output (capacity factor) time series to statistically account for non-availabilities.

Parameters:: availability_factor (float) – Factor that will be applied to the output time series.
Return type:: A reference to the invoking WindWorkflowManager

interpolate_raster_vals_to_hub_height(name, height_to_raster_dict, **kwargs)#

Given several raster datasets which correspond to a desired value (e.g. average wind speed) at different altitudes, this function will read values for each placement location from each of these datasets, and will then linearly interpolate them to the hub height of each turbine

Parameters:

name (str) – The name of the variable to create (will be placed in the self.placements member)
height_to_raster_dict (dict) – A dictionary which maps altitude values to raster datasets

Return type:

A reference to the invoking WindWorkflowManager

set_correction_factors(correction_factors, verbose=True)#

Gets the correction factors if necessary and sets them as class attribute.

Parameters:: correction_factors (str, float) – correction factor as float or path to the correction factor raster file
Return type:: A reference to the invoking WindWorkflowManager

reskit.wind.wind_era5_PenaSanchezDunkelWinklerEtAl2025(placements, era5_path, gwa_100m_path, height_scaling_data, gwa_nodata_fallback=1.0, output_netcdf_path=None, cf_correction=True, output_variables=None, max_batch_size=15000, **simulate_kwargs)#

Simulates wind turbine locations onshore and offshore using ECMWF’s ERA5 database [1], with an optional correction loop to ensure that generated capacity factors for historic wind fleets meet reported generation/capacity based on Renewables Market Report [2] by the International Energy Agency (IEA).

Please cite the following publication when using the workflow [3]: Peña-Sánchez, Dunkel, Winkler et al. (2025): Towards high resolution, validated and open global wind power assessments. https://doi.org/10.48550/arXiv.2501.07937

Parameters:

placements (pandas Dataframe) – A Dataframe object with the parameters needed by the simulation.
era5_path (str) – Path to the ERA5 data.
gwa_100m_path (str) – Path to the Global Wind Atlas v4 (GWA4) at 100m [4] raster file.
height_scaling_data (dict) – The data required for the height_scaling_method (“lra”, “linear”). Dict with integer heights as keys and str paths to the Global Wind Atlas windspeeds [4] at the respective heights as values. Must contain at least one higher and one lower height than 100 [m].
gwa_nodata_fallback (str, optional) –
If no GWA data is available, use for simulation: (1) float value for a multiple of the respective ERA-5 value

(ERA5 raw), i.e. 1.0 means fall back on unscaled ERA-5 value
1. np.nan for nan output
2. a callable function to be applied to the ERA-5 value in the format: nodata_fallback(locs, ERA5_value)
3. a filepath to a raster file containing the fallback values
output_netcdf_path (str, optional) – Path to a directory to put the output files, by default None
cf_correction (bool, optional) – If False, the capacity factors will be calculated based on a calibrated physical model only, else an additional correction step will be added to ensure that historic capacity factors based on [2] are met if historic wind fleets are simulated. By default True.
output_variables (str, optional) – Restrict the output variables to these variables, by default None
max_batch_size (int) – The maximum number of locations to be simulated simultaneously, else multiple batches will be simulated iteratively. Helps limiting RAM requirements but may affect runtime. Should be adapted to individual computation system (roughly 7GB RAM per 10k locations), by default 25 000.
simulate_kwargs (optional) – Will be passed on to simulate().

Returns:

xarray.Dataset – A xarray dataset including all the output variables you defined as your output variables.
Sources
——-
[1] European Centre for Medium-Range Weather Forecasts. (2019). ERA5 dataset. https (//www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5)
[2] International Energy Agency. (2023). Renewables Market Report. https (//www.iea.org/reports/renewables-2023)
[3] Peña-Sánchez, Dunkel, Winkler et al. (2025) (Towards high resolution, validated and open global wind power assessments. https://doi.org/10.48550/arXiv.2501.07937)
[4] DTU Wind Energy. (2025). Global Wind Atlas v4. https (//globalwindatlas.info/)

reskit.wind.onshore_wind_merra_ryberg2019_europe(placements, merra_path, gwa_50m_path, clc2012_path, output_netcdf_path=None, output_variables=None, max_batch_size=25000)#

Simulates onshore wind generation in Europe using NASA’s MERRA2 database [1].

Parameters:

placements (pandas Dataframe) – A Dataframe object with the parameters needed by the simulation.
merra_path (str) – Path to the MERRA2 data.
gwa_50m_path (str) – Path to the Global Wind Atlas at 50m [2] rater file.
clc2012_path (str) – Path to the CLC 2012 raster file [3].
output_netcdf_path (str, optional) – Path to a directory to put the output files, by default None
output_variables (str, optional) – Restrict the output variables to these variables, by default None
max_batch_size (int) – The maximum number of locations to be simulated simultaneously, else multiple batches will be simulated iteratively. Helps limiting RAM requirements but may affect runtime. By default 25 000. Roughly 7GB RAM per 10k locations.

Returns:

xarray.Dataset – A xarray dataset including all the output variables you defined as your output variables.
Sources
——-
[1] NASA (National Aeronautics and Space Administration). (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). https (//disc.gsfc.nasa.gov/datasets?keywords=%22MERRA-2%22&page=1&source=Models%2FAnalyses MERRA-2)
[2] DTU Wind Energy. (2019). Global Wind Atlas. https (//globalwindatlas.info/)
[3] Copernicus (European Union’s Earth Observation Programme). (2012). Corine Land Cover 2012. Copernicus. https (//land.copernicus.eu/pan-european/corine-land-cover/clc-2012)

reskit.wind.offshore_wind_merra_caglayan2019(placements, merra_path, output_netcdf_path=None, output_variables=None, max_batch_size=25000)#

Simulates offshore wind generation using NASA’s MERRA2 database [1].

Parameters:

placements (pandas Dataframe) – A Dataframe object with the parameters needed by the simulation.
merra_path (str) – Path to the MERRA2 data.
output_netcdf_path (str, optional) – Path to a directory to put the output files, by default None
output_variables (str, optional) – Restrict the output variables to these variables, by default None
max_batch_size (int) – The maximum number of locations to be simulated simultaneously, else multiple batches will be simulated iteratively. Helps limiting RAM requirements but may affect runtime. By default 25 000. Roughly 7GB RAM per 10k locations.

Returns:

xarray.Dataset – A xarray dataset including all the output variables you defined as your output variables.
Sources
——-
[1] National Aeronautics and Space Administration. (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). https (//disc.gsfc.nasa.gov/datasets?keywords=%22MERRA-2%22&page=1&source=Models%2FAnalyses MERRA-2)

reskit.wind.onshore_wind_iconlam_2023(placements, icon_lam_path, esa_cci_path, output_netcdf_path=None, output_variables=None, max_batch_size=25000)#

Simulates onshore wind generation using high-resolution dynamically downscaled dataset ICON-LAM over southern Africa. This workflow was used in the publicaiton [1].

Parameters:

placements (pandas Dataframe) – A Dataframe object with the parameters needed by the simulation.
icon_lam_path (str) – Path to the specified weather data. May contain ‘<X-TILE>’ and <Y-TILE>’ spacers, in that case, a zoom level value is expected and the correct tiles will be assigned for every loation individually.
esa_cci_path (str) – Path to the ESA CCI raster file [2].
output_netcdf_path (str, optional) – Path to a directory to put the output files, by default None
output_variables (str, optional) – Restrict the output variables to these variables, by default None
max_batch_size (int) – The maximum number of locations to be simulated simultaneously, else multiple batches will be simulated iteratively. Helps limiting RAM requirements but may affect runtime. By default 20 000.

Returns:

xarray.Dataset – A xarray dataset including all the output variables.
Sources
——-
[1] Chen, S., Goergen, K., Hendricks Franssen, H. J., Winkler, C., Poll, S., Houssoukri Zounogo Wahabou, Y., … & Heinrichs, H. (2024). Higher onshore wind energy potentials revealed by kilometer‐scale atmospheric modeling. Geophysical Research Letters, 51(19), e2024GL110122. https (//doi.org/10.1029/2024GL110122)
[2] ESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at (maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf)

reskit.wind.wind_config(placements, weather_path, weather_source_type, weather_lra_ws_path, real_lra_ws_path, real_lra_ws_scaling, real_lra_ws_spatial_interpolation, real_lra_ws_nodata_fallback, height_scaling_method, height_scaling_data, ws_correction_func, cf_correction_factor, wake_curve, availability_factor, consider_boundary_layer_height, allow_height_extrapolation, power_curve_scaling, power_curve_base, convolute_power_curves_args={}, loss_factor_args={}, output_variables=None, max_batch_size=25000, output_netcdf_path=None, elevated_wind_speed=None)#

A generic configuration workflow for wind simulations that allows flexible calibration of all arguments used in the workflow. NOTE: Only for calibration/validation purposes!

Parameters:

placements (pandas Dataframe) – A Dataframe object with the parameters needed by the simulation.
weather_path (str) – Path to the temporally resolved weather data, e.g. ERA-5 or MERRA-2 etc.
weather_lra_ws_path (str) – The path to a raster with the corresponding long-run-average windspeeds of the actual weather data (will be corrected to the real lra if given, else weather_lra_path has no effect)
real_lra_ws_path (str, float) – Either a float/int (1.0 means no scaling) or a path to a raster with real long-run-average wind speeds, e.g. the Global Wind Atlas at the same height as the weather data.
real_lra_ws_scaling (float) – Accounts for unit differences, set to 1.0 if both weather data and real_lra_ws are in the same unit.
real_lra_ws_spatial_interpolation (str) – The spatial interpolation how the real lra ws shall be extracted, e.g. ‘near’, ‘average’, ‘linear_spline’, ‘cubic_spline’
real_lra_ws_nodata_fallback (str, optional) –
If no GWA available, use for simulation: (1) float value for a multiple of the ‘weather_lra_ws_path’ value

(ERA5 raw), i.e. 1.0 means weather_lra_ws_path value
1. np.nan for nan output
2. a callable function to be applied to the weather_lra_ws_path (ERA-5) value in the format: nodata_fallback(locs, weather_lra_ws_path_value)
3. a filepath to a raster file containing the fallback values
height_scaling_method (tuple) –
The method to project the windspeeds from the default height (here 100m in ERA-5/GWA3) to hub height (possibly affected by the planetary boundary layer height). First tuple entry (str) describes the general approach (e.g. logarithmic scaling or based on long-run-average windspeeds). No height scaling will be applied when None. Options are: (“lra”, [vertical method]) : Calculation based on the long-run average

wind speeds (e.g. GWA) of the 2 nearest available height levels. [vertical method] (str) describes the form of interpolation. By default “linear”.

(“log”, [landcover])Logarithmic height scaling based on surface
roughness defined via a mapping of the land cover category. [landcover] (str) defines the landcover data used for roughness mapping. All landcover types accepted as land_cover_type in logarithmic_profile.roughness_from_land_cover_classification() are allowed, by default “cci” (ESA CCI raster).
height_scaling_data (str, dict) –
The data required for the selected height_scaling_method (see above). The expected data formats are, depending on height_scaling_method: (“log”, [landcover]) : str

Path to the respective “landcover” raster file.

(“lra”, [vertical method]){intstr}
Dict with heights as keys and paths to the LRA-windspeeds at the respective heights as values. Must contain at least one higher and one lower height than the reference height of real_lra_ws_path.
ws_correction_func (float, callable, tuple, list) – An executable function that takes a numpy array as single input argument and returns an adapted windspeed. If 1.0 is passed, no windspeed correction will be applied. Can also be passed as tuple or list of length 2 with data_type (e.g. ‘linear’ or ‘ws_bins’) and data dict (dict or path to yaml) with parameters.
cf_correction_factor (float, str) – The factor by which the output capacity factors will be corrected indirectly (via corresponding adaptation of the windspeeds). Can be str formatted path to a raster with spatially resolved correction factors, set to 1.0 to not apply any correction.
wake_curve (str, optional) – string value to describe the wake reduction method. None will cause no reduction, by default “dena_mean”. Choose from (see more information here under wind_efficiency_curve_name[1]): “dena_mean”, “knorr_mean”, “dena_extreme1”, “dena_extreme2”, “knorr_extreme1”, “knorr_extreme2”, “knorr_extreme3”. Alternatively, the ‘wake_curve’ str can also be provided per each location in a ‘wake_curve’ column of the placements dataframe, ‘wake_curve’ argument must then be None.
availability_factor (float, optional) – This factor accounts for all downtimes and applies an average reduction to the output curve, assuming a statistical deviation of the downtime occurrences and a large enough turbine fleet. Suggested availability is 0.98 including technical availability of turbine and connector as well as outages for ecological reasons (e.g. bat protection). This does not include wake effects (see above) or curtailment/outage for economical reasons or transmission grid congestion.
consider_boundary_layer_height (bool) – If True, boundary layer height will be considered.
allow_height_extrapolation (bool) – Takes effect only if a height scaling method based on interpolation is chosen, will then allow or forbid extrapolation beyond the min/max provided height range.
power_curve_scaling (float) – The scaling factor to smoothen the power curve, for details see: convolute_power_curves()
power_curve_base (float) – The base factor to smoothen the power curve, for details see: convolute_power_curves()
convolute_power_curves_args (dict, optional) – Further convolute_power_curve() arguments, for details see: convolute_power_curves(). By default {}.
loss_factor_args (dict, optional) – Arguments that are passed to reskit.utils.low_generation_loss() besides the capacity factor. If empty dict ({}), no loss will be applied. For details see: reskit.utils.loss_factors.low_generation_loss() By default {}.
output_variables (str, optional) – Restrict the output variables to these variables, by default None
max_batch_size (int) – The maximum number of locations to be simulated simultaneously, else multiple batches will be simulated iteratively. Helps limiting RAM requirements but may affect runtime. By default 25 000. Roughly 7GB RAM per 10k locations.
output_netcdf_path (str, optional) – Path to a directory to put the output files, by default None

Returns:

A xarray dataset including all the output variables you defined as your output variables.

Return type:

xarray.Dataset

reskit.wind.wind_era5_2023(**kwargs)#: Simulates onshore and offshore (200km from shoreline) wind generation using ECMWF’s ERA5 database [1].

Sources#

[1] European Centre for Medium-Range Weather Forecasts. (2019). ERA5 dataset. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

reskit.wind.onshore_wind_era5(**kwargs)#: Simulates onshore wind generation using ECMWF’s ERA5 database [1].

Sources#

[1] European Centre for Medium-Range Weather Forecasts. (2019). ERA5 dataset. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

reskit.wind.offshore_wind_era5(**kwargs)#: Simulates offshore wind generation using NASA’s ERA5 database [1].

Sources#

[1] European Centre for Medium-Range Weather Forecasts. (2019). ERA5 dataset. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5.

reskit.wind.onshore_wind_era5_pure_2023(**kwargs)#: Simulates onshore wind generation using pure ECMWF’s ERA5 database [1] without further disaggregation or correction besides height projection and power curve convolution.

Sources#

[1] European Centre for Medium-Range Weather Forecasts. (2019). ERA5 dataset. https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5