pywasp.estimate_sensitivity_factor#

pywasp.estimate_sensitivity_factor(pwc, wtg, wind_perturbation_factor=0.05)[source]#

Calculate the sensitivity factor that multiplies wind uncertainty terms.

Parameters:

  • pwc (xarray.Dataset) – The Weibull Wind Climate dataset containing the predicted wind climate at the different turbine locations in a wind farm.

  • wtg (xarray.Dataset) – The wind turbine generator dataset. Single wind turbine, with 2 dimensions: (mode, wind_speed) and 11 variables.

  • wind_perturbation_factor (float, optional) – The factor by which the wind speed is perturbed. Default is 0.05.

Returns:

float – The sensitivity factor value (between 0 and 1).

Notes

The sensitivity factor is calculated as the ratio of the change in AEP to the change in mean wind speed:

[(AEP_+%_wind - AEP_-%_wind) / AEP_gross] / [((U+U') - (U-U')) / U]

AEP = f(x1, x2, x3, …, xn) where xi are all the uncertain variables that affect the AEP. Some variables have a linear effect on AEP (Energy kind), while others have a non-linear effect (Wind kind). Since wind turbine power output grows with the cube of wind speed, wind uncertainty terms must be multiplied by a sensitivity factor.

Examples

>>> pwc = pw.wasp.downscale(
...     gwc, topo_map, output_locs, conf, interp_method="nearest"
... )
>>> wtg = wk.read_wtg("./data/Bonus_1_MW.wtg")
>>> sf = estimate_sensitivity_factor(pwc, wtg, wind_perturbation_factor)