pywasp.estimate_sensitivity_factor

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

Calculate the sensitivity factor that multiplies the 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) – The factor by which the wind speed is perturbed. Default is 0.05.

Returns:

sensitivity_factor (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 = fn(x1,x2,x3…xn) where xi are all the uncertain variables that affect the AEP.

    Some of these variables are have a linear effect on the AEP (those we refer to as Energy kind), while others have a non-linear effect on the AEP, these are Wind kind variables. As it is well known that the power output of a wind turbine grows to the cubic power of the wind-speed. That is why 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 = get_sensitivity_factor(pwc, wtg, wind_perturbation_factor)