pywasp.potential_aep#

pywasp.potential_aep(wwc, wtg, /, wind_turbines=None, *, air_density_correction='infer', air_density=None, wind_farm_model='PARK2_onshore', ws_stepsize=1.0, ws_upper_limit=None, ws_lower_limit=0.0, n_subsector=5, site_interp_method='nearest', site_interp_bounds='ignore', turbulence_intensity=None, n_cpu_pywake=1)#

Calculate Potential Annual Energy Production using wind farm effects from PyWake.

Warning

This function is experimental and its signature may change.

Parameters:

  • wwc (Dataset) – PyWAsP Weibull Wind Climate Dataset containing wind climates for all the turbine locations.

  • wtg (Dataset, dict) –

    • If a xr.Dataset is passed it should be a WTG formatted dataset. PyWAsP will assume WWC points match the turbines hub height and that the WTG is the same for all turbines

    • If a dict of wtg_keys and WTG xr.Datasets is passed, a turbines dataset must be passed as well, and the key’s must match between the two.

  • wind_turbines (Dataset, optional) – Wind Turbine locations, hub heights, group_id’s, and wtg_keys. Paired with the wind climate by position: turbine i is given point i’s wind climate and air density, and the results come back in the wind climate’s order. A turbine need not stand exactly at its point – a wind climate measured nearby is fine – but there must be one turbine per point, in the same order. Built from the wind climate’s own locations when not passed.

  • air_density_correction (string, optional) – “infer” –> correct the power curve to the site’s air density. “none” –> use the power curve as it is; the air density is not read.

  • air_density (float, optional) – Air density to correct the power curve to, used only with air_density_correction=”infer”. Overwrites the air density in the WWC, if present; otherwise the WWC’s own is used.

  • wind_farm_model (str, function) – Wind farm model to use for deficit calculations. Can either be a name of a predefined wind farm model: “PARK1”, “PARK2_onshore”, “PARK2_offshore”, or a predefined py_wake wind_farm_model object (wrapped in a ‘functools.partial’ function)

  • ws_stepsize (float, optional) – Wind speed step between the simulated wind speed cases, by default 1.0 m/s. Effective wind speeds are taken as linear between the cases, so a jump in the wake response, where an upstream turbine cuts in or out, is only resolved to this step. That matters most for small deficits, which it can shift by a noticeable fraction of their value. wspd does not depend on it.

  • ws_upper_limit (float, optional) – Upper limit of wind speed range, by default None Which means the upper limit of the wind speed range will be the maximum

  • ws_lower_limit (float, optional) – Deprecated since 2.1.0; will be removed in 3.0. Warns for any value other than 0.0. A float is ignored; the wind speed cases start at 0.0 m/s. None starts them at the cut-in wind speed instead, and has no replacement.

  • n_subsector (int, optional) – Number of equispaced wind directions simulated within each sector, by default 5. Subsectors sample the wake geometry within their sector only: a subsector always uses its parent sector’s wind climate, speed-up and turbulence intensity.

  • site_interp_method (str, optional) – The interpolation method to use in py_wake XRSite, by default “nearest”. Interpolation applies to the spatial axes; a subsector always uses its parent sector’s speed-up and turbulence intensity.

  • site_interp_bounds (str, optional) – How to handle values outside the site boundary, by default “ignore” Which means values outside the site bondary will not be checked for.

  • turbulence_intensity (float, optional) – Turbulence intensity for the calculation; wind climates produced by pywasp do not include it, so it is only available if passed here or added to the WWC as a “turbulence_intensity” variable, which a value passed here overwrites. The built-in wind farm models do not use it – it only feeds the effective turbulence intensity outputs, which are omitted when it is absent – but a custom wind farm model requires it.

  • n_cpu_pywake (int, optional) – Number of CPUs to use for the PyWake calculations, by default 1 If n_cpu_pywake is None, the number of CPUs will be set to the number of CPUs available on the machine.

Returns:

Dataset – Potential Annual Energy Production for each turbine/location. Each variable below has a _sector counterpart holding the same quantity per sector, alongside wdfreq.

  • potential_aep:

    Potential AEP in GWh for each location

  • gross_aep:

    Gross AEP in GWh for each location. Integrated exactly over the Weibull distribution, as gross_aep() does, but only up to the highest wind speed simulated, so a ws_upper_limit stopping short of where the turbine still produces lowers it. Its ratio with potential_aep is therefore a pure wake effect.

  • potential_aep_deficit:

    AEP deficit in units of fraction for each location, 1 - potential_aep / gross_aep

  • wspd:

    Mean wind speed in m/s for each location: the mean of its sector Weibull distributions, weighted by wdfreq

  • wspd_eff:

    Mean effective wind speed in m/s for each location. Above the highest wind speed simulated, the effective wind speed keeps its ratio to the free wind speed there.

  • wspd_deficit:

    Average wind speed deficit in units of fraction for each location, 1 - wspd_eff / wspd

  • turbulence_intensity_eff:

    Mean effective turbulence intensity for each location, held at its value at the highest wind speed simulated above it (only present when turbulence intensity is available)

Raises:

  • PywaspError – If a variable the calculation needs is not in the WWC; if no location has a known air density; if wind_turbines does not hold one turbine per WWC point; or if ws_upper_limit leaves fewer than two wind speeds to simulate.

  • TypeError – If wtg is neither an xr.Dataset nor a dict of them, or if a dict of several is passed without wind_turbines.

  • ValueError – If a custom wind farm model is used and turbulence_intensity is neither in the WWC nor passed explicitly.

Warns:

  • UserWarning – If the simulated wind speeds do not reach the wind speeds where the turbines still produce, which truncates the AEP integrals. Raise ws_upper_limit or leave it unset.

  • UserWarning – If some locations have no air density, which leaves their AEP missing.

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