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switching from kW-s to kW-hr for AEP per standard conventions on units #39

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Merged
gbarter merged 1 commit into from
Sep 9, 2025
Merged

switching from kW-s to kW-hr for AEP per standard conventions on units #39

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2 changes: 1 addition & 1 deletion examples/crunch_example.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -54015,7 +54015,7 @@
}
],
"source": [
"# Probability weighted and unweighted AEP values are returned\n",
"# Probability weighted and unweighted AEP values are returned in kWh\n",
"mycruncher_mc.set_probability_turbine_class('Wind1VelX', 2)\n",
"mycruncher_mc.compute_aep('GenPwr')"
]
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14 changes: 7 additions & 7 deletions pCrunch/crunch.py
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Expand Up @@ -448,7 +448,7 @@ def set_probability_turbine_class(self, windspeed, turbine_class, idx=None):

def compute_aep(self, pwrchan, loss_factor=0.0, idx=None):
"""
Computes annual energy production based on all outputs in the list.
Computes annual energy production [kWh] based on all outputs in the list.

Parameters
----------
Expand All @@ -463,10 +463,10 @@ def compute_aep(self, pwrchan, loss_factor=0.0, idx=None):

Returns
----------
aep_weighted : float
aep_weighted_kWh : float
Weighted AEP where each output is weighted by the probability of
occurence determined from its average wind speed
aep_unweighted : float
aep_unweighted_kWh : float
Unweighted AEP that assumes all outputs in the list are equally
likely (just a mean)
"""
Expand All @@ -485,16 +485,16 @@ def compute_aep(self, pwrchan, loss_factor=0.0, idx=None):
prob = prob / prob.sum()

# Calculate scaling factor for year with losses included
fact = (1.0 - loss_factor) * 365.0 * 24.0 * 60.0 * 60.0
fact = (1.0 - loss_factor) * 365.0 * 24.0 #* 60.0 * 60.0 # Add these in for kW-s

# Sum with probability. Finding probability weighted average per second, scaled by total seconds
aep_weighted = fact * np.dot(E, prob) / np.dot(T, prob)
aep_weighted_kWh = fact * np.dot(E, prob) / np.dot(T, prob)

# Assume equal probability
prob = np.ones(E.shape)/E.size
aep_unweighted = fact * np.dot(E, prob) / np.dot(T, prob)
aep_unweighted_kWh = fact * np.dot(E, prob) / np.dot(T, prob)

return aep_weighted, aep_unweighted
return aep_weighted_kWh, aep_unweighted_kWh


def compute_total_fatigue(self, lifetime=0.0, availability=1.0,
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2 changes: 1 addition & 1 deletion pCrunch/test/test_crunch.py
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Expand Up @@ -290,7 +290,7 @@ def test_aep(self):
myout = AeroelasticOutput(data)
myouts = [myout]*10

aep_true = myout.compute_energy('WindVxi') * 8760*60*60/myout.elapsed_time
aep_true = myout.compute_energy('WindVxi') * 8760/myout.elapsed_time

myobj = Crunch(myouts, magnitude_channels=mc)
aep_w1, aep_uw1 = myobj.compute_aep('Wind')
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2 changes: 1 addition & 1 deletion pyproject.toml
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Expand Up @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"

[project]
name = "pCrunch"
version = "2.1.0"
version = "2.1.1"
description = "IO and Post Processing for generic time series data of multibody aeroelastic wind turbine simulations."
readme = "README.rst"
requires-python = ">=3.9"
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