import numpy as np
from numpy import newaxis as na
class AEPCalculator():
def __init__(self, wake_model):
"""Initialize AEPCalculator
Parameters
----------
site : py_wake.site.Site
windTurbines : WindTurbines
wake_model : WakeModel
"""
self.wake_model = wake_model
self.site = wake_model.site
self.windTurbines = wake_model.windTurbines
def _get_defaults(self, x_i, h_i, type_i, wd, ws):
type_i, h_i, _ = self.windTurbines.get_defaults(len(x_i), type_i, h_i)
wd, ws = self.site.get_defaults(wd, ws)
return h_i, type_i, wd, ws
def _run_wake_model(self, x_i, y_i, h_i=None, type_i=None, wd=None, ws=None):
h_i, type_i, wd, ws = self._get_defaults(x_i, h_i, type_i, wd, ws)
self.WS_eff_ilk, self.TI_eff_ilk, self.power_ilk, self.ct_ilk, self.WD_ilk, self.WS_ilk, self.TI_ilk, self.P_ilk =\
self.wake_model.calc_wake(x_i, y_i, h_i, type_i, wd, ws)
def calculate_AEP(self, x_i, y_i, h_i=None, type_i=None, wd=None, ws=None):
"""Calculate AEP
In addition effective wind speed, turbulence intensity, and the
power, ct and probability is calculated
Parameters
----------
x_i : array_like
X position of wind turbines
y_i : array_like
Y position of wind turbines
h_i : array_like or None, optional
Hub height of wind turbines\n
If None, default, the standard hub height is used
type_i array_like or None, optional
Wind turbine types\n
If None, default, the first type is used (type=0)
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
Returns
-------
AEP_GWh_ilk : array_like
AEP in GWh
"""
self._run_wake_model(x_i=x_i, y_i=y_i, h_i=h_i, type_i=type_i, wd=wd, ws=ws)
AEP_GWh_ilk = self.power_ilk * self.P_ilk * 24 * 365 * 1e-9
return AEP_GWh_ilk
def calculate_AEP_no_wake_loss(self, x_i, y_i, h_i=None, type_i=None, wd=None, ws=None):
"""Calculate AEP without wake loss(GWh). Same input as calculate_AEP"""
h_i, type_i, wd, ws = self._get_defaults(x_i, h_i, type_i, wd=wd, ws=ws)
# Find local wind speed, wind direction, turbulence intensity and probability
self.WD_ilk, self.WS_ilk, self.TI_ilk, self.P_ilk = self.site.local_wind(
x_i=x_i, y_i=y_i, h_i=h_i, wd=wd, ws=ws)
self.power_ilk = self.windTurbines.power(self.WS_ilk, type_i)
AEP_GWh_ilk = self.power_ilk * self.P_ilk * 24 * 365 * 1e-9
return AEP_GWh_ilk
def wake_map(self, x_j=None, y_j=None, height_level=None, wt_x=[], wt_y=[], wt_type=None, wt_height=None, wd=None, ws=None):
"""Calculate wake(effective wind speed) map
Parameters
----------
x_j : array_like or None, optional
X position map points
y_j : array_like
Y position of map points
height_level : int, float or None, optional
Height of wake map\n
If None, default, the mean hub height is used
wt_x : array_like, optional
X position of wind turbines
wt_y : array_like, optional
Y position of wind turbines
wt_type : array_like or None, optional
Type of the wind turbines
wt_height : array_like or None, optional
Hub height of the wind turbines\n
If None, default, the standard hub height is used
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
Returns
-------
X_j : array_like
2d array of map x positions
Y_j : array_like
2d array of map y positions
WS_eff_avg : array_like
2d array of average effective local wind speed taking into account
the probability of wind direction and speed
See Also
--------
plot_wake_map
"""
# X_j, Y_j, WS_eff_jlk, P_ilk = self._eff_map(
# 'WS', x_j, y_j, h, wt_x, wt_y, wt_type, wt_height, wd, ws)
res = self.wake_model.ws_map(x_j=x_j, y_j=y_j, h=height_level,
wt_x_i=wt_x, wt_y_i=wt_y, wt_type_i=wt_type, wt_h_i=wt_height,
wd=wd, ws=ws)
X_j, Y_j, WS_eff_jlk, WS_jlk, P_ilk = res
# if P_ilk.sum() > 0:
# WS_eff_jlk = WS_eff_jlk * (P_ilk / P_ilk.sum((1, 2)))
return X_j, Y_j, WS_eff_jlk.sum((1, 2)).reshape(X_j.shape)
def ti_map(self, x_j=None, y_j=None, height_level=None, wt_x=[], wt_y=[], wt_type=None, wt_height=None, wd=None, ws=None):
"""Calculate turbulence intensity map
Parameters
----------
x_j : array_like or None, optional
X position map points
y_j : array_like
Y position of map points
height_level : int, float or None, optional
Height of wake map\n
If None, default, the mean hub height is used
wt_x : array_like, optional
X position of wind turbines
wt_y : array_like, optional
Y position of wind turbines
wt_type : array_like or None, optional
Type of the wind turbines
wt_height : array_like or None, optional
Hub height of the wind turbines\n
If None, default, the standard hub height is used
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
Returns
-------
X_j : array_like
2d array of map x positions
Y_j : array_like
2d array of map y positions
WS_eff_avg : array_like
2d array of average effective local wind speed taking into account
the probability of wind direction and speed
See Also
--------
plot_wake_map
"""
X_j, Y_j, TI_eff_jlk, P_ilk = self.wake_model.ti_map(x_j=x_j, y_j=y_j, h=height_level,
wt_x_i=wt_x, wt_y_i=wt_y, wt_type_i=wt_type, wt_h_i=wt_height,
wd=wd, ws=ws)
if P_ilk.sum() > 0:
TI_eff_jlk = TI_eff_jlk * (P_ilk / P_ilk.sum((1, 2)))
return X_j, Y_j, TI_eff_jlk.sum((1, 2)).reshape(X_j.shape)
def plot_wake_map(self, x_j=None, y_j=None, h=None, wt_x=[], wt_y=[], wt_type=None, wt_height=None,
wd=None, ws=None, ax=None, levels=100):
"""Plot wake(effective wind speed) map
Parameters
----------
x_j : array_like or None, optional
X position map points
y_j : array_like
Y position of map points
h : int, float or None, optional
Height of wake map\n
If None, default, the mean hub height is used
wt_x : array_like, optional
X position of wind turbines
wt_y : array_like, optional
Y position of wind turbines
wt_type : array_like or None, optional
Type of the wind turbines
wt_height : array_like or None, optional
Hub height of the wind turbines\n
If None, default, the standard hub height is used
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
ax : pyplot or matplotlib axes object, default None
Axes to plot on
levels : int or array_like
levels for pyplot.contourf
"""
import matplotlib.pyplot as plt
if ax is None:
ax = plt.gca()
X, Y, Z = self.wake_map(x_j, y_j, h, wt_x, wt_y, wt_type, wt_height, wd, ws)
c = ax.contourf(X, Y, Z, levels, cmap='Blues_r')
plt.colorbar(c, label='wind speed [m/s]')
def aep_map(self, x_j=None, y_j=None, type_j=None, wt_x=[], wt_y=[], wt_type=None, wt_height=None, wd=None, ws=None):
"""Calculate AEP map
The map represents the of AEP produced by a new turbine at the specified positions
Parameters
----------
x_j : array_like or None, optional
X position map points (potential turbine positions)
y_j : array_like
Y position of map points (potential turbine positions)
type_j : int, float or None, optional
Type of potential turbine positions\n
If None, default, first turbine type(0) is used
wt_x : array_like, optional
X position of the current wind turbines
wt_y : array_like, optional
Y position of the current wind turbines
wt_type : array_like or None, optional
Type of the current wind turbines
wt_height : array_like or None, optional
Hub height of the current wind turbines\n
If None, default, the standard hub height is used
wd : int, float, array_like or None
Wind directions(s)\n
If None, default, the wake is calculated for site.default_wd
ws : int, float, array_like or None
Wind speed(s)\n
If None, default, the wake is calculated for site.default_ws
Returns
-------
X_j : array_like
2d array of map x positions
Y_j : array_like
2d array of map y positions
WS_eff_avg : array_like
2d array of average effective local wind speed taking into account
the probability of wind direction and speed
"""
h_j = self.windTurbines.hub_height(type_j)
res = self.wake_model.ws_map(x_j=x_j, y_j=y_j, h=h_j,
wt_x_i=wt_x, wt_y_i=wt_y, wt_type_i=wt_type, wt_h_i=wt_height,
wd=wd, ws=ws)
X_j, Y_j, WS_eff_jlk, WS_jlk, P_jlk = res
# power_jlk = self.windTurbines.power_func(WS_eff_jlk, type_j)
# aep_jlk = power_jlk * P_jlk * 24 * 365 * 1e-9
# return X_j, Y_j, aep_jlk.sum((1, 2)).reshape(X_j.shape)
if P_jlk.sum() > 0:
P_jlk /= P_jlk.sum((1, 2))
# same as above but requires less memory
return X_j, Y_j, ((self.windTurbines.power(WS_eff_jlk, type_j) * P_jlk).sum((1, 2)) * 24 * 365 * 1e-9).reshape(X_j.shape)
def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37 import iea37_path
from py_wake.examples.data.iea37._iea37 import IEA37Site
from py_wake.examples.data.iea37._iea37 import IEA37_WindTurbines
from py_wake.wake_models import NOJ
# setup site, turbines and wakemodel
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = NOJ(site, windTurbines)
# calculate AEP
aep_calculator = AEPCalculator(wake_model)
aep = aep_calculator.calculate_AEP(x, y)[0].sum()
print(aep_calculator.WS_eff_ilk.shape)
# plot wake map
import matplotlib.pyplot as plt
aep_calculator.plot_wake_map(wt_x=x, wt_y=y, wd=[0], ws=[9])
plt.title('AEP: %.2f GWh' % aep)
windTurbines.plot(x, y)
plt.show()
main()