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Mads M. Pedersen authoredadd selfsimilarity blokage model prepare for gradients New structure. Old WakeModel mainly in new EngineeringWindFarmModel add deflectionmodel update documentation
Mads M. Pedersen authoredadd selfsimilarity blokage model prepare for gradients New structure. Old WakeModel mainly in new EngineeringWindFarmModel add deflectionmodel update documentation
aep_calculator.py 10.65 KiB
import warnings
from py_wake.flow_map import HorizontalGrid
class AEPCalculator():
def __init__(self, wake_model):
"""Initialize AEPCalculator
Parameters
----------
site : py_wake.site.Site
windTurbines : WindTurbines
flow_model : FlowModel
"""
warnings.warn("""AEPCalculator(wake_model) is deprecated;
wake_model(x_i, y_i, ...) returns a flowModelResult with same functionality as AEPCalculator.""", DeprecationWarning)
self.wake_model = wake_model
self.site = wake_model.site
self.windTurbines = wake_model.windTurbines
def _set_flowModelResult(self, flowModelResult):
for n in ['WS_eff_ilk', 'TI_eff_ilk', 'power_ilk', 'ct_ilk']:
setattr(self, n, getattr(flowModelResult, n))
for n in ['WD_ilk', 'WS_ilk', 'TI_ilk', 'P_ilk']:
setattr(self, n, getattr(flowModelResult.localWind, n))
def calculate_AEP(self, x_i, y_i, h_i=None, type_i=0, 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
"""
flowModelResult = self.wake_model(x=x_i, y=y_i, h=h_i, type=type_i, wd=wd, ws=ws)
self._set_flowModelResult(flowModelResult)
return flowModelResult.aep_ilk()
def calculate_AEP_no_wake_loss(self, x_i, y_i, h_i=None, type_i=0, wd=None, ws=None):
"""Calculate AEP without wake loss(GWh). Same input as calculate_AEP"""
flowModelResult = self.wake_model(x=x_i, y=y_i, h=h_i, type=type_i, wd=wd, ws=ws)
self._set_flowModelResult(flowModelResult)
return flowModelResult.aep_ilk(with_wake_loss=False)
def wake_map(self, x_j=None, y_j=None, height_level=None, wt_x=[],
wt_y=[], wt_type=0, 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
"""
sim_res = self.wake_model(x=wt_x, y=wt_y, type=wt_type, h=wt_height, wd=wd, ws=ws)
flow_map = sim_res.flow_map(HorizontalGrid(x=x_j, y=y_j, h=height_level))
X, Y = flow_map.XY
return X, Y, flow_map.WS_eff_xylk.mean((2, 3))
def ti_map(self, x_j=None, y_j=None, height_level=None, wt_x=[],
wt_y=[], wt_type=0, 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
"""
sim_res = self.wake_model(x=wt_x, y=wt_y, type=wt_type, h=wt_height, wd=wd, ws=ws)
flow_map = sim_res.flow_map(HorizontalGrid(x=x_j, y=y_j, h=height_level))
X, Y = flow_map.XY
return X, Y, flow_map.TI_eff_xylk.mean((2, 3))
def plot_wake_map(self, x_j=None, y_j=None, h=None, wt_x=[], wt_y=[], wt_type=0, 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=0, 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)
sim_res = self.wake_model(x=wt_x, y=wt_y, type=wt_type, h=wt_height, wd=wd, ws=ws)
flow_map = sim_res.flow_map(HorizontalGrid(x=x_j, y=y_j, h=h_j))
X, Y = flow_map.XY
aep_xy = flow_map.aep_xy(normalize_probabilities=True)
return X, Y, aep_xy
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 import NOJ
# setup site, turbines and flow model
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()