from abc import abstractmethod, ABC
from py_wake.site._site import Site, UniformSite, UniformWeibullSite, LocalWind
from py_wake.wind_turbines import WindTurbines
import numpy as np
from py_wake.flow_map import FlowMap, HorizontalGrid, FlowBox, YZGrid, Grid, Points
import xarray as xr
from py_wake.utils import xarray_utils, weibull # register ilk function @UnusedImport
from numpy import newaxis as na
from py_wake.utils.model_utils import check_model, fix_shape
class WindFarmModel(ABC):
"""Base class for RANS and engineering flow models"""
verbose = True
def __init__(self, site, windTurbines):
check_model(site, Site, 'site')
check_model(windTurbines, WindTurbines, 'windTurbines')
self.site = site
self.windTurbines = windTurbines
def __call__(self, x, y, h=None, type=0, wd=None, ws=None, yaw=None, time=False, verbose=False, **kwargs):
"""Run the wind farm simulation
Parameters
----------
x : array_like
Wind turbine x positions
y : array_like
Wind turbine y positions
h : array_like, optional
Wind turbine hub heights
type : int or array_like, optional
Wind turbine type, default is 0
wd : int or array_like
Wind direction(s)
ws : int, float or array_like
Wind speed(s)
yaw_ilk : array_like or None, optional
Yaw misalignement of turbine(i) for wind direction(l) and wind speed (k)\n
Positive is counter-clockwise when seen from above
Returns
-------
SimulationResult
"""
if time is False and np.ndim(wd):
wd = np.sort(wd)
assert len(x) == len(y)
self.verbose = verbose
h, _ = self.windTurbines.get_defaults(len(x), type, h)
I, L, K, = len(x), len(np.atleast_1d(wd)), (1, len(np.atleast_1d(ws)))[time is False]
if len([k for k in kwargs if 'yaw' in k.lower() and k != 'yaw' and not k.startswith('yawc_')]):
raise ValueError(
'Custom *yaw*-keyword arguments not allowed to avoid confusion with the default "yaw" keyword')
yaw_ilk = fix_shape(yaw, (I, L, K), allow_None=True)
if len(x) == 0:
lw = UniformSite([1], 0.1).local_wind(x_i=[], y_i=[], h_i=[], wd=wd, ws=ws)
z = xr.DataArray(np.zeros((0, len(lw.wd), len(lw.ws))), coords=[('wt', []), ('wd', lw.wd), ('ws', lw.ws)])
return SimulationResult(self, lw, [], yaw, z, z, z, z, kwargs)
res = self.calc_wt_interaction(x_i=np.asarray(x), y_i=np.asarray(y), h_i=h, type_i=type, yaw_ilk=yaw_ilk,
wd=wd, ws=ws, time=time, **kwargs)
WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk, localWind, wt_inputs = res
return SimulationResult(self, localWind=localWind,
type_i=np.zeros(len(x), dtype=int) + type, yaw_ilk=yaw_ilk,
WS_eff_ilk=WS_eff_ilk, TI_eff_ilk=TI_eff_ilk,
power_ilk=power_ilk, ct_ilk=ct_ilk, wt_inputs=wt_inputs)
def aep(self, x, y, h=None, type=0, wd=None, ws=None, yaw_ilk=None, # @ReservedAssignment
normalize_probabilities=False, with_wake_loss=True):
"""Anual Energy Production (sum of all wind turbines, directions and speeds) in GWh.
the typical use is:
>> sim_res = windFarmModel(x,y,...)
>> sim_res.aep()
This function bypasses the simulation result and returns only the total AEP,
which makes it slightly faster for small problems.
>> windFarmModel.aep(x,y,...)
"""
_, _, power_ilk, _, localWind, power_ct_inputs = self.calc_wt_interaction(
x_i=x, y_i=y, h_i=h, type_i=type, yaw_ilk=yaw_ilk, wd=wd, ws=ws)
P_ilk = localWind.P_ilk
if normalize_probabilities:
norm = P_ilk.sum((1, 2))[:, na, na]
else:
norm = 1
if with_wake_loss is False:
power_ilk = self.windTurbines.power(localWind.WS_ilk, **power_ct_inputs)
return (power_ilk * P_ilk / norm * 24 * 365 * 1e-9).sum()
@abstractmethod
def calc_wt_interaction(self, x_i, y_i, h_i=None, type_i=None, yaw_ilk=None,
wd=None, ws=None, time=False, **kwargs):
"""Calculate effective wind speed, turbulence intensity,
power and thrust coefficient, and local site parameters
Typical users should not call this function directly, but by calling the
windFarmModel object (invokes the __call__() function above)
which returns a nice SimulationResult object
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)
yaw_ilk : array_like or None, optional
Yaw misalignement [deg] of turbine(i) for wind direction(l) and wind speed (k)\n
Positive is counter-clockwise when seen from above
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
-------
WS_eff_ilk : array_like
Effective wind speeds [m/s]
TI_eff_ilk : array_like
Effective turbulence intensities [-]
power_ilk : array_like
Power productions [w]
ct_ilk : array_like
Thrust coefficients
localWind : LocalWind
Local free-flow wind
"""
class SimulationResult(xr.Dataset):
"""Simulation result returned when calling a WindFarmModel object"""
__slots__ = ('windFarmModel', 'localWind', 'wt_inputs')
def __init__(self, windFarmModel, localWind, type_i, yaw_ilk,
WS_eff_ilk, TI_eff_ilk, power_ilk, ct_ilk, wt_inputs):
self.windFarmModel = windFarmModel
lw = localWind
self.localWind = localWind
self.wt_inputs = wt_inputs
n_wt = len(lw.i)
coords = {k: (dep, v, {'Description': d}) for k, dep, v, d in [
('wt', 'wt', np.arange(n_wt), 'Wind turbine number'),
('wd', ('wd', 'time')['time' in lw], lw.wd.values, 'Ambient reference wind direction [deg]'),
('ws', ('ws', 'time')['time' in lw], lw.ws.values, 'Ambient reference wind speed [m/s]'),
('x', 'wt', lw.x.values, 'Wind turbine x coordinate [m]'),
('y', 'wt', lw.y.values, 'Wind turbine y coordinate [m]'),
('h', 'wt', lw.h.values, 'Wind turbine hub height [m]'),
('type', 'wt', type_i, 'Wind turbine type')]}
ilk_dims = (['wt', 'wd', 'ws'], ['wt', 'time'])['time' in lw]
xr.Dataset.__init__(self,
data_vars={k: (ilk_dims, (v, v[:, :, 0])['time' in lw], {'Description': d}) for k, v, d in [
('WS_eff', WS_eff_ilk, 'Effective local wind speed [m/s]'),
('TI_eff', np.zeros_like(WS_eff_ilk) + TI_eff_ilk,
'Effective local turbulence intensity'),
('Power', power_ilk, 'Power [W]'),
('CT', ct_ilk, 'Thrust coefficient'),
]},
coords=coords)
for n in localWind:
self[n] = localWind[n]
self.attrs.update(localWind.attrs)
for n in set(wt_inputs) - {'type', 'TI_eff', 'yaw'}:
if '_ijl' in n:
self.add_ijlk(n, wt_inputs[n])
else:
self.add_ilk(n, wt_inputs[n])
if yaw_ilk is None:
self['yaw'] = self.Power * 0
else:
self.add_ilk('yaw', yaw_ilk)
self['yaw'].attrs['Description'] = 'Yaw misalignment [deg]'
# for backward compatibility
for k in ['WD', 'WS', 'TI', 'P', 'WS_eff', 'TI_eff']:
setattr(self.__class__, "%s_ilk" % k, property(lambda self, k=k: self[k].ilk()))
setattr(self.__class__, "ct_ilk", property(lambda self: self.CT.ilk()))
setattr(self.__class__, "power_ilk", property(lambda self: self.Power.ilk()))
def aep_ilk(self, normalize_probabilities=False, with_wake_loss=True):
"""Anual Energy Production of all turbines (i), wind directions (l) and wind speeds (k) in in GWh
Parameters
----------
normalize_propabilities : Optional bool, defaults to False
In case only a subset of all wind speeds and/or wind directions is simulated,
this parameter determines whether the returned AEP represents the energy produced in the fraction
of a year where these flow cases occur or a whole year of only these cases.
If for example, wd=[0], then
- False means that the AEP only includes energy from the faction of year\n
with northern wind (359.5-0.5deg), i.e. no power is produced the rest of the year.
- True means that the AEP represents a whole year of northen wind.
with_wake_loss : Optional bool, defaults to True
If True, wake loss is included, i.e. power is calculated using local effective wind speed\n
If False, wake loss is neglected, i.e. power is calculated using local free flow wind speed
"""
return self.aep(normalize_probabilities=normalize_probabilities, with_wake_loss=with_wake_loss).ilk()
def aep(self, normalize_probabilities=False, with_wake_loss=True,
hours_pr_year=24 * 365, linear_power_segments=False):
"""Anual Energy Production (sum of all wind turbines, directions and speeds) in GWh.
See aep_ilk
"""
if normalize_probabilities:
norm = self.P.ilk().sum((1, 2))[:, na, na]
else:
norm = 1
if with_wake_loss:
power_ilk = self.Power.ilk()
else:
power_ilk = self.windFarmModel.windTurbines.power(self.WS.ilk(self.Power.shape), **self.wt_inputs)
if linear_power_segments:
s = "The linear_power_segments method "
assert all([n in self for n in ['Weibull_A', 'Weibull_k', 'Sector_frequency']]),\
s + "requires a site with weibull information"
assert normalize_probabilities is False, \
s + "cannot be combined with normalize_probabilities"
assert np.all(self.Power.isel(ws=0) == 0) and np.all(self.Power.isel(ws=-1) == 0),\
s + "requires first wind speed to have no power (just below cut-in)"
assert np.all(self.Power.isel(ws=-1) == 0),\
s + "requires last wind speed to have no power (just above cut-out)"
weighted_power = weibull.WeightedPower(
self.ws.values,
self.Power.ilk(),
self.Weibull_A.ilk(),
self.Weibull_k.ilk())
aep = weighted_power * self.Sector_frequency.ilk() * hours_pr_year * 1e-9
ws = (self.ws.values[1:] + self.ws.values[:-1]) / 2
return xr.DataArray(aep, [('wt', self.wt), ('wd', self.wd), ('ws', ws)])
else:
weighted_power = power_ilk * self.P.ilk() / norm
if 'time' in self and weighted_power.shape[2] == 1:
weighted_power = weighted_power[:, :, 0]
return xr.DataArray(weighted_power * hours_pr_year * 1e-9,
self.Power.coords,
name='AEP [GWh]',
attrs={'Description': 'Annual energy production [GWh]'})
def loads(self, method, lifetime_years=20, n_eq_lifetime=1e7, normalize_probabilities=False, softmax_base=None):
assert method in ['TwoWT', 'OneWT_WDAvg', 'OneWT']
wt = self.windFarmModel.windTurbines
P_ilk = self.P_ilk
if normalize_probabilities:
P_ilk /= P_ilk.sum((1, 2))[:, na, na]
WS_eff_ilk = self.WS_eff_ilk
TI_eff_ilk = self.TI_eff_ilk
kwargs = self.wt_inputs
if method == 'OneWT_WDAvg': # average over wd
p_wd_ilk = P_ilk.sum((0, 2))[na, :, na]
ws_ik = (WS_eff_ilk * p_wd_ilk).sum(1)
kwargs_ik = {k: (fix_shape(v, WS_eff_ilk) * p_wd_ilk).sum(1) for k, v in kwargs.items()
if k != 'TI_eff' and v is not None}
kwargs_ik.update({k: v for k, v in kwargs.items() if v is None})
loads, i_lst = [], []
m_lst = np.asarray(wt.loadFunction.wohler_exponents)
for m in np.unique(m_lst):
i = np.where(m_lst == m)[0]
if 'TI_eff' in kwargs:
kwargs_ik['TI_eff'] = ((p_wd_ilk * TI_eff_ilk ** m).sum(1)) ** (1 / m)
loads.extend(wt.loads(ws_ik, run_only=i, **kwargs_ik))
i_lst.extend(i)
loads = [loads[i] for i in np.argsort(i_lst)] # reorder
ds = xr.DataArray(
loads,
dims=['sensor', 'wt', 'ws'],
coords={'sensor': wt.loadFunction.output_keys,
'm': ('sensor', wt.loadFunction.wohler_exponents),
'wt': self.wt, 'ws': self.ws},
attrs={'description': '1Hz Damage Equivalent Load'}).to_dataset(name='DEL')
ds['P'] = self.P.sum('wd')
t_flowcase = ds.P * lifetime_years * 365 * 24 * 3600
f = ds.DEL.mean() # factor used to reduce numerical errors in power
ds['LDEL'] = ((t_flowcase * (ds.DEL / f)**ds.m).sum('ws') / n_eq_lifetime)**(1 / ds.m) * f
ds.LDEL.attrs['description'] = "Lifetime (%d years) equivalent loads, n_eq_L=%d" % (
lifetime_years, n_eq_lifetime)
elif method == 'OneWT' or method == 'TwoWT':
if method == 'OneWT':
loads_silk = wt.loads(WS_eff_ilk, **kwargs)
else: # method == 'TwoWT':
I, L, K = WS_eff_ilk.shape
ws_iilk = np.broadcast_to(WS_eff_ilk[na], (I, I, L, K))
def _fix_shape(k, v):
if k[-3:] == 'ijl':
return fix_shape(v, ws_iilk)
else:
return np.broadcast_to(fix_shape(v, WS_eff_ilk)[na], (I, I, L, K))
kwargs_iilk = {k: _fix_shape(k, v)
for k, v in kwargs.items()
if k in wt.loadFunction.required_inputs + wt.loadFunction.optional_inputs}
loads_siilk = np.array(wt.loads(ws_iilk, **kwargs_iilk))
if softmax_base is None:
loads_silk = loads_siilk.max(1)
else:
# factor used to reduce numerical errors in power
f = loads_siilk.mean((1, 2, 3, 4)) / 10
loads_silk = (np.log((softmax_base**(loads_siilk / f[:, na, na, na, na])).sum(1)) /
np.log(softmax_base) * f[:, na, na, na])
if 'time' in self.dims:
ds = xr.DataArray(
np.array(loads_silk)[..., 0],
dims=['sensor', 'wt', 'time'],
coords={'sensor': wt.loadFunction.output_keys,
'm': ('sensor', wt.loadFunction.wohler_exponents, {'description': 'Wohler exponents'}),
'wt': self.wt, 'time': self.time, 'wd': self.wd, 'ws': self.ws},
attrs={'description': '1Hz Damage Equivalent Load'}).to_dataset(name='DEL')
else:
ds = xr.DataArray(
loads_silk,
dims=['sensor', 'wt', 'wd', 'ws'],
coords={'sensor': wt.loadFunction.output_keys,
'm': ('sensor', wt.loadFunction.wohler_exponents, {'description': 'Wohler exponents'}),
'wt': self.wt, 'wd': self.wd, 'ws': self.ws},
attrs={'description': '1Hz Damage Equivalent Load'}).to_dataset(name='DEL')
f = ds.DEL.mean() # factor used to reduce numerical errors in power
if 'time' in self.dims:
assert 'duration' in self, "Simulation must contain a dataarray 'duration' with length of time steps in seconds"
t_flowcase = self.duration
ds['LDEL'] = ((t_flowcase * (ds.DEL / f)**ds.m).sum(('time')) / n_eq_lifetime)**(1 / ds.m) * f
else:
ds['P'] = self.P
t_flowcase = ds.P * 3600 * 24 * 365 * lifetime_years
ds['LDEL'] = ((t_flowcase * (ds.DEL / f)**ds.m).sum(('wd', 'ws')) / n_eq_lifetime)**(1 / ds.m) * f
ds.LDEL.attrs['description'] = "Lifetime (%d years) equivalent loads, n_eq_L=%d" % (
lifetime_years, n_eq_lifetime)
return ds
def flow_box(self, x, y, h, wd=None, ws=None):
X, Y, H = np.meshgrid(x, y, h)
x_j, y_j, h_j = X.flatten(), Y.flatten(), H.flatten()
wd, ws = self._wd_ws(wd, ws)
lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map(
x_j, y_j, h_j,
self.sel(wd=wd, ws=ws)
)
return FlowBox(self, X, Y, H, lw_j, WS_eff_jlk, TI_eff_jlk)
def flow_map(self, grid=None, wd=None, ws=None):
"""Return a FlowMap object with WS_eff and TI_eff of all grid points
Parameters
----------
grid : Grid or tuple(X, Y, x, y, h)
Grid, e.g. HorizontalGrid or\n
tuple(X, Y, x, y, h) where X, Y is the meshgrid for visualizing data\n
and x, y, h are the flattened grid points
See Also
--------
pywake.wind_farm_models.flow_map.FlowMap
"""
if grid is None:
grid = HorizontalGrid()
if isinstance(grid, Grid):
if isinstance(grid, HorizontalGrid):
plane = "XY", self.h
if isinstance(grid, YZGrid):
plane = 'YZ', grid.x
if isinstance(grid, Points):
plane = 'xyz', None
grid = grid(x_i=self.x, y_i=self.y, h_i=self.h,
d_i=self.windFarmModel.windTurbines.diameter(self.type))
else:
plane = (None,)
wd, ws = self._wd_ws(wd, ws)
X, Y, x_j, y_j, h_j = grid
lw_j, WS_eff_jlk, TI_eff_jlk = self.windFarmModel._flow_map(
x_j, y_j, h_j,
self.sel(wd=wd, ws=ws)
)
return FlowMap(self, X, Y, lw_j, WS_eff_jlk, TI_eff_jlk, plane=plane)
def _wd_ws(self, wd, ws):
if wd is None:
wd = self.wd
else:
assert np.all(np.isin(wd, self.wd)), "All wd=%s not in simulation result" % wd
if ws is None:
ws = self.ws
else:
assert np.all(np.isin(ws, self.ws)), "All ws=%s not in simulation result (ws=%s)" % (ws, self.ws)
return np.atleast_1d(wd), np.atleast_1d(ws)
def save(self, filename):
self.to_netcdf(filename)
@staticmethod
def load(filename, wfm):
ds = xr.load_dataset(filename)
lw = LocalWind(ds.x, ds.y, ds.h, ds.wd, ds.ws, time=False, wd_bin_size=ds.attrs['wd_bin_size'],
WD=ds.WD, WS=ds.WS, TI=ds.TI, P=ds.P)
sim_res = SimulationResult(wfm, lw, type_i=ds.type.values, yaw_ilk=ds.yaw,
WS_eff_ilk=ds.WS_eff.ilk(), TI_eff_ilk=ds.TI_eff.ilk(), power_ilk=ds.Power, ct_ilk=ds.CT,
wt_inputs={})
return sim_res
def main():
if __name__ == '__main__':
from py_wake.examples.data.iea37 import IEA37Site, IEA37_WindTurbines
from py_wake import IEA37SimpleBastankhahGaussian
import matplotlib.pyplot as plt
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wind_farm_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
simulation_result = wind_farm_model(x, y)
fm = simulation_result.flow_map(wd=30)
fm.plot_wake_map()
plt.figure()
fm.plot(fm.power_xylk().sum(['wd', 'ws']) * 1e-3, "Power [kW]")
fm = simulation_result.flow_map(grid=HorizontalGrid(resolution=50))
plt.figure()
fm.plot(fm.aep_xy(), "AEP [GWh]")
plt.show()
main()