def test_aep_map():
site = IEA37Site(16)
x = [0, 0]
y = [0, 200]
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
aep = AEPCalculator(wake_model)
# print(aep.calculate_AEP([0], [0]).sum())
x_j = np.arange(-150, 150, 20)
y_j = np.arange(-250, 250, 20)
X, Y, Z = aep.aep_map(x_j, y_j, 0, x, y, wd=[0], ws=np.arange(3, 25))
m = 17
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z, 100) # , np.arange(2, 10, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
plt.plot(X[m], Y[m], '.-r')
plt.show()
# print(np.round(Z[m], 2).tolist()) # ref
ref = [
21.5, 21.4, 21.02, 20.34, 18.95, 16.54, 13.17, 10.17, 10.17, 13.17,
16.54, 18.95, 20.34, 21.02, 21.4
]
npt.assert_array_almost_equal(Z[m], ref, 2)
def get_iea37_cost(n_wt=9):
"""Cost component that wraps the IEA 37 AEP calculator"""
wd = npa(range(16)) * 22.5 # only 16 bins
site = IEA37Site(n_wt)
wind_turbines = IEA37_WindTurbines()
wake_model = IEA37SimpleBastankhahGaussian(wind_turbines)
aep_calc = PyWakeAEP(site, wind_turbines, wake_model)
return aep_calc.get_TopFarm_cost_component(n_wt, wd=wd)
def main():
if __name__ == '__main__':
try:
import matplotlib.pyplot as plt
plt.gcf()
plot_comp = XYPlotComp()
plot = True
except RuntimeError:
plot_comp = NoPlot()
plot = False
n_wt = 16
site = IEA37Site(n_wt)
windTurbines = IEA37_WindTurbines()
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
Drotor_vector = [windTurbines.diameter()] * n_wt
power_rated_vector = [float(windTurbines.power(20) / 1000)] * n_wt
hub_height_vector = [windTurbines.hub_height()] * n_wt
AEPCalc = AEPCalculator(wake_model)
def aep_func(x, y, **kwargs):
return AEPCalc.calculate_AEP(x_i=x, y_i=y).sum(-1).sum(-1) * 10**6
def irr_func(aep, **kwargs):
my_irr = economic_evaluation(Drotor_vector, power_rated_vector,
hub_height_vector,
aep).calculate_irr()
print(my_irr)
return my_irr
aep_comp = CostModelComponent(input_keys=['x', 'y'],
n_wt=n_wt,
cost_function=aep_func,
output_key="aep",
output_unit="GWh",
objective=False,
output_val=np.zeros(n_wt))
irr_comp = CostModelComponent(input_keys=['aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True,
income_model=True)
group = TopFarmGroup([aep_comp, irr_comp])
problem = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=group,
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Circle(), max_iter=50),
constraints=[
SpacingConstraint(200),
CircleBoundaryConstraint([0, 0], 1300.1)
],
plot_comp=plot_comp)
cost, state, recorder = problem.optimize()
def test_aep_map_no_turbines():
_, _, freq = read_iea37_windrose(iea37_path + "iea37-windrose.yaml")
site = UniformSite(freq, ti=0.075)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
aep = AEPCalculator(wake_model)
x_j = np.arange(-150, 150, 20)
y_j = np.arange(-250, 250, 20)
X, Y, Z = aep.aep_map(x_j, y_j, 0, [], [], wd=[0])
expect = (3.35 * 1e6) * 24 * 365 * (1e-9)
npt.assert_array_almost_equal(Z, expect, 2)
def test_aep_map_no_turbines():
_, _, freq = read_iea37_windrose(iea37_path + "iea37-windrose.yaml")
# n_wt = 16
# x, y, _ = read_iea37_windfarm(iea37_path + 'iea37-ex%d.yaml' % n_wt)
site = UniformSite(freq, ti=0.75)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
x_j = np.arange(-150, 150, 20)
y_j = np.arange(-250, 250, 20)
X, Y, Z = aep.aep_map(x_j, y_j, 0, [], [], wd=[0])
npt.assert_array_almost_equal(Z, 21.89, 2)
def main():
if __name__ == '__main__':
site = IEA37Site(16)
windTurbines = IEA37_WindTurbines()
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
aep_calc = PyWakeAEP(wake_model)
tf = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=aep_calc.get_TopFarm_cost_component(16),
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Circle(), max_iter=5),
constraints=[CircleBoundaryConstraint([0, 0], 1300.1)],
plot_comp=XYPlotComp())
tf.optimize()
tf.plot_comp.show()
def main():
if __name__ == '__main__':
plot_comp = XYPlotComp()
site = get_site()
n_wt = len(site.initial_position)
windTurbines = DTU10MW()
min_spacing = 2 * windTurbines.diameter(0)
wake_model = IEA37SimpleBastankhahGaussian(site, windTurbines)
Drotor_vector = [windTurbines.diameter()] * n_wt
power_rated_vector = [float(windTurbines.power(20) / 1000)] * n_wt
hub_height_vector = [windTurbines.hub_height()] * n_wt
AEPCalc = AEPCalculator(wake_model)
def aep_func(x, y, **kwargs):
return AEPCalc.calculate_AEP(x_i=x, y_i=y).sum(-1).sum(-1) * 10**6
def irr_func(aep, **kwargs):
return economic_evaluation(Drotor_vector, power_rated_vector,
hub_height_vector, aep).calculate_irr()
aep_comp = CostModelComponent(input_keys=['x', 'y'],
n_wt=n_wt,
cost_function=aep_func,
output_key="aep",
output_unit="GWh",
objective=False,
output_val=np.zeros(n_wt))
irr_comp = CostModelComponent(input_keys=['aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True,
income_model=True)
group = TopFarmGroup([aep_comp, irr_comp])
problem = TopFarmProblem(
design_vars=dict(zip('xy', site.initial_position.T)),
cost_comp=group,
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Circle(), max_iter=50),
constraints=[
SpacingConstraint(min_spacing),
XYBoundaryConstraint(site.boundary),
],
plot_comp=plot_comp)
cost, state, recorder = problem.optimize()
def test_IEA37SimpleBastankhahGaussian_ex16():
_, _, freq = read_iea37_windrose(iea37_path + "iea37-windrose.yaml")
n_wt = 16
x, y, aep_ref = read_iea37_windfarm(iea37_path + 'iea37-ex%d.yaml' % n_wt)
if 0:
import matplotlib.pyplot as plt
plt.plot(x, y, '2k')
for i, (x_, y_) in enumerate(zip(x, y)):
plt.annotate(i, (x_, y_))
plt.axis('equal')
plt.show()
site = UniformSite(freq, ti=0.75)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
aep_ilk = aep.calculate_AEP(x, y, wd=np.arange(0, 360, 22.5), ws=[9.8])
aep_MW_l = aep_ilk.sum((0, 2)) * 1000
# test that the result is equal to results provided for IEA task 37
npt.assert_almost_equal(aep_ref[0], aep_MW_l.sum(), 5)
npt.assert_array_almost_equal(aep_ref[1], aep_MW_l, 5)
def test_IEA37SimpleBastankhahGaussian_wake_map():
site = IEA37_Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = IEA37SimpleBastankhahGaussian(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
x_j = np.linspace(-1500, 1500, 200)
y_j = np.linspace(-1500, 1500, 100)
X, Y, Z = aep.wake_map(x_j, y_j, 110, x, y, wd=[0], ws=[9])
# test that the result is equal to last run (no evidens that these number are correct)
ref = [
3.32, 4.86, 7.0, 8.1, 7.8, 7.23, 6.86, 6.9, 7.3, 7.82, 8.11, 8.04,
7.87, 7.79, 7.85, 8.04, 8.28
]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z, np.arange(2, 9.1, .01))
plt.colorbar(c)
site.plot_windturbines(x, y)
plt.plot(X[49, 100:133:2], Y[49, 100:133:2], '-.')
plt.show()