def test_NOJ_Nibe_result():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
site = UniformSite([1], 0.1)
wake_model = NOJ(site, NibeA0)
x_i = [0, 0, 0]
y_i = [0, -40, -100]
h_i = [50, 50, 50]
WS_eff_ilk = wake_model.calc_wake(x_i, y_i, h_i, [0, 1, 1], 0.0, 8.1)[0]
npt.assert_array_almost_equal(WS_eff_ilk[:, 0, 0], [8.1, 4.35, 5.7])
def test_NOJ_Nibe_result():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
wake_model = NOJ(NibeA0)
WS_ilk = np.array([[[8.1]], [[8.1]], [[8.1]]])
TI_ilk = np.zeros_like(WS_ilk)
site = UniformSite([1], 0.1)
dw_iil, cw_iil, dh_iil, dw_order_indices_l = site.wt2wt_distances(
x_i=[0, 0, 0], y_i=[0, -40, -100], h_i=[50, 50, 50], wd_il=[[0]])
WS_eff_ilk = wake_model.calc_wake(WS_ilk, TI_ilk, dw_iil, cw_iil, dh_iil, dw_order_indices_l, [0, 1, 1])[0]
npt.assert_array_almost_equal(WS_eff_ilk[:, 0, 0], [8.1, 4.35, 5.7])
def test_NOJ_two_turbines_in_row(wdir, x, y):
# Two turbines in a row, North-South
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
windTurbines = NibeA0
site = UniformSite([1], 0.1)
wake_model = NOJ(site, windTurbines)
h_i = [50, 50, 50]
WS_eff_ilk = wake_model.calc_wake(x, y, h_i, [0, 0, 0], wdir, 8.1)[0]
ws_wt3 = 8.1 - np.hypot(8.1 * 2 / 3 * (20 / 26)**2, 8.1 * 2 / 3 *
(20 / 30)**2)
npt.assert_array_almost_equal(WS_eff_ilk[:, 0, 0], [8.1, 4.35, ws_wt3])
def test_NOJ_6_turbines_in_row():
n_wt = 6
x = [0] * n_wt
y = -np.arange(n_wt) * 40 * 2
site = UniformSite([1], 0.1)
wake_model = NOJ(site, NibeA0)
WS_eff_ilk = wake_model.calc_wake(x, y, [50] * n_wt, [0.0] * n_wt, 0.0,
11.0)[0]
np.testing.assert_array_almost_equal(
WS_eff_ilk[1:, 0, 0], 11 - np.sqrt(
np.cumsum(
((11 * 2 / 3 * 20**2)**2) / (20 + 8 * np.arange(1, 6))**4)))
def test_NOJ_two_turbines_in_row(wdir, x, y):
# Two turbines in a row, North-South
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
windTurbines = NibeA0
wake_model = NOJ(windTurbines)
WS_ilk = np.array([[[8.1]], [[8.1]], [[8.1]]])
TI_ilk = np.zeros_like(WS_ilk)
site = UniformSite([1], 0.1)
dw_iil, cw_iil, dh_iil, dw_order_indices_l = site.wt2wt_distances(x_i=x, y_i=y, h_i=[50, 50, 50], wd_il=[[wdir]])
WS_eff_ilk = wake_model.calc_wake(WS_ilk, TI_ilk, dw_iil, cw_iil, dh_iil, dw_order_indices_l, [0, 0, 0])[0]
ws_wt3 = 8.1 - np.sqrt((8.1 * 2 / 3 * (20 / 26)**2)**2 + (8.1 * 2 / 3 * (20 / 30)**2)**2)
npt.assert_array_almost_equal(WS_eff_ilk[:, 0, 0], [8.1, 4.35, ws_wt3])
def test_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = NOJ(site, windTurbines)
aep = AEPCalculator(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])
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z) # , np.arange(2, 10, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
plt.show()
ref = [
3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3,
8.3, 8.3, 8.3, 8.3, 8.3
]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_NOJ_6_turbines_in_row():
n_wt = 6
x = [0] * n_wt
y = - np.arange(n_wt) * 40 * 2
wake_model = NOJ(NibeA0)
site = UniformSite([1], .1)
WD_ilk, WS_ilk, _, _ = site.local_wind(x_i=x, y_i=y, wd=[0], ws=[11])
TI_ilk = np.zeros_like(WS_ilk)
site = UniformSite([1], 0.1)
dw_iil, cw_iil, dh_iil, dw_order_indices_l = site.wt2wt_distances(
x_i=x, y_i=y, h_i=[50] * n_wt, wd_il=WD_ilk.mean(2))
WS_eff_ilk = wake_model.calc_wake(WS_ilk, TI_ilk, dw_iil, cw_iil, dh_iil, dw_order_indices_l, [0] * n_wt)[0]
np.testing.assert_array_almost_equal(
WS_eff_ilk[1:, 0, 0], 11 - np.sqrt(np.cumsum(((11 * 2 / 3 * 20**2)**2) / (20 + 8 * np.arange(1, 6))**4)))
def test_distance_over_rectangle():
x, y = [-100, 50], [200, -100]
windTurbines = IEA37_WindTurbines()
site = Rectangle(height=200, width=100, distance_resolution=100)
wake_model = NOJ(site, windTurbines)
aep = AEPCalculator(wake_model)
x_j = np.linspace(-100, 500, 100)
y_j = np.linspace(-200, 300, 100)
X, Y, Z = aep.wake_map(x_j, y_j, 110, x, y, wd=[270], ws=[9])
my = np.argmin(np.abs(Y[:, 0] - 200))
my2 = np.argmin(np.abs(Y[:, 0] + 100))
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z, cmap='Blues_r',
levels=100) # , np.arange(2, 10, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
H = site.elevation(X, Y)
plt.plot(X[my], Z[my] * 10, label='wsp*10')
plt.plot(X[my2], Z[my2] * 10, label='wsp*10')
plt.contour(X, Y, H)
plt.plot(X[my, :50:4], Z[my, :50:4] * 10, '.')
plt.plot(x_j, site.elevation(x_j, x_j * 0), label='terrain level')
plt.legend()
plt.show()
ref = [
9., 3.42, 3.8, 6.02, 6.17, 6.31, 6.43, 7.29, 7.35, 7.41, 7.47, 7.53,
7.58
]
npt.assert_array_almost_equal(Z[my, :50:4], ref, 2)
def test_twotype_windturbines():
from py_wake.wind_turbines import WindTurbines
v80 = V80()
def power(ws, types):
power = v80.power(ws)
# add 10% type 1 turbines
power[types == 1] *= 1.1
return power
wts = WindTurbines(names=['V80', 'V88'],
diameters=[80, 88],
hub_heights=[70, 77],
ct_funcs=[v80.ct_funcs[0], v80.ct_funcs[0]],
power_funcs=[v80.power, lambda ws: v80.power(ws) * 1.1],
power_unit='w')
import matplotlib.pyplot as plt
types0 = [0] * 9
types1 = [0, 0, 0, 1, 1, 1, 0, 0, 0]
types2 = [1] * 9
wts.plot(wt9_x, wt9_y, types1)
aep_calculator = AEPCalculator(NOJ(Hornsrev1Site(), wts))
npt.assert_almost_equal(
aep_calculator.calculate_AEP(wt9_x, wt9_y, type_i=types0).sum(),
81.2066072392765)
npt.assert_almost_equal(
aep_calculator.calculate_AEP(wt9_x, wt9_y, type_i=types1).sum(),
83.72420504573488)
npt.assert_almost_equal(
aep_calculator.calculate_AEP(wt9_x, wt9_y, type_i=types2).sum(),
88.87227386796884)
if 0:
plt.show()
def run_NOJ():
windTurbines = IEA37_WindTurbines()
site = IEA37Site(64)
wake_model = NOJ(windTurbines)
aep_calculator = AEPCalculator(site, windTurbines, wake_model)
x, y = site.initial_position.T
print(aep_calculator.calculate_AEP(x, y).sum())
def main():
if __name__ == '__main__':
import matplotlib.pyplot as plt
wt = HornsrevV80()
wt.plot(wt_x, wt_y)
aep_calculator = AEPCalculator(Hornsrev1Site(), wt, NOJ(wt))
print('AEP', aep_calculator.calculate_AEP(wt_x, wt_y)[0].sum())
plt.show()
def test_wake_model():
_, _, freq = read_iea37_windrose(iea37_path + "iea37-windrose.yaml")
site = UniformSite(freq, ti=0.75)
windTurbines = IEA37_WindTurbines(iea37_path + 'iea37-335mw.yaml')
wake_model = NOJ(windTurbines)
aep = AEPCalculator(site, windTurbines, wake_model)
with pytest.raises(ValueError, match="Turbines 0 and 1 are at the same position"):
aep.calculate_AEP([0, 0], [0, 0], wd=np.arange(0, 360, 22.5), ws=[9.8])
def test_smart_start_aep_map():
site = IEA37Site(16)
n_wt = 4
np.random.seed(1)
x, y = site.initial_position[:n_wt].T
wd_lst = np.arange(0, 360, 45)
ws_lst = [10]
turbines = hornsrev1.HornsrevV80()
site = UniformSite([1], .75)
site.default_ws = ws_lst
site.default_wd = wd_lst
aep = PyWakeAEP(site=site, windTurbines=turbines, wake_model=NOJ(turbines))
aep_1wt = aep.calculate_AEP([0], [0]).sum()
tf = TopFarmProblem(design_vars={
'x': x,
'y': y
},
cost_comp=aep.get_TopFarm_cost_component(n_wt),
driver=EasyScipyOptimizeDriver(),
constraints=[
SpacingConstraint(160),
CircleBoundaryConstraint((0, 0), 500)
])
x = np.arange(-500, 500, 10)
y = np.arange(-500, 500, 10)
XX, YY = np.meshgrid(x, y)
tf.smart_start(XX,
YY,
aep.get_aep4smart_start(wd=wd_lst, ws=ws_lst),
radius=40)
tf.evaluate()
if 0:
wt_x, wt_y = tf['x'], tf['y']
for i, _ in enumerate(wt_x, 1):
print(aep.calculate_AEP(wt_x[:i], wt_y[:i]).sum((1, 2)))
X_j, Y_j, aep_map = aep.aep_map(x,
y,
0,
wt_x,
wt_y,
ws=ws_lst,
wd=wd_lst)
print(tf.evaluate())
import matplotlib.pyplot as plt
c = plt.contourf(X_j, Y_j, aep_map, 100)
plt.colorbar(c)
plt.plot(wt_x, wt_y, '2r')
for c in tf.model.constraint_components:
c.plot()
plt.axis('equal')
plt.show()
npt.assert_almost_equal(aep_1wt * n_wt, tf['AEP'], 5)
def test_aep_no_wake_loss_hornsrev():
wt = hornsrev1.V80()
x, y = hornsrev1.wt_x, hornsrev1.wt_y
site = UniformWeibullSite([1], [10], [2], .75)
site.default_ws = np.arange(3, 25)
aep = AEPCalculator(site, wt, NOJ(wt))
npt.assert_almost_equal(aep.calculate_AEP_no_wake_loss(x, y).sum() / 80, 8.260757098)
cap_factor = aep.calculate_AEP(x, y).sum() / aep.calculate_AEP_no_wake_loss(x, y).sum()
# print(cap_factor)
npt.assert_almost_equal(cap_factor, 0.947175839142014)
def test_NOJ_Nibe_result_wake_map():
# Replicate result from: Jensen, Niels Otto. "A note on wind generator interaction." (1983).
def ct_func(_):
return 8 / 9
def power_func(*_):
return 0
windTurbines = NibeA0
wake_model = NOJ(windTurbines)
site = UniformSite([1], 0.1)
aep = AEPCalculator(site, windTurbines, wake_model)
_, _, WS_eff_yx = aep.wake_map(x_j=[0], y_j=[0, -40, -100], h=50, wt_x=[0], wt_y=[0], wd=[0], ws=[8.1])
npt.assert_array_almost_equal(WS_eff_yx[:, 0], [8.1, 4.35, 5.7])
def main():
if __name__ == '__main__':
site = WaspGridSiteBase.from_wasp_grd(ParqueFicticio_path, speedup_using_pickle=False)
site.initial_position = np.array([
[263655.0, 6506601.0],
[263891.1, 6506394.0],
[264022.2, 6506124.0],
[264058.9, 6505891.0],
[264095.6, 6505585.0],
[264022.2, 6505365.0],
[264022.2, 6505145.0],
[263936.5, 6504802.0],
])
windTurbines = IEA37_WindTurbines()
wake_model = NOJ(site, windTurbines)
x, y = site.initial_position.T
aep_calculator = AEPCalculator(wake_model)
print(aep_calculator.calculate_AEP(x, y).sum())
def test_wake_map():
_, _, 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 = NOJ(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])
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z) # , np.arange(2, 10, .01))
plt.colorbar(c)
site.plot_windturbines(x, y)
plt.show()
ref = [3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3, 8.3, 8.3, 8.3, 8.3, 8.3]
npt.assert_array_almost_equal(Z[49, 100:133:2], ref, 2)
def test_wake_map():
site = IEA37Site(16)
x, y = site.initial_position.T
windTurbines = IEA37_WindTurbines()
wake_model = NOJ(site, windTurbines)
aep = AEPCalculator(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])
m = 49, slice(100, 133, 2)
# print(np.round(Z[m], 2).tolist()) # ref
if 0:
import matplotlib.pyplot as plt
c = plt.contourf(X, Y, Z) # , np.arange(2, 10, .01))
plt.colorbar(c)
windTurbines.plot(x, y)
plt.plot(X[m], Y[m], '.-r')
plt.show()
ref = [
3.27, 3.27, 9.0, 7.46, 7.46, 7.46, 7.46, 7.31, 7.31, 7.31, 7.31, 8.3,
8.3, 8.3, 8.3, 8.3, 8.3
]
npt.assert_array_almost_equal(Z[m], ref, 2)
def test_aep_no_wake():
site = UniformWeibullSite([1], [10], [2], .75)
V80 = hornsrev1.V80()
aep = AEPCalculator(NOJ(site, V80))
npt.assert_almost_equal(
aep.calculate_AEP([0], [0], ws=np.arange(3, 25)).sum(), 8.260757098, 9)
def test_wasp_resources_grid_point(site):
# x = np.array([l.split() for l in """0.6010665 -10.02692 32.71442 -6.746912
# 0.5007213 -4.591617 37.10247 -11.0699
# 0.3104101 -1.821247 59.18301 -12.56743
# 0.4674515 16.14293 44.84665 -9.693183
# 0.8710347 5.291974 26.01634 -6.154611
# 0.9998786 -2.777032 15.72486 1.029988
# 0.9079611 -7.882853 16.33216 6.42329
# 0.759553 -5.082487 17.23354 10.18187
# 0.7221162 4.606324 17.96417 11.45838
# 0.8088576 8.196074 16.16308 9.277925
# 0.8800673 3.932325 14.82337 5.380589
# 0.8726974 -3.199536 19.99724 -1.433086""".split("\n")], dtype=np.float)
# for x_ in x.T:
# print(list(x_))
x = [262978]
y = [6504814]
npt.assert_almost_equal(site.elevation(x, y), 227.8, 1)
# Data from WAsP:
# - add turbine (262878,6504814,30)
# - Turbine (right click) - reports - Turbine Site Report full precision
wasp_A = [2.197305, 1.664085, 1.353185, 2.651781, 5.28438, 5.038289,
4.174325, 4.604496, 5.043066, 6.108261, 6.082033, 3.659798]
wasp_k = [1.771484, 2.103516, 2.642578, 2.400391, 2.357422, 2.306641,
2.232422, 2.357422, 2.400391, 2.177734, 1.845703, 1.513672]
wasp_f = [5.188083, 2.509297, 2.869334, 4.966141, 13.16969, 9.514355,
4.80275, 6.038354, 9.828702, 14.44174, 16.60567, 10.0659]
wasp_spd = [0.6010665, 0.5007213, 0.3104101, 0.4674515, 0.8710347, 0.9998786,
0.9079611, 0.759553, 0.7221162, 0.8088576, 0.8800673, 0.8726974]
wasp_trn = [-10.02692, -4.591617, -1.821247, 16.14293, 5.291974, -
2.777032, -7.882853, -5.082487, 4.606324, 8.196074, 3.932325, -3.199536]
wasp_inc = [-6.746912, -11.0699, -12.56743, -9.693183, -6.154611,
1.029988, 6.42329, 10.18187, 11.45838, 9.277925, 5.380589, -1.433086]
wasp_ti = [32.71442, 37.10247, 59.18301, 44.84665, 26.01634, 15.72486,
16.33216, 17.23354, 17.96417, 16.16308, 14.82337, 19.99724]
rho = 1.179558
wasp_u_mean = [1.955629, 1.473854, 1.202513, 2.350761, 4.683075,
4.463644, 3.697135, 4.080554, 4.470596, 5.409509, 5.402648, 3.300305]
wasp_p_air = [9.615095, 3.434769, 1.556282, 12.45899, 99.90289,
88.03519, 51.41135, 66.09097, 85.69466, 164.5592, 193.3779, 56.86945]
wasp_aep = np.array([3725293.0, 33722.71, 0.3093564, 3577990.0, 302099600.0, 188784100.0,
48915640.0, 84636210.0, 189009800.0, 549195100.0, 691258600.0, 120013000.0]) / 1000
wasp_aep_no_density_correction = np.array([3937022.0, 36046.93, 0.33592, 3796496.0, 314595600.0,
196765700.0, 51195440.0, 88451200.0, 197132700.0, 568584400.0, 712938400.0, 124804600.0]) / 1000
wasp_aep_total = 2.181249024
wasp_aep_no_density_correction_total = 2.26224
wt_u = np.array([3.99, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])
wt_p = np.array([0, 55., 185., 369., 619., 941., 1326., 1741., 2133., 2436., 2617., 2702., 2734.,
2744., 2747., 2748., 2748., 2750., 2750., 2750., 2750., 2750., 2750.])
wt_ct = np.array([0, 0.871, 0.853, 0.841, 0.841, 0.833, 0.797, 0.743, 0.635, 0.543, 0.424,
0.324, 0.258, 0.21, 0.175, 0.147, 0.126, 0.109, 0.095, 0.083, 0.074, 0.065, 0.059])
wt = OneTypeWindTurbines.from_tabular(name="NEG-Micon 2750/92 (2750 kW)", diameter=92, hub_height=70,
ws=wt_u, ct=wt_ct, power=wt_p, power_unit='kw')
A_lst, k_lst, f_lst, spd_lst, orog_trn_lst, flow_inc_lst, tke_lst = [site.interp_funcs[n](
(x, y, 30, range(0, 360, 30))) for n in ['A', 'k', 'f', 'spd', 'orog_trn', 'flow_inc', 'tke']]
f_lst = f_lst * 360 / 12
pdf_lst = [lambda x, A=A, k=k: k / A * (x / A)**(k - 1) * np.exp(-(x / A)**k) * (x[1] - x[0])
for A, k in zip(A_lst, k_lst)]
cdf_lst = [lambda x, A=A, k=k: 1 - np.exp(-(x / A) ** k) for A, k in zip(A_lst, k_lst)]
dx = .1
ws = np.arange(dx / 2, 35, dx)
# compare to wasp data
npt.assert_array_equal(A_lst, wasp_A)
npt.assert_array_equal(k_lst, wasp_k)
npt.assert_array_almost_equal(f_lst, np.array(wasp_f) / 100)
npt.assert_array_almost_equal(spd_lst, wasp_spd)
npt.assert_array_almost_equal(orog_trn_lst, wasp_trn)
npt.assert_array_almost_equal(flow_inc_lst, wasp_inc)
npt.assert_array_almost_equal(tke_lst, np.array(wasp_ti) / 100)
# compare pdf, u_mean and aep to wasp
WD, WS, TI, P = site.local_wind(x, np.array(y) + 1e-6, 30, wd=np.arange(0, 360, 30), ws=ws)
P = P / f_lst[na, :, na] # only wind speed probablity (not wdir)
# pdf
for i in range(12):
npt.assert_array_almost_equal(np.interp(ws, WS[0, i], np.cumsum(P[0, i])),
np.cumsum(pdf_lst[i](ws)), 1)
# u_mean
npt.assert_almost_equal([A * math.gamma(1 + 1 / k) for A, k in zip(A_lst, k_lst)], wasp_u_mean, 5)
npt.assert_almost_equal([(pdf(ws) * ws).sum() for pdf in pdf_lst], wasp_u_mean, 5)
npt.assert_almost_equal((P * WS).sum((0, 2)), wasp_u_mean, 5)
# air power
p_air = [(pdf(ws) * 1 / 2 * rho * ws**3).sum() for pdf in pdf_lst]
npt.assert_array_almost_equal(p_air, wasp_p_air, 3)
npt.assert_array_almost_equal((P * 1 / 2 * rho * WS**3).sum((0, 2)), wasp_p_air, 2)
# AEP
AEP_ilk = AEPCalculator(wake_model=NOJ(site, wt)).calculate_AEP_no_wake_loss(
x_i=x, y_i=y, h_i=30, wd=np.arange(0, 360, 30), ws=ws)
if 0:
import matplotlib.pyplot as plt
plt.plot(wasp_aep_no_density_correction / 1000, '.-', label='WAsP')
plt.plot(AEP_ilk.sum((0, 2)) * 1e3, label='PyWake')
plt.xlabel('Sector')
plt.ylabel('AEP [MWh]')
plt.legend()
plt.show()
npt.assert_array_less(np.abs(wasp_aep_no_density_correction - AEP_ilk.sum((0, 2)) * 1e6), 300)
npt.assert_almost_equal(AEP_ilk.sum(), wasp_aep_no_density_correction_total, 3)
def setup_prob(differentiable):
#####################################
## Setup Floris run with gradients ##
#####################################
topfarm.x_key = 'turbineX'
topfarm.y_key = 'turbineY'
turbineX = np.array(
[1164.7, 947.2, 1682.4, 1464.9, 1982.6, 2200.1])
turbineY = np.array(
[1024.7, 1335.3, 1387.2, 1697.8, 2060.3, 1749.7])
f = np.array([
3.597152, 3.948682, 5.167395, 7.000154, 8.364547,
6.43485, 8.643194, 11.77051, 15.15757, 14.73792,
10.01205, 5.165975
])
wind_speed = 8
site = Amalia1Site(f, mean_wsp=wind_speed)
site.initial_position = np.array([turbineX, turbineY]).T
wt = NREL5MWREF()
wake_model = NOJ(site, wt)
aep_calculator = AEPCalculator(wake_model)
n_wt = len(turbineX)
differentiable = differentiable
wake_model_options = {
'nSamples': 0,
'nRotorPoints': 1,
'use_ct_curve': True,
'ct_curve': ct_curve,
'interp_type': 1,
'differentiable': differentiable,
'use_rotor_components': False
}
aep_comp = AEPGroup(
n_wt,
differentiable=differentiable,
use_rotor_components=False,
wake_model=floris_wrapper,
params_IdepVar_func=add_floris_params_IndepVarComps,
wake_model_options=wake_model_options,
datasize=len(power_curve),
nDirections=len(f),
cp_points=len(power_curve)) # , cp_curve_spline=None)
def cost_func(AEP, **kwargs):
return AEP
cost_comp = CostModelComponent(input_keys=[('AEP', [0])],
n_wt=n_wt,
cost_function=cost_func,
output_key="aep",
output_unit="kWh",
objective=True,
income_model=True,
input_units=['kW*h'])
group = TopFarmGroup([aep_comp, cost_comp])
boundary = np.array([(900, 1000), (2300, 1000),
(2300, 2100),
(900, 2100)]) # turbine boundaries
prob = TopFarmProblem(
design_vars={
'turbineX': (turbineX, 'm'),
'turbineY': (turbineY, 'm')
},
cost_comp=group,
driver=EasyRandomSearchDriver(
randomize_func=RandomizeTurbinePosition_Square(),
max_iter=500),
# driver=EasyScipyOptimizeDriver(optimizer='SLSQP',tol=10**-12),
# driver=EasyScipyOptimizeDriver(optimizer='COBYLA'),
constraints=[
SpacingConstraint(200, units='m'),
XYBoundaryConstraint(boundary, units='m')
],
plot_comp=plot_comp,
expected_cost=-100e2,
)
turbineZ = np.array([90.0, 100.0, 90.0, 80.0, 70.0, 90.0])
air_density = 1.1716 # kg/m^3
rotorDiameter = np.zeros(n_wt)
hubHeight = np.zeros(n_wt)
axialInduction = np.zeros(n_wt)
generatorEfficiency = np.zeros(n_wt)
yaw = np.zeros(n_wt)
for turbI in range(0, n_wt):
rotorDiameter[turbI] = wt.diameter() # m
hubHeight[turbI] = wt.hub_height() # m
axialInduction[turbI] = 1.0 / 3.0
generatorEfficiency[turbI] = 1.0 # 0.944
yaw[turbI] = 0. # deg.
prob['turbineX'] = turbineX
prob['turbineY'] = turbineY
prob['hubHeight'] = turbineZ
prob['yaw0'] = yaw
prob['rotorDiameter'] = rotorDiameter
prob['hubHeight'] = hubHeight
prob['axialInduction'] = axialInduction
prob['generatorEfficiency'] = generatorEfficiency
prob['windSpeeds'] = np.ones(len(f)) * wind_speed
prob['air_density'] = air_density
prob['windDirections'] = np.arange(0, 360, 360 / len(f))
prob['windFrequencies'] = f / 100
# turns off cosine spread (just needs to be very large)
prob['model_params:cos_spread'] = 1E12
prob['model_params:shearExp'] = 0.25
prob['model_params:z_ref'] = 80.
prob['model_params:z0'] = 0.
prob['rated_power'] = np.ones(n_wt) * 5000.
prob['cut_in_speed'] = np.ones(n_wt) * 3
prob['cp_curve_wind_speed'] = cp_curve[:, 0]
prob['cp_curve_cp'] = cp_curve[:, 1]
prob['rated_wind_speed'] = np.ones(n_wt) * 11.4
prob['cut_out_speed'] = np.ones(n_wt) * 25.0
# if 0:
# prob.check_partials(compact_print=True,includes='*direction_group0*')
# else:
return prob
def main(obj=False, max_con_on=True):
# ------ DEFINE WIND TURBINE TYPES, LOCATIONS & STORE METADATA -------
windTurbines = WindTurbines(
names=['Ghost_T1', 'T2'],
diameters=[40, 84],
hub_heights=[70, hornsrev1.HornsrevV80().hub_height()],
ct_funcs=[dummy_thrust(ct_rated=0),
hornsrev1.HornsrevV80()._ct],
power_funcs=[cube_power(power_rated=0),
cube_power(power_rated=3000)],
# hornsrev1.HornsrevV80()._power],
power_unit='kW')
Drotor_vector = windTurbines._diameters
power_rated_vec = np.array(
[pcurv(25) / 1000 for pcurv in windTurbines.power_funcs])
hub_height_vector = windTurbines._hub_heights
x, y = np.meshgrid(range(-840, 840, 420), range(-840, 840, 420))
n_wt = len(x.flatten())
# initial turbine positions and other independent variables
ext_vars = {'x': x.flatten(), 'y': y.flatten(), 'obj': obj * 1}
capconst = []
if max_con_on:
capconst = [
CapacityConstraint(max_capacity=30.01,
rated_power_array=power_rated_vec)
]
# ---------------- DEFINE SITE & SELECT WAKE MODEL -------------------
# site = UniformWeibullSite(p_wd=[50, 50], a=[9, 9], k=[2.3, 2.3], ti=.1, alpha=0, h_ref=100)
site = UniformWeibullSite(p_wd=[100], a=[9], k=[2.3], ti=.1)
site.default_ws = [9] # reduce the number of calculations
site.default_wd = [0] # reduce the number of calculations
wake_model = NOJ(site, windTurbines)
AEPCalc = AEPCalculator(wake_model)
# ------------- OUTPUTS AEP PER TURBINE & FARM IRR -------------------
def aep_func(
x, y, type, obj,
**kwargs): # TODO fix type as input change to topfarm turbinetype
out = AEPCalc.calculate_AEP(x_i=x, y_i=y, type_i=type).sum((1, 2))
if obj: # if objective is AEP; output the total Farm_AEP
out = np.sum(out)
return out * 10**6
def irr_func(aep, type, **kwargs):
idx = type.astype(int)
return economic_evaluation(Drotor_vector[idx], power_rated_vec[idx],
hub_height_vector[idx],
aep).calculate_irr()
# ----- WRAP AEP AND IRR INTO TOPFARM COMPONENTS AND THEN GROUP -----
aep_comp = CostModelComponent(input_keys=[
topfarm.x_key, topfarm.y_key, topfarm.type_key, ('obj', obj)
],
n_wt=n_wt,
cost_function=aep_func,
output_key="aep",
output_unit="GWh",
objective=obj,
output_val=np.zeros(n_wt),
income_model=True)
comps = [aep_comp] # AEP component is always in the group
if not obj: # if objective is IRR initiate/add irr_comp
irr_comp = CostModelComponent(input_keys=[topfarm.type_key, 'aep'],
n_wt=n_wt,
cost_function=irr_func,
output_key="irr",
output_unit="%",
objective=True)
comps.append(irr_comp)
group = TopFarmGroup(comps)
# - INITIATE THE PROBLEM WITH ONLY TURBINE TYPE AS DESIGN VARIABLES -
tf = TopFarmProblem(
design_vars={
topfarm.type_key: ([0] * n_wt, 0, len(windTurbines._names) - 1)
},
cost_comp=group,
driver=EasyRandomSearchDriver(randomize_func=RandomizeAllUniform(
[topfarm.type_key]),
max_iter=1),
# driver=EasySimpleGADriver(max_gen=2, random_state=1),
constraints=capconst,
# plot_comp=TurbineTypePlotComponent(windTurbines._names),
plot_comp=NoPlot(),
ext_vars=ext_vars)
cost, state, rec = tf.optimize()
# view_model(problem, outfile='ex5_n2.html', show_browser=False)
end = time.time()
print(end - start)
# %%
# ------------------- OPTIONAL VISUALIZATION OF WAKES ----------------
post_visual, save = False, False
if post_visual:
# import matplotlib.pyplot as plt
for cou, (i, j, k, co, ae) in enumerate(
zip(rec['x'], rec['y'], rec['type'], rec['cost'], rec['aep'])):
AEPCalc.calculate_AEP(x_i=i, y_i=j, type_i=k)
AEPCalc.plot_wake_map(wt_x=i,
wt_y=j,
wt_type=k,
wd=site.default_wd[0],
ws=site.default_ws[0],
levels=np.arange(2.5, 12, .1))
windTurbines.plot(i, j, types=k)
title = f'IRR: {-np.round(co,2)} %, AEP : {round(np.sum(ae))} GWh, '
if "totalcapacity" in rec.keys():
title += f'Total Capacity: {rec["totalcapacity"][cou]} MW'
plt.title(title)
if save:
plt.savefig(
r'..\..\..\ima2\obj_AEP_{}_MaxConstraint_{}_{}.png'.format(
obj, max_con_on, cou))
plt.show()