def HornsrevAEP_FUSEDWake_GCL():
# wind parameters from "Horns Rev 1\hornsrev2.lib
wdir_freq, weibull_A, weibull_k = read_lib(fuga_path + 'LUT/Farms/Horns Rev 1\hornsrev2.lib')
ti = np.zeros_like(wdir_freq) + .1
wr = WindResource(wdir_freq, weibull_A, weibull_k, ti)
hornsrev_yml = os.path.dirname(fusedwake.__file__) + "/../examples/hornsrev.yml"
wm = FusedWakeGCLWakeModel(hornsrev_yml)
aep_calc = AEPCalculator(wr, wm)
return aep_calc(wm.windFarm.pos.T)
def aep_calc():
# f, A, k = read_lib(fuga_path + 'LUT/Farms/Horns Rev 1\hornsrev_north_only_pm45.lib')
f = [1.0, 0.0, 0.0, 0.0]
A = [9.176929, 9.782334, 9.531809, 9.909545]
k = [2.392578, 2.447266, 2.412109, 2.591797]
wr = WindResource(f, A, k, ti=np.zeros_like(f) + .1)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
wm = FusedWakeGCLWakeModel(tfp + "wind_farms/3tb.yml")
return AEPCalculator(wr, wm)
def aep_calc():
# f, A, k = read_lib(fuga_path + 'LUT/Farms/Horns Rev 1\hornsrev_north_only_pm45.lib')
f = [1.0, 0.0, 0.0, 0.0]
A = [9.176929, 9.782334, 9.531809, 9.909545]
k = [2.392578, 2.447266, 2.412109, 2.591797]
wr = WindResource(f, A, k, ti=np.zeros_like(f) + .1)
with warnings.catch_warnings(
): # suppress "warning, make sure that this position array is oriented in ndarray([n_wt, 2]) or ndarray([n_wt, 3])"
warnings.simplefilter("ignore")
wm = FusedWakeNOJWakeModel(tfp + "wind_farms/3tb.yml")
return AEPCalculator(wr, wm)
def test_AEPCalculator():
f = [1, 0, 0, 0]
A = [9.176929, 10, 10, 10]
k = [2.392578, 2, 2, 2]
wr = WindResource(np.array(f), A, k, ti=np.zeros_like(f) + .1)
wf_3tb = testfilepath + "wind_farms/3tb.yml"
with warnings.catch_warnings():
warnings.simplefilter("ignore")
wm = FusedWakeGCLWakeModel(wf_3tb)
aep_calc = AEPCalculator(wr, wm)
npt.assert_almost_equal(aep_calc([-1600, 0, 1600], [0, 0, 0]),
22.3178800761)
def main():
if __name__ == '__main__':
f = [0.035972, 0.039487, 0.051674, 0.070002, 0.083645, 0.064348, 0.086432, 0.117705, 0.151576, 0.147379, 0.10012, 0.05166]
A = [9.176929, 9.782334, 9.531809, 9.909545, 10.04269, 9.593921, 9.584007, 10.51499, 11.39895, 11.68746, 11.63732, 10.08803]
k = [2.392578, 2.447266, 2.412109, 2.591797, 2.755859, 2.595703, 2.583984, 2.548828, 2.470703, 2.607422, 2.626953, 2.326172]
wr = WindResource(np.array(f) / 100, A, k, ti=np.zeros_like(f) + .1)
wf_3tb = testfilepath + "wind_farms/3tb.yml"
with warnings.catch_warnings():
warnings.filterwarnings('ignore') # temporarily disable fusedwake warnings
wm = FusedWakeGCLWakeModel(wf_3tb)
aep_calc = AEPCalculator(wr, wm)
print(aep_calc(wm.windFarm.pos[:, 0], wm.windFarm.pos[:, 1]))
def test_WindResource():
f = [
0.035972, 0.039487, 0.051674, 0.070002, 0.083645, 0.064348, 0.086432,
0.117705, 0.151576, 0.147379, 0.10012, 0.05166
]
A = [
9.176929, 9.782334, 9.531809, 9.909545, 10.04269, 9.593921, 9.584007,
10.51499, 11.39895, 11.68746, 11.63732, 10.08803
]
k = [
2.392578, 2.447266, 2.412109, 2.591797, 2.755859, 2.595703, 2.583984,
2.548828, 2.470703, 2.607422, 2.626953, 2.326172
]
ti = np.zeros_like(f) + .1
wr = WindResource(f, A, k, ti)
wdir, ws, ti, weight = wr([0], [0], [4, 5])
npt.assert_array_almost_equal(
weight,
np.array([[0.071381703], [0.088361194]]) * 0.035972 * (12 / 360))
def optimize_AEP_FusedWake_GCL():
plot_comp = PlotComp()
f, A, k = read_lib(fuga_path +
'LUT/Farms/Horns Rev 1\hornsrev_north_only_pm45.lib')
wr = WindResource(f, A, k, ti=np.zeros_like(f) + .1)
wm = FusedWakeGCLWakeModel(os.path.dirname(__file__) + "/3tb.yml")
aep_calc = AEPCalculator(wr, wm)
init_pos = initial_position.copy()
init_pos[:, 0] += [-20, 0, 20]
tf = TopFarm(init_pos,
aep_calc.get_TopFarm_cost_component(),
minSpacing * D,
boundary=boundary,
plot_comp=plot_comp,
driver_options={'optimizer': 'SLSQP'})
tf.evaluate()
print(tf.get_cost())
tf.optimize()
print(tf.get_cost())
save_plot('final_gcl.png', tf, False)
plot_comp.show()
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
# ------------------------ INPUTS ------------------------
# paths to input files
test_files_dir = os.path.dirname(
test_files.__file__) + "/" # file locations
wf_path = test_files_dir + 'wind_farms/3tb.yml' # path to wind farm
# ------------------------ DEFINE WIND RESOURCE ------------------------
# wind resource info (wdir frequency, weibull a and k)
f = [
3.597152, 3.948682, 5.167395, 7.000154, 8.364547, 6.43485,
8.643194, 11.77051, 15.15757, 14.73792, 10.01205, 5.165975
]
a = [
9.176929, 9.782334, 9.531809, 9.909545, 10.04269, 9.593921,
9.584007, 10.51499, 11.39895, 11.68746, 11.63732, 10.08803
]
k = [
2.392578, 2.447266, 2.412109, 2.591797, 2.755859, 2.595703,
2.583984, 2.548828, 2.470703, 2.607422, 2.626953, 2.326172
]
wind_res = WindResource(f, a, k, np.zeros_like(k))
# ------------------------ setup problem ____---------------------------
rot_diam = 80.0 # rotor diameter [m]
init_pos = np.array([(0, 2 * rot_diam), (0, 0),
(0, -2 * rot_diam)]) # initial turbine positions
b = 2 * rot_diam + 10 # boundary size
boundary = [(-b, -b), (-b, b), (b, b),
(b, -b)] # corners of wind farm boundary
min_spacing = 2.0 * rot_diam # minimum spacing between turbines [m]
# ------------------------ OPTIMIZATION ------------------------
def get_tf(wake_model):
return TopFarmProblem(design_vars=dict(zip('xy', init_pos.T)),
cost_comp=AEPCalculator(
wind_res,
wake_model,
wdir=np.arange(
0, 360,
12)).get_TopFarm_cost_component(),
constraints=[
SpacingConstraint(min_spacing),
XYBoundaryConstraint(boundary)
],
driver=EasyScipyOptimizeDriver(),
plot_comp=plot_comp)
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore') # temporarily disable fusedwake warnings
# GCL: define the wake model and optimization problem
wake_mod_gcl = FusedWakeGCLWakeModel(wf_path)
tf_gcl = get_tf(wake_mod_gcl)
# NOJ: define the wake model and optimization problem
wake_mod_noj = FusedWakeNOJWakeModel(wf_path)
tf_noj = get_tf(wake_mod_noj)
# run the optimization
cost_gcl, state_gcl, recorder_gcl = tf_gcl.optimize()
cost_noj, state_noj, recorder_noj = tf_noj.optimize()
# ------------------------ POST-PROCESS ------------------------
# get the optimized locations
opt_gcl = tf_gcl.turbine_positions
opt_noj = tf_noj.turbine_positions
# create the array of costs for easier printing
costs = np.diag([cost_gcl, cost_noj])
costs[0, 1] = tf_noj.evaluate(state_gcl)[0] # noj cost of gcl locs
costs[1, 0] = tf_gcl.evaluate(state_noj)[0] # gcl cost of noj locs
# ------------------------ PRINT STATS ------------------------
aep_diffs = 200 * (costs[:, 0] - costs[:, 1]) / (costs[:, 0] +
costs[:, 1])
loc_diffs = 200 * (costs[0, :] - costs[1, :]) / (costs[0, :] +
costs[1, :])
print('\nComparison of cost models vs. optimized locations:')
print('\nCost | GCL_aep NOJ_aep')
print('---------------------------------')
print(f'GCL_loc |{costs[0,0]:11.2f} {costs[0,1]:11.2f}' +
f' ({aep_diffs[0]:.2f}%)')
print(f'NOJ_loc |{costs[1,0]:11.2f} {costs[1,1]:11.2f}' +
f' ({aep_diffs[1]:.2f}%)')
print(f' ({loc_diffs[0]:.2f}%) ({loc_diffs[1]:.2f}%)')
# ------------------------ PLOT (if possible) ------------------------
if plot:
# initialize the figure and axes
fig = plt.figure(1, figsize=(7, 5))
plt.clf()
ax = plt.axes()
# plot the boundary and desired locations
ax.add_patch(Polygon(boundary, fill=False,
label='Boundary')) # boundary
ax.plot(init_pos[:, 0], init_pos[:, 1], 'xk', label='Initial')
ax.plot(opt_gcl[:, 0], opt_gcl[:, 1], 'o', label='GCL')
ax.plot(opt_noj[:, 0], opt_noj[:, 1], '^', label='NOJ')
# make a few adjustments to the plot
ax.autoscale_view() # autoscale the boundary
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=4,
mode='expand',
borderaxespad=0.) # add a legend
plt.tight_layout() # zoom the plot in
plt.axis('off') # remove the axis
# save the png
folder, file = os.path.split(__file__)
fig.savefig(folder + "/figures/" + file.replace('.py', '.png'))