"""Example: Optimize yaw for a single wind speed and multiple wind directions.
Compare certain and uncertain results.
Use the serial-refine method to optimize the yaw angles for a 3-turbine wind farm. In one
case use the FlorisModel without uncertainty and in the other use the UncertainFlorisModel
with a wind direction standard deviation of 3 degrees. Compare the results.
"""
import matplotlib.pyplot as plt
import numpy as np
from floris import (
FlorisModel,
TimeSeries,
UncertainFlorisModel,
)
from floris.optimization.yaw_optimization.yaw_optimizer_sr import YawOptimizationSR
# Load the floris model and uncertain floris model
fmodel = FlorisModel("../inputs/gch.yaml")
ufmodel = UncertainFlorisModel("../inputs/gch.yaml", wd_std=3)
# Define an inflow that
# keeps wind speed and TI constant while sweeping the wind directions
wind_directions = np.arange(250, 290.0, 1.0)
time_series = TimeSeries(
wind_directions=wind_directions,
wind_speeds=8.0,
turbulence_intensities=0.06,
)
# Reinitialize as a 3-turbine using the above inflow
D = 126.0 # Rotor diameter for the NREL 5 MW
fmodel.set(
layout_x=[0.0, 5 * D, 10 * D],
layout_y=[0.0, 0.0, 0.0],
wind_data=time_series,
)
ufmodel.set(
layout_x=[0.0, 5 * D, 10 * D],
layout_y=[0.0, 0.0, 0.0],
wind_data=time_series,
)
# Initialize optimizer object and run optimization using the Serial-Refine method
print("++++++++++CERTAIN++++++++++++")
yaw_opt = YawOptimizationSR(fmodel)
df_opt = yaw_opt.optimize()
# Repeat with uncertain model
print("++++++++++UNCERTAIN++++++++++++")
yaw_opt_u = YawOptimizationSR(ufmodel)
df_opt_uncertain = yaw_opt_u.optimize()
# Split out the turbine results
for t in range(3):
df_opt["t%d" % t] = df_opt.yaw_angles_opt.apply(lambda x: x[t])
df_opt_uncertain["t%d" % t] = df_opt_uncertain.yaw_angles_opt.apply(lambda x: x[t])
# Show the yaw and turbine results
fig, axarr = plt.subplots(3, sharex=True, sharey=False, figsize=(15, 8))
# Yaw results
for tindex in range(3):
ax = axarr[tindex]
ax.plot(
df_opt.wind_direction, df_opt["t%d" % tindex], label="FlorisModel", color="k", marker="o"
)
ax.plot(
df_opt_uncertain.wind_direction,
df_opt_uncertain["t%d" % tindex],
label="UncertainFlorisModel",
color="r",
marker="x",
)
ax.set_ylabel("Yaw Offset (deg")
ax.legend()
ax.grid(True)
# Power results
fig, axarr = plt.subplots(1, 2, figsize=(15, 5), sharex=True, sharey=True)
ax = axarr[0]
ax.plot(df_opt.wind_direction, df_opt.farm_power_baseline, color="k", label="Baseline Farm Power")
ax.plot(df_opt.wind_direction, df_opt.farm_power_opt, color="r", label="Optimized Farm Power")
ax.set_ylabel("Power (W)")
ax.set_xlabel("Wind Direction (deg)")
ax.legend()
ax.grid(True)
ax.set_title("Certain")
ax = axarr[1]
ax.plot(
df_opt_uncertain.wind_direction,
df_opt_uncertain.farm_power_baseline,
color="k",
label="Baseline Farm Power",
)
ax.plot(
df_opt_uncertain.wind_direction,
df_opt_uncertain.farm_power_opt,
color="r",
label="Optimized Farm Power",
)
ax.set_xlabel("Wind Direction (deg)")
ax.grid(True)
ax.set_title("Uncertain")
plt.show()
import warnings
warnings.filterwarnings('ignore')
