Example: Wind Data Comparisons

In this example, a random time series of wind speeds, wind directions, turbulenceintensities, and values is generated. Value represents the value of the powergenerated at each time step or wind condition (e.g., the price of electricity). Thiscan then be used in later optimization methods to optimize for total value instead ofenergy. This time series is then used to instantiate a TimeSeries object. The TimeSeriesobject is then used to instantiate a WindRose object and WindTIRose object based on thesame data. The three objects are then each used to drive a FLORIS model of a simpletwo-turbine wind farm. The annual energy production (AEP) and annual value production(AVP) outputs are then compared and printed to the console.

import matplotlib.pyplot as plt
import numpy as np

from floris import (
    FlorisModel,
    TimeSeries,
    WindRose,
)
from floris.utilities import wrap_360


# Generate a random time series of wind speeds, wind directions, turbulence
# intensities, and values. In this case let's treat value as the dollars per MWh.
N = 500
wd_array = wrap_360(270 * np.ones(N) + np.random.randn(N) * 20)
ws_array = np.clip(8 * np.ones(N) + np.random.randn(N) * 8, 3, 50)
ti_array = np.clip(0.1 * np.ones(N) + np.random.randn(N) * 0.05, 0, 0.25)
value_array = np.clip(25 * np.ones(N) + np.random.randn(N) * 10, 0, 100)

fig, axarr = plt.subplots(4, 1, sharex=True, figsize=(7, 6))
ax = axarr[0]
ax.plot(wd_array, marker=".", ls="None")
ax.set_ylabel("Wind Direction")
ax = axarr[1]
ax.plot(ws_array, marker=".", ls="None")
ax.set_ylabel("Wind Speed")
ax = axarr[2]
ax.plot(ti_array, marker=".", ls="None")
ax.set_ylabel("Turbulence Intensity")
ax = axarr[3]
ax.plot(value_array, marker=".", ls="None")
ax.set_ylabel("Value")


# Build the time series
time_series = TimeSeries(wd_array, ws_array, turbulence_intensities=ti_array, values=value_array)

# Now build the wind rose
wind_rose = time_series.to_WindRose()

# Plot the wind rose
fig, ax = plt.subplots(subplot_kw={"polar": True})
wind_rose.plot_wind_rose(ax=ax,legend_kwargs={"title": "WS"})
fig.suptitle("WindRose Plot")

# Now build a wind rose with turbulence intensity
wind_ti_rose = time_series.to_WindTIRose()

# Plot the wind rose with TI
fig, axs = plt.subplots(2, 1, figsize=(6,8), subplot_kw={"polar": True})
wind_ti_rose.plot_wind_rose(ax=axs[0], wind_rose_var="ws",legend_kwargs={"title": "WS"})
axs[0].set_title("Wind Direction and Wind Speed Frequencies")
wind_ti_rose.plot_wind_rose(ax=axs[1], wind_rose_var="ti",legend_kwargs={"title": "TI"})
axs[1].set_title("Wind Direction and Turbulence Intensity Frequencies")
fig.suptitle("WindTIRose Plots")
plt.tight_layout()

# Now set up a FLORIS model and initialize it using the time series and wind rose
fmodel = FlorisModel("../inputs/gch.yaml")
fmodel.set(layout_x=[0, 500.0], layout_y=[0.0, 0.0])

fmodel_time_series = fmodel.copy()
fmodel_wind_rose = fmodel.copy()
fmodel_wind_ti_rose = fmodel.copy()

fmodel_time_series.set(wind_data=time_series)
fmodel_wind_rose.set(wind_data=wind_rose)
fmodel_wind_ti_rose.set(wind_data=wind_ti_rose)

fmodel_time_series.run()
fmodel_wind_rose.run()
fmodel_wind_ti_rose.run()

# Now, compute AEP using the FLORIS models initialized with the three types of
# WindData objects. The AEP values are very similar but not exactly the same
# because of the effects of binning in the wind roses.

time_series_aep = fmodel_time_series.get_farm_AEP()
wind_rose_aep = fmodel_wind_rose.get_farm_AEP()
wind_ti_rose_aep = fmodel_wind_ti_rose.get_farm_AEP()

print(f"AEP from TimeSeries {time_series_aep / 1e9:.2f} GWh")
print(f"AEP from WindRose {wind_rose_aep / 1e9:.2f} GWh")
print(f"AEP from WindTIRose {wind_ti_rose_aep / 1e9:.2f} GWh")

# Now, compute annual value production (AVP) using the FLORIS models initialized
# with the three types of WindData objects. The AVP values are very similar but
# not exactly the same because of the effects of binning in the wind roses.

time_series_avp = fmodel_time_series.get_farm_AVP()
wind_rose_avp = fmodel_wind_rose.get_farm_AVP()
wind_ti_rose_avp = fmodel_wind_ti_rose.get_farm_AVP()

print(f"Annual Value Production (AVP) from TimeSeries {time_series_avp / 1e6:.2f} dollars")
print(f"AVP from WindRose {wind_rose_avp / 1e6:.2f} dollars")
print(f"AVP from WindTIRose {wind_ti_rose_avp / 1e6:.2f} dollars")

plt.show()
import warnings
warnings.filterwarnings('ignore')

floris.floris_model.FlorisModel WARNING Computing AEP with uniform frequencies. Results results may not reflect annual operation.

floris.floris_model.FlorisModel WARNING Computing AVP with uniform frequencies. Results results may not reflect annual operation.

AEP from TimeSeries 39.44 GWh
AEP from WindRose 39.76 GWh
AEP from WindTIRose 39.75 GWh
Annual Value Production (AVP) from TimeSeries 1010600.47 dollars
AVP from WindRose 1017825.20 dollars
AVP from WindTIRose 1017319.46 dollars