"""Example 6: Getting Expected Power and AEP
The expected power of a farm is computed by multiplying the power output of the farm by the
frequency of each findex. This is done by the `get_expected_farm_power` method. The expected
AEP is annual energy production is computed by multiplying the expected power by the number of
hours in a year.
If a wind_data object is provided to the model, the expected power and AEP
can be computed directly by the`get_farm_AEP_with_wind_data` using the frequency table
of the wind data object. If not, a frequency table must be passed into these functions
"""
import numpy as np
import pandas as pd
from floris import (
FlorisModel,
TimeSeries,
WindRose,
)
fmodel = FlorisModel("inputs/gch.yaml")
# Set to a 3-turbine layout
D = 126.
fmodel.set(layout_x=[0.0, 5 * D, 10 * D],
layout_y=[0.0, 0.0, 0.0])
# Using TimeSeries
# Randomly generated a time series with time steps = 365 * 24
N = 365 * 24
wind_directions = np.random.uniform(0, 360, N)
wind_speeds = np.random.uniform(5, 25, N)
# Set up a time series
time_series = TimeSeries(
wind_directions=wind_directions, wind_speeds=wind_speeds, turbulence_intensities=0.06
)
# Set the wind data
fmodel.set(wind_data=time_series)
# Run the model
fmodel.run()
expected_farm_power = fmodel.get_expected_farm_power()
aep = fmodel.get_farm_AEP()
# Note this is equivalent to the following
aep_b = fmodel.get_farm_AEP(freq=time_series.unpack_freq())
print(f"AEP from time series: {aep}, and re-computed AEP: {aep_b}")
# Using WindRose==============================================
# Load the wind rose from csv as in example 003
wind_rose = WindRose.read_csv_long(
"inputs/wind_rose.csv", wd_col="wd", ws_col="ws", freq_col="freq_val", ti_col_or_value=0.06
)
# Store some values
n_wd = len(wind_rose.wind_directions)
n_ws = len(wind_rose.wind_speeds)
# Store the number of elements of the freq_table which are 0
n_zeros = np.sum(wind_rose.freq_table == 0)
# Set the wind rose
fmodel.set(wind_data=wind_rose)
# Run the model
fmodel.run()
# Note that the frequency table contains 0 frequency for some wind directions and wind speeds
# and we've not selected to compute 0 frequency bins, therefore the n_findex will be less than
# the total number of wind directions and wind speed combinations
print(f"Total number of wind direction and wind speed combination: {n_wd * n_ws}")
print(f"Number of 0 frequency bins: {n_zeros}")
print(f"n_findex: {fmodel.n_findex}")
# Get the AEP
aep = fmodel.get_farm_AEP()
# Print the AEP
print(f"AEP from wind rose: {aep/1E9:.3f} (GWh)")
# Run the model again, without wakes, and use the result to compute the wake losses
fmodel.run_no_wake()
# Get the AEP without wake
aep_no_wake = fmodel.get_farm_AEP()
# Compute the wake losses
wake_losses = 100 * (aep_no_wake - aep) / aep_no_wake
# Print the wake losses
print(f"Wake losses: {wake_losses:.2f}%")
import warnings
warnings.filterwarnings('ignore')
