Bug fix: Scipy layout optimization boundary constraints are violated

[bayc]

Feature or improvement description
This PR fixes the issue of boundary constraints not being respected with the Scipy layout optimization tool.

Related issue, if one exists
Closes #575

Impacted areas of the software
layout_optimization_scipy.py

Additional supporting information
This problem was originally identified in discussion #557 by @Rodrig03.

Test results, if applicable
Using the below script, all turbine optimized locations are within the defined boundary.

import os
import numpy as np

from floris.tools import FlorisInterface

from floris.tools.optimization.layout_optimization.layout_optimization_scipy import LayoutOptimizationScipy

# Initialize the FLORIS interface fi
file_dir = os.path.dirname(os.path.abspath(__file__))
fi = FlorisInterface('inputs/gch.yaml')

# Setup 72 wind directions with a random wind speed and frequency distribution
wind_directions = np.arange(0, 360.0, 5.0)
np.random.seed(1)
wind_speeds = 8.0 + np.random.randn(1) * 0.5
# Shape frequency distribution to match number of wind directions and wind speeds
freq = np.abs(np.sort(np.random.randn(len(wind_directions)))).reshape((len(wind_directions), len(wind_speeds)))
freq = freq / freq.sum()

fi.reinitialize(wind_directions=wind_directions, wind_speeds=wind_speeds)

# The boundaries for the turbines, specified as vertices
boundaries = [(0, 0), (0, 1600), (1000, 1600), (1600, 1200), (1600, 0), (0, 0)]

# Set turbine locations to 4 turbines in a rectangle
D = 126.0 # rotor diameter for the NREL 5MW
layout_x = [0, 0, 6 * D, 6 * D]
layout_y = [0, 4 * D, 0, 4 * D]
fi.reinitialize(layout_x=layout_x, layout_y=layout_y)

# Setup the optimization problem
layout_opt = LayoutOptimizationScipy(fi, boundaries, freq=freq)

# Run the optimization
sol = layout_opt.optimize()

# Get the resulting improvement in AEP
print('... calcuating improvement in AEP')
fi.calculate_wake()
base_aep = fi.get_farm_AEP(freq=freq) / 1e6
fi.reinitialize(layout_x=sol[0], layout_y=sol[1])
fi.calculate_wake()
opt_aep = fi.get_farm_AEP(freq=freq) / 1e6
percent_gain = 100 * (opt_aep - base_aep) / base_aep

# Print and plot the results
print('Optimal layout: ', sol)
print('Optimal layout improves AEP by %.1f%% from %.1f MWh to %.1f MWh' % (percent_gain, base_aep, opt_aep))
layout_opt.plot_layout_opt_results()

[rafmudaf]

Well done documenting this so well @bayc!

[paulf81]

This looks good to be me too, thanks @bayc !