import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import minimize
from scipy.spatial.distance import cdist
from shapely.geometry import Point
from .layout_optimization_base import LayoutOptimization, list_depth
[docs]
class LayoutOptimizationScipy(LayoutOptimization):
"""
This class provides an interface for optimizing the layout of wind turbines
using the Scipy optimization library. The optimization objective is to
maximize annual energy production (AEP) or annual value production (AVP).
"""
def __init__(
self,
fmodel,
boundaries,
bnds=None,
min_dist=None,
solver='SLSQP',
optOptions=None,
enable_geometric_yaw=False,
use_value=False,
):
"""
Args:
fmodel (FlorisModel): A FlorisModel object.
boundaries (iterable(float, float)): Pairs of x- and y-coordinates
that represent the boundary's vertices (m).
bnds (iterable, optional): Bounds for the optimization
variables (pairs of min/max values for each variable (m)). If
none are specified, they are set to 0 and 1. Defaults to None.
min_dist (float, optional): The minimum distance to be maintained
between turbines during the optimization (m). If not specified,
initializes to 2 rotor diameters. Defaults to None.
solver (str, optional): Sets the solver used by Scipy. Defaults to 'SLSQP'.
optOptions (dict, optional): Dictionary for setting the
optimization options. Defaults to None.
enable_geometric_yaw (bool, optional): If True, enables geometric yaw
optimization. Defaults to False.
use_value (bool, optional): If True, the layout optimization objective
is to maximize annual value production using the value array in the
FLORIS model's WindData object. If False, the optimization
objective is to maximize AEP. Defaults to False.
"""
if list_depth(boundaries) > 1 and hasattr(boundaries[0][0], "__len__"):
raise NotImplementedError(
"LayoutOptimizationScipy is not configured for multiple regions."
)
super().__init__(
fmodel,
boundaries,
min_dist=min_dist,
enable_geometric_yaw=enable_geometric_yaw,
use_value=use_value
)
self.boundaries_norm = [
[
self._norm(val[0], self.xmin, self.xmax),
self._norm(val[1], self.ymin, self.ymax),
]
for val in self.boundaries
]
self.x0 = [
self._norm(x, self.xmin, self.xmax)
for x in self.fmodel.layout_x
] + [
self._norm(y, self.ymin, self.ymax)
for y in self.fmodel.layout_y
]
if bnds is not None:
self.bnds = bnds
else:
self._set_opt_bounds()
if solver is not None:
self.solver = solver
default_optOptions = {"maxiter": 100, "disp": True, "iprint": 2, "ftol": 1e-9, "eps":0.01}
if optOptions is not None:
self.optOptions = {**default_optOptions, **optOptions}
else:
self.optOptions = default_optOptions
self._generate_constraints()
# Private methods
def _optimize(self):
self._num_aep_calls = 0
self._aep_record = []
self.residual_plant = minimize(
self._obj_func,
self.x0,
method=self.solver,
bounds=self.bnds,
constraints=self.cons,
options=self.optOptions,
)
return self.residual_plant.x
def _obj_func(self, locs):
locs_unnorm = [
self._unnorm(valx, self.xmin, self.xmax)
for valx in locs[0 : self.nturbs]
] + [
self._unnorm(valy, self.ymin, self.ymax)
for valy in locs[self.nturbs : 2 * self.nturbs]
]
self._change_coordinates(locs_unnorm)
# Compute turbine yaw angles using PJ's geometric code (if enabled)
yaw_angles = self._get_geoyaw_angles()
self.fmodel.set_operation(yaw_angles=yaw_angles)
self.fmodel.run()
self._num_aep_calls += 1
if self.use_value:
val = -1 * self.fmodel.get_farm_AVP() / self.initial_AEP_or_AVP
self._aep_record.append(val)
return val
else:
aep = -1 * self.fmodel.get_farm_AEP() / self.initial_AEP_or_AVP
self._aep_record.append(aep)
return aep
def _change_coordinates(self, locs):
# Parse the layout coordinates
layout_x = locs[0 : self.nturbs]
layout_y = locs[self.nturbs : 2 * self.nturbs]
# Store on object for use in geoyaw code
self.x = layout_x
self.y = layout_y
# Update the turbine map in floris
self.fmodel.set(layout_x=layout_x, layout_y=layout_y)
def _generate_constraints(self):
tmp1 = {
"type": "ineq",
"fun": lambda x, *args: self._space_constraint(x),
}
tmp2 = {
"type": "ineq",
"fun": lambda x: self._distance_from_boundaries(x),
}
self.cons = [tmp1, tmp2]
def _set_opt_bounds(self):
self.bnds = [(0.0, 1.0) for _ in range(2 * self.nturbs)]
def _space_constraint(self, x_in, rho=500):
x = [
self._unnorm(valx, self.xmin, self.xmax)
for valx in x_in[0 : self.nturbs]
]
y = [
self._unnorm(valy, self.ymin, self.ymax)
for valy in x_in[self.nturbs : 2 * self.nturbs]
]
# Calculate distances between turbines
locs = np.vstack((x, y)).T
distances = cdist(locs, locs)
arange = np.arange(distances.shape[0])
distances[arange, arange] = 1e10
dist = np.min(distances, axis=0)
g = 1 - np.array(dist) / self.min_dist
# Following code copied from OpenMDAO KSComp().
# Constraint is satisfied when KS_constraint <= 0
g_max = np.max(np.atleast_2d(g), axis=-1)[:, np.newaxis]
g_diff = g - g_max
exponents = np.exp(rho * g_diff)
summation = np.sum(exponents, axis=-1)[:, np.newaxis]
KS_constraint = g_max + 1.0 / rho * np.log(summation)
return -1*KS_constraint[0][0]
def _distance_from_boundaries(self, x_in):
x = [
self._unnorm(valx, self.xmin, self.xmax)
for valx in x_in[0 : self.nturbs]
]
y = [
self._unnorm(valy, self.ymin, self.ymax)
for valy in x_in[self.nturbs : 2 * self.nturbs]
]
boundary_con = np.zeros(self.nturbs)
for i in range(self.nturbs):
loc = Point(x[i], y[i])
boundary_con[i] = loc.distance(self._boundary_line)
if self._boundary_polygon.contains(loc) is True:
boundary_con[i] *= 1.0
else:
boundary_con[i] *= -1.0
return boundary_con
def _get_initial_and_final_locs(self):
x_initial = [
self._unnorm(valx, self.xmin, self.xmax)
for valx in self.x0[0 : self.nturbs]
]
y_initial = [
self._unnorm(valy, self.ymin, self.ymax)
for valy in self.x0[self.nturbs : 2 * self.nturbs]
]
x_opt = [
self._unnorm(valx, self.xmin, self.xmax)
for valx in self.residual_plant.x[0 : self.nturbs]
]
y_opt = [
self._unnorm(valy, self.ymin, self.ymax)
for valy in self.residual_plant.x[self.nturbs : 2 * self.nturbs]
]
return x_initial, y_initial, x_opt, y_opt
# Public methods
[docs]
def optimize(self):
"""
This method finds the optimized layout of wind turbines for power
production given the provided frequencies of occurrence of wind
conditions (wind speed, direction).
Returns:
opt_locs (iterable): A list of the optimized locations of each
turbine (m).
"""
print("=====================================================")
print("Optimizing turbine layout...")
print("Number of parameters to optimize = ", len(self.x0))
print("=====================================================")
opt_locs_norm = self._optimize()
print("Optimization complete.")
opt_locs = [
[
self._unnorm(valx, self.xmin, self.xmax)
for valx in opt_locs_norm[0 : self.nturbs]
],
[
self._unnorm(valy, self.ymin, self.ymax)
for valy in opt_locs_norm[self.nturbs : 2 * self.nturbs]
],
]
return opt_locs
