# Parse the variable dictionary
yaw = varDict["yaw"]
# Compute the objective function
funcs = {}
funcs["obj"] = -1 * fi.get_farm_power_for_yaw_angle(yaw) / 1e5
fail = False
return funcs, fail
# Setup the optimization problem
optProb = pyoptsparse.Optimization("yaw_opt", objective_function)
# Add the design variables to the optimization problem
optProb.addVarGroup("yaw", 4, "c", lower=0, upper=20, value=2.0)
# Add the objective to the optimization problem
optProb.addObj("obj")
# Setup the optimization solver
# Note: pyOptSparse has other solvers available; some may require additional
# licenses/installation. See https://github.com/mdolab/pyoptsparse for more
# information. When ready, they can be invoked by changing 'SLSQP' to the
# solver name, for example: 'opt = pyoptsparse.SNOPT(fi=fi)'.
opt = pyoptsparse.SLSQP(fi=fi)
# Run the optimization with finite-differencing
solution = opt(optProb, sens="FD")
print(solution)
def Pyoptsparse_Solve(problem,
solver='SNOPT',
FD='single',
sense_step=1.0E-6,
nonderivative_line_search=False):
""" This converts your SUAVE Nexus problem into a PyOptsparse optimization problem and solves it.
Pyoptsparse has many algorithms, they can be switched out by using the solver input.
Assumptions:
None
Source:
N/A
Inputs:
problem [nexus()]
solver [str]
FD (parallel or single) [str]
sense_step [float]
nonderivative_line_search [bool]
Outputs:
outputs [list]
Properties Used:
None
"""
# Have the optimizer call the wrapper
mywrap = lambda x: PyOpt_Problem(problem, x)
inp = problem.optimization_problem.inputs
obj = problem.optimization_problem.objective
con = problem.optimization_problem.constraints
if FD == 'parallel':
from mpi4py import MPI
comm = MPI.COMM_WORLD
myrank = comm.Get_rank()
# Instantiate the problem and set objective
try:
import pyoptsparse as pyOpt
except:
raise ImportError('No version of pyOptsparse found')
opt_prob = pyOpt.Optimization('SUAVE', mywrap)
for ii in range(len(obj)):
opt_prob.addObj(obj[ii, 0])
# Set inputs
nam = inp[:, 0] # Names
ini = inp[:, 1] # Initials
bnd = inp[:, 2] # Bounds
scl = inp[:, 3] # Scale
typ = inp[:, 4] # Type
# Pull out the constraints and scale them
bnd_constraints = help_fun.scale_const_bnds(con)
scaled_constraints = help_fun.scale_const_values(con, bnd_constraints)
x = ini / scl
for ii in range(0, len(inp)):
lbd = (bnd[ii][0] / scl[ii])
ubd = (bnd[ii][1] / scl[ii])
#if typ[ii] == 'continuous':
vartype = 'c'
#if typ[ii] == 'integer':
#vartype = 'i'
opt_prob.addVar(nam[ii], vartype, lower=lbd, upper=ubd, value=x[ii])
# Setup constraints
for ii in range(0, len(con)):
name = con[ii][0]
edge = scaled_constraints[ii]
if con[ii][1] == '<':
opt_prob.addCon(name, upper=edge)
elif con[ii][1] == '>':
opt_prob.addCon(name, lower=edge)
elif con[ii][1] == '=':
opt_prob.addCon(name, lower=edge, upper=edge)
# Finalize problem statement and run
print(opt_prob)
if solver == 'SNOPT':
opt = pyOpt.SNOPT()
CD_step = (sense_step**2.)**(1. / 3.
) #based on SNOPT Manual Recommendations
opt.setOption('Function precision', sense_step**2.)
opt.setOption('Difference interval', sense_step)
opt.setOption('Central difference interval', CD_step)
elif solver == 'SLSQP':
opt = pyOpt.SLSQP()
elif solver == 'FSQP':
opt = pyOpt.FSQP()
elif solver == 'PSQP':
opt = pyOpt.PSQP()
elif solver == 'NSGA2':
opt = pyOpt.NSGA2(pll_type='POA')
elif solver == 'ALPSO':
#opt = pyOpt.pyALPSO.ALPSO(pll_type='DPM') #this requires DPM, which is a parallel implementation
opt = pyOpt.ALPSO()
elif solver == 'CONMIN':
opt = pyOpt.CONMIN()
elif solver == 'IPOPT':
opt = pyOpt.IPOPT()
elif solver == 'NLPQLP':
opt = pyOpt.NLQPQLP()
elif solver == 'NLPY_AUGLAG':
opt = pyOpt.NLPY_AUGLAG()
if nonderivative_line_search == True:
opt.setOption('Nonderivative linesearch')
if FD == 'parallel':
outputs = opt(opt_prob, sens='FD', sensMode='pgc')
elif solver == 'SNOPT' or solver == 'SLSQP':
outputs = opt(opt_prob, sens='FD', sensStep=sense_step)
else:
outputs = opt(opt_prob)
return outputs
optProb.addVarGroup('modes', 14, 'c', lower=None, upper=None, value=.5)
optProb.addVar('alpha', 'c', lower=low_alpha, upper=up_alpha, value=.5)
optProb.addCon('thick_0.1', lower=thickness_constraint[0], upper=None)
optProb.addCon('thick_0.3', lower=thickness_constraint[1], upper=None)
optProb.addCon('thick_0.5', lower=thickness_constraint[2], upper=None)
optProb.addCon('thick_0.7', lower=thickness_constraint[3], upper=None)
optProb.addCon('thick_0.9', lower=thickness_constraint[4], upper=None)
optProb.addCon('Cl', lower=Cl_req, upper=Cl_req)
optProb.addObj('obj')
print(optProb)
#%%
opt = pyoptsparse.SLSQP()
sol = opt(optProb, sens=sens)
print(sol)
#%%
modes_opt = sol.xStar['modes']
alpha_opt = sol.xStar['alpha']
Cd_opt = denormalize_y(sol.fStar, 1)
x0 = np.zeros((1, x_dim))
x0[0, 0:14] = airfoil
x0[0, 14] = Mach
x0_n = normalize_x(x0)
Cd_0 = Cd(x0_n)
print(Cd_opt, Cd_0)
x_opt = np.zeros((1, x_dim))
x_opt[0, 0:14] = modes_opt
x_opt[0, 15] = alpha_opt
lower=1,
upper=500,
value=start_solar)
optProb.addVar("battery_storage_mwh",
type="c",
lower=0,
upper=1000,
value=start_battery_mwh)
optProb.addVar("battery_storage_mw",
type="c",
lower=0,
upper=1000,
value=start_battery_mw)
optProb.addObj("h_lcoe")
optimize = pyoptsparse.SLSQP()
optimize.setOption("MAXIT", value=5)
# optimize.setOption("ACC",value=1E-6)
# optimize = pyoptsparse.SNOPT()
print("start GB optimization")
solution = optimize(optProb, sens="FD")
print("******************************************")
print("finished optimization")
opt_DVs = solution.getDVs()
opt_electrolyzer = opt_DVs["electrolyzer_size_mw"]
opt_wind = opt_DVs["wind_capacity_mw"]
opt_solar = opt_DVs["solar_capacity_mw"]
opt_battery_mwh = opt_DVs["battery_storage_mwh"]
opt_battery_mw = opt_DVs["battery_storage_mw"]
