def configure(self):
super(Assembly, self).configure()
# Let's analyze the lifetime cost of energy cost scaling model!
self.add('lcoe_csm', lcoe_csm_assembly())
# ..using the cutting edge pydakdriver
dakDriver = pydakdriver()
dakDriver.UQ(seed=3232)
driver = self.add('driver',dakDriver)
driver.workflow.add('lcoe_csm')
driver.stdout = 'dakota.out'
driver.stderr = 'dakota.err'
driver.add_parameter('lcoe_csm.weibull_k',low=2.6*0.5, high=2.6*1.5)
driver.add_parameter('lcoe_csm.shear_exponent',low=.1*0.5, high=.1*1.5)
# use same objectives as previous sensitivity analysis
driver.add_objective('lcoe_csm.coe')
driver.add_objective('lcoe_csm.lcoe')
driver.add_objective('lcoe_csm.net_aep')
driver.add_objective('lcoe_csm.bos_costs')
driver.add_objective('lcoe_csm.avg_annual_opex')
driver.add_objective('lcoe_csm.turbine_cost')
driver.add_objective('lcoe_csm.turbine_mass')
driver.add_objective('lcoe_csm.rated_rotor_speed')
def configure(self):
self.add('rose', rosen())
driver_obj = pydakdriver()
driver_obj.Optimization(opt_type='conmin', surrogate_model=True)
driver = self.add('driver', driver_obj)
driver.stdout = 'dakotaOPT.out'
driver.stderr = 'dakotaOPT.err'
driver.samples = 500
driver.add_parameter('rose.x1', low=-1.5, high=1.5)
driver.add_parameter('rose.x2', low=-1.5, high=1.5)
driver.add_objective('rose.f')
def configure(self):
self.add('rose', rosen())
driver_obj = pydakdriver()
driver_obj.Parameter_Study(study_type='centered')
driver = self.add('driver', driver_obj)
driver.stdout = 'dakotaPS.out'
driver.stderr = 'dakotaPS.err'
driver.step_vector = [.1, .1]
driver.steps_per_variable = 9
driver.add_parameter('rose.x1', low=-1.5, high=1.5)
driver.add_parameter('rose.x2', low=-1.5, high=1.5)
driver.add_objective('rose.f')
def configure(self):
self.add('rose', rosen())
self.add('x1', Float(-999, iotype = 'in'))
self.connect('x1', 'rose.x1')
driver_obj = pydakdriver()
driver_obj.UQ()
self.driver = self.add('driver',driver_obj)
self.driver.stdout = dacout
self.driver.stderr = 'dakota.err'
self.driver.sample_type = 'random'
self.driver.seed = 4
self.driver.samples = 5000
self.driver.add_special_distribution('rose.x2', "normal", mean = .1, std_dev = .5)
self.driver.add_objective('x1')
self.driver.add_objective('rose.f')
def configure(self):
self.add('rose', rosen())
driver_obj = pydakdriver()
driver_obj.add_method('sampling',
method_options={
'samples': 30,
'sample_type': 'lhs'
})
driver = self.add('driver', driver_obj)
driver.stdout = 'dakotaDist.out'
driver.stderr = 'dakotaDist.err'
driver.precision = 1e-4
driver.samples = 15
#driver.add_special_distribution('rose.x2', "weibull", alpha = .5, beta = 0.2)
driver.add_parameter('rose.x1', low=-1.5, high=1.5)
driver.add_objective('rose.f')
def configure(self):
self.add('rose', rosen())
self.add('x1', Float(-999, iotype='in'))
self.connect('x1', 'rose.x1')
driver_obj = pydakdriver()
driver_obj.UQ()
self.driver = self.add('driver', driver_obj)
self.driver.stdout = dacout
self.driver.stderr = 'dakota.err'
self.driver.sample_type = 'random'
self.driver.seed = 4
self.driver.samples = 5000
self.driver.add_special_distribution('rose.x2',
"normal",
mean=.1,
std_dev=.5)
self.driver.add_objective('x1')
self.driver.add_objective('rose.f')
def configure(self):
super(Assembly, self).configure()
# Let's analyze the lifetime cost of energy cost scaling model!
wind_class = 'I'
sea_depth = 0.0
with_new_nacelle = True
with_landbos = False
flexible_blade = False
with_3pt_drive = False
with_ecn_opex = False
ecn_file = ''
with_openwind = False
#create_example_se_assembly(wind_class,sea_depth,with_new_nacelle,with_landbos,flexible_blade,with_3pt_drive,with_ecn_opex,ecn_file)
self.add('lcoe_se', lcoe_se_assembly(with_new_nacelle,with_landbos,flexible_blade,with_3pt_drive,with_ecn_opex,ecn_file))
# === Set assembly variables and objects ===
self.lcoe_se.sea_depth = sea_depth # 0.0 for land-based turbine
self.lcoe_se.turbine_number = 100
self.lcoe_se.year = 2009
self.lcoe_se.month = 12
rotor = self.lcoe_se.rotor
nacelle = self.lcoe_se.nacelle
tower = self.lcoe_se.tower
tcc_a = self.lcoe_se.tcc_a
# bos_a = self.lcoe_se.bos_a
# opex_a = self.lcoe_se.opex_a
aep_a = self.lcoe_se.aep_a
fin_a = self.lcoe_se.fin_a
# Turbine ===========
from wisdem.reference_turbines.nrel5mw.nrel5mw import configure_nrel5mw_turbine
configure_nrel5mw_turbine(self.lcoe_se,wind_class,self.lcoe_se.sea_depth)
# tcc ====
self.lcoe_se.advanced_blade = True
self.lcoe_se.offshore = False
self.lcoe_se.assemblyCostMultiplier = 0.30
self.lcoe_se.profitMultiplier = 0.20
self.lcoe_se.overheadCostMultiplier = 0.0
self.lcoe_se.transportMultiplier = 0.0
# for new landBOS
# === new landBOS ===
if with_landbos:
self.lcoe_se.voltage = 137
self.lcoe_se.distInter = 5
self.lcoe_se.terrain = 'FLAT_TO_ROLLING'
self.lcoe_se.layout = 'SIMPLE'
self.lcoe_se.soil = 'STANDARD'
# aep ==== # based on COE review for land-based machines
if not with_openwind:
self.lcoe_se.array_losses = 0.059
self.lcoe_se.A = 8.9 # weibull of 7.25 at 50 m with shear exp of 0.143
self.lcoe_se.k = 2.0
self.lcoe_se.other_losses = 0.101
if not with_ecn_opex:
self.lcoe_se.availability = 0.94
# fin ===
self.lcoe_se.fixed_charge_rate = 0.095
self.lcoe_se.construction_finance_rate = 0.0
self.lcoe_se.tax_rate = 0.4
self.lcoe_se.discount_rate = 0.07
self.lcoe_se.construction_time = 1.0
self.lcoe_se.project_lifetime = 20.0
# Set plant level inputs ===
shearExp = 0.2 #TODO : should be an input to lcoe
#rotor.cdf_reference_height_wind_speed = 90.0
if not with_openwind:
self.lcoe_se.array_losses = 0.1
self.lcoe_se.other_losses = 0.0
if not with_ecn_opex:
self.lcoe_se.availability = 0.98
rotor.turbulence_class = 'B'
self.lcoe_se.multiplier = 2.23
if wind_class == 'Offshore':
# rotor.cdf_reference_mean_wind_speed = 8.4 # TODO - aep from its own module
# rotor.cdf_reference_height_wind_speed = 50.0
# rotor.weibull_shape = 2.1
shearExp = 0.14 # TODO : should be an input to lcoe
self.lcoe_se.array_losses = 0.15
if not with_ecn_opex:
self.lcoe_se.availability = 0.96
self.lcoe_se.offshore = True
self.lcoe_se.multiplier = 2.33
self.lcoe_se.fixed_charge_rate = 0.118
rotor.shearExp = shearExp
tower.wind1.shearExp = shearExp
tower.wind2.shearExp = shearExp
# ..using the cutting edge pydakdriver
dakDriver = pydakdriver()
dakDriver.UQ()
dakDriver.seed = 4723
driver = self.add('driver',dakDriver)
driver.workflow.add('lcoe_se')
driver.stdout = 'dakota.out'
driver.stderr = 'dakota.err'
driver.add_parameter('lcoe_se.A',low=8.2*0.5, high=8.2*1.5)
driver.add_parameter('lcoe_se.k',low=2.0*0.5, high=2.0*1.5)
driver.add_parameter('lcoe_se.shear_exponent',low=.2*0.5, high=.2*1.5)
# use same objectives as previous sensitivity analysis
driver.add_objective('lcoe_se.coe')
# driver.add_objective('lcoe_se.lcoe')
driver.add_objective('lcoe_se.net_aep')
driver.add_objective('lcoe_se.bos_costs')
driver.add_objective('lcoe_se.avg_annual_opex')
driver.add_objective('lcoe_se.turbine_cost')
