import warnings
import numpy as np
import openmdao.api as om
from wisdem.plant_financese.plant_finance import PlantFinance as PlantFinance_orig
from wisdem.landbosse.landbosse_omdao.landbosse import LandBOSSE as LandBOSSE_orig
from wisdem.orbit.orbit_api import Orbit as Orbit_orig
from ard.cost.approximate_turbine_spacing import SpacingApproximations
[docs]
class LandBOSSEWithSpacingApproximations(om.Group):
"""
OpenMDAO group that connects the SpacingApproximations component to the LandBOSSE component.
This group calculates the turbine spacing using the SpacingApproximations and passes it
to the LandBOSSE component for further cost estimation.
"""
[docs]
def initialize(self):
"""Initialize the group and declare options."""
self.options.declare(
"modeling_options", types=dict, desc="Ard modeling options"
)
[docs]
def setup(self):
"""Set up the group by adding and connecting components."""
# Add the PrimarySpacingApproximations component
self.add_subsystem(
"spacing_approximations",
SpacingApproximations(modeling_options=self.options["modeling_options"]),
promotes_inputs=["total_length_cables"],
)
# Add the LandBOSSE component
self.add_subsystem(
"landbosse",
LandBOSSEGroup(modeling_options=self.options["modeling_options"]),
promotes_inputs=[
"*",
(
"turbine_spacing_rotor_diameters",
"internal_turbine_spacing_rotor_diameters",
),
(
"row_spacing_rotor_diameters",
"internal_row_spacing_rotor_diameters",
),
],
promotes_outputs=["*"], # Expose all outputs from LandBOSSE
)
# Connect the turbine and row spacing outputs from the approximations to LandBOSSE
self.connect(
"spacing_approximations.primary_turbine_spacing_diameters",
"internal_turbine_spacing_rotor_diameters",
)
self.connect(
"spacing_approximations.secondary_turbine_spacing_diameters",
"internal_row_spacing_rotor_diameters",
)
[docs]
class LandBOSSEGroup(om.Group):
[docs]
def initialize(self):
"""Initialize the group and declare options."""
self.options.declare(
"modeling_options", types=dict, desc="Ard modeling options"
)
[docs]
def setup(self):
# add IVCs for landbosse
variable_mapping = LandBOSSE_setup_latents(
modeling_options=self.options["modeling_options"]
)
# create source independent variable components for LandBOSSE inputs
for key, meta in variable_mapping.items():
if key in ["num_turbines", "number_of_blades"]:
comp = om.IndepVarComp()
comp.add_discrete_output(name=key, val=meta["val"])
self.add_subsystem(f"IVC_landbosse_{key}", comp, promotes=["*"])
else:
self.add_subsystem(
f"IVC_landbosse_{key}",
om.IndepVarComp(key, val=meta["val"], units=meta["units"]),
promotes=["*"],
)
# add landbosse
self.add_subsystem(
"landbosse",
LandBOSSE_orig(),
promotes=[
"total_capex",
"total_capex_kW",
"bos_capex_kW",
"turbine_spacing_rotor_diameters",
"row_spacing_rotor_diameters",
],
)
# connect
for key, val in variable_mapping.items():
self.connect(key, f"landbosse.{key}")
[docs]
class ORBITGroup(om.Group):
[docs]
def initialize(self):
"""Initialize the group and declare options."""
self.options.declare(
"modeling_options", types=dict, desc="Ard modeling options"
)
[docs]
def setup(self):
# add IVCs for landbosse
variable_mapping = ORBIT_setup_latents(
modeling_options=self.options["modeling_options"]
)
# create source independent variable components for LandBOSSE inputs
for key, meta in variable_mapping.items():
if key in ["number_of_turbines", "number_of_blades", "num_mooring_lines"]:
comp = om.IndepVarComp()
comp.add_discrete_output(name=key, val=meta["val"])
self.add_subsystem(f"IVC_orbit_{key}", comp, promotes=["*"])
else:
self.add_subsystem(
f"IVC_orbit_{key}",
om.IndepVarComp(key, val=meta["val"], units=meta["units"]),
promotes=["*"],
)
# add orbit
self.add_subsystem(
"orbit",
Orbit_orig(
floating=self.options["modeling_options"]["floating"],
jacket=self.options["modeling_options"].get("jacket"),
jacket_legs=self.options["modeling_options"].get("jacket_legs"),
),
promotes=[
"total_capex",
"total_capex_kW",
"bos_capex",
"installation_capex",
"plant_turbine_spacing",
"plant_row_spacing",
],
)
# connect
for key, val in variable_mapping.items():
self.connect(key, f"orbit.{key}")
[docs]
class FinanceSEGroup(om.Group):
[docs]
def initialize(self):
"""Initialize the group and declare options."""
self.options.declare(
"modeling_options", types=dict, desc="Ard modeling options"
)
[docs]
def setup(self):
# add IVCs for landbosse
variable_mapping = FinanceSE_setup_latents(
modeling_options=self.options["modeling_options"]
)
# create source independent variable components for LandBOSSE inputs
for key, meta in variable_mapping.items():
if key in [
"turbine_number",
]:
comp = om.IndepVarComp()
comp.add_discrete_output(name=key, val=meta["val"])
self.add_subsystem(f"IVC_financese_{key}", comp, promotes=["*"])
else:
self.add_subsystem(
f"IVC_financese_{key}",
om.IndepVarComp(key, val=meta["val"], units=meta["units"]),
promotes=["*"],
)
# add financese #TODO check promotes
self.add_subsystem(
"financese",
PlantFinance_orig(),
promotes=[
"offset_tcc_per_kW",
"plant_aep_in",
"bos_per_kW",
# "tcc_per_kW",
"lcoe",
],
)
# connect
for key, val in variable_mapping.items():
self.connect(key, f"financese.{key}")
[docs]
class TurbineCapitalCosts(om.ExplicitComponent):
"""
A simple component to compute the turbine capital costs.
Inputs
------
machine_rating : float
rating of the wind turbine in kW
tcc_per_kW : float
turbine capital costs per kW (as output from WISDEM tools)
offset_tcc_per_kW : float
additional tcc per kW (offset)
Discrete Inputs
---------------
turbine_number : int
number of turbines in the farm
Outputs
-------
tcc : float
turbine capital costs in USD
"""
[docs]
def setup(self):
"""Setup of OM component."""
self.add_input("machine_rating", 0.0, units="kW")
self.add_input("tcc_per_kW", 0.0, units="USD/kW")
self.add_input("offset_tcc_per_kW", 0.0, units="USD/kW")
self.add_discrete_input("turbine_number", 0)
self.add_output("tcc", 0.0, units="USD")
[docs]
def setup_partials(self):
"""Derivative setup for OM component."""
# complex step for simple gradients
self.declare_partials("*", "*", method="cs")
[docs]
def compute(self, inputs, outputs, discrete_inputs=None, discrete_outputs=None):
"""Computation for the OM compoent."""
# Unpack parameters
t_rating = inputs["machine_rating"]
n_turbine = discrete_inputs["turbine_number"]
tcc_per_kW = inputs["tcc_per_kW"] + inputs["offset_tcc_per_kW"]
outputs["tcc"] = n_turbine * tcc_per_kW * t_rating
[docs]
class OperatingExpenses(om.ExplicitComponent):
"""
A simple component to compute the operating costs.
Inputs
------
machine_rating : float
rating of the wind turbine in kW
opex_per_kW : float
annual operating and maintenance costs per kW (as output from WISDEM
tools)
Discrete Inputs
---------------
turbine_number : int
number of turbines in the farm
Outputs
-------
opex : float
annual operating and maintenance costs in USD
"""
[docs]
def setup(self):
"""Setup of OM component."""
self.add_input("machine_rating", 0.0, units="kW")
self.add_input("opex_per_kW", 0.0, units="USD/kW/yr")
self.add_discrete_input("turbine_number", 0)
self.add_output("opex", 0.0, units="USD/yr")
[docs]
def setup_partials(self):
"""Derivative setup for OM component."""
# complex step for simple gradients
self.declare_partials("*", "*", method="cs")
[docs]
def compute(self, inputs, outputs, discrete_inputs=None, discrete_outputs=None):
"""Computation for the OM compoent."""
# Unpack parameters
t_rating = inputs["machine_rating"]
n_turbine = discrete_inputs["turbine_number"]
opex_per_kW = inputs["opex_per_kW"]
outputs["opex"] = n_turbine * opex_per_kW * t_rating
[docs]
def LandBOSSE_setup_latents(modeling_options: dict) -> None:
"""
A function to set up the LandBOSSE latent variables using modeling options.
Parameters
----------
prob : openmdao.api.Problem
an OpenMDAO problem for which we want to setup the LandBOSSE latent
variables
modeling_options : dict
a modeling options dictionary
"""
# Define the mapping between OpenMDAO variable names and modeling_options keys
offshore_fixed_keys = [
"monopile_mass",
"monopile_cost",
]
offshore_floating_keys = [
"num_mooring_lines",
"mooring_line_mass",
"mooring_line_diameter",
"mooring_line_length",
"anchor_mass",
"floating_substructure_cost",
]
def _base_common():
return {
"num_turbines": {
"val": modeling_options["layout"]["N_turbines"],
"units": None,
},
"turbine_rating_MW": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"performance"
]["rated_power"]
/ 1.0e6,
"units": "MW",
},
"hub_height_meters": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"hub_height"
],
"units": "m",
},
"rotor_diameter_m": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"rotor_diameter"
],
"units": "m",
},
"number_of_blades": {
"val": modeling_options["costs"]["num_blades"],
"units": None,
},
"tower_mass": {
"val": modeling_options["costs"]["tower_mass"],
"units": "t",
},
"nacelle_mass": {
"val": modeling_options["costs"]["nacelle_mass"],
"units": "t",
},
"blade_mass": {
"val": modeling_options["costs"]["blade_mass"],
"units": "t",
},
"commissioning_cost_kW": {
"val": modeling_options["costs"]["commissioning_cost_kW"],
"units": "USD/kW",
},
"decommissioning_cost_kW": {
"val": modeling_options["costs"]["decommissioning_cost_kW"],
"units": "USD/kW",
},
}
if any(key in modeling_options["costs"] for key in offshore_fixed_keys):
variable_mapping = _base_common()
variable_mapping.update(
{
"monopile_mass": {
"val": modeling_options["costs"]["monopile_mass"],
"units": "kg",
},
"monopile_cost": {
"val": modeling_options["costs"]["monopile_cost"],
"units": "USD",
},
}
)
elif any(key in modeling_options["costs"] for key in offshore_floating_keys):
variable_mapping = _base_common()
variable_mapping.update(
{
"num_mooring_lines": {
"val": modeling_options["costs"]["num_mooring_lines"],
"units": None,
},
"mooring_line_mass": {
"val": modeling_options["costs"]["mooring_line_mass"],
"units": "kg",
},
"mooring_line_diameter": {
"val": modeling_options["costs"]["mooring_line_diameter"],
"units": "m",
},
"mooring_line_length": {
"val": modeling_options["costs"]["mooring_line_length"],
"units": "m",
},
"anchor_mass": {
"val": modeling_options["costs"]["anchor_mass"],
"units": "kg",
},
"floating_substructure_cost": {
"val": modeling_options["costs"]["floating_substructure_cost"],
"units": "USD",
},
}
)
else:
variable_mapping = _base_common()
variable_mapping.update(
{
"rated_thrust_N": {
"val": modeling_options["costs"]["rated_thrust_N"],
"units": "N",
},
"gust_velocity_m_per_s": {
"val": modeling_options["costs"]["gust_velocity_m_per_s"],
"units": "m/s",
},
"blade_surface_area": {
"val": modeling_options["costs"]["blade_surface_area"],
"units": "m**2",
},
"hub_mass": {
"val": modeling_options["costs"]["hub_mass"],
"units": "kg",
},
"foundation_height": {
"val": modeling_options["costs"]["foundation_height"],
"units": "m",
},
"trench_len_to_substation_km": {
"val": modeling_options["costs"]["trench_len_to_substation_km"],
"units": "km",
},
"distance_to_interconnect_mi": {
"val": modeling_options["costs"]["distance_to_interconnect_mi"],
"units": "mi",
},
"interconnect_voltage_kV": {
"val": modeling_options["costs"]["interconnect_voltage_kV"],
"units": "kV",
},
}
)
return variable_mapping
[docs]
def ORBIT_setup_latents(modeling_options: dict) -> None:
"""
A function to set up the ORBIT latent variables using modeling options.
Parameters
----------
prob : openmdao.api.Problem
an OpenMDAO problem for which we want to setup the ORBIT latent
variables
modeling_options : dict
a modeling options dictionary
"""
variable_mapping = {
"turbine_rating": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"performance"
]["rated_power"],
"units": "W",
},
"site_depth": {"val": modeling_options["site_depth"], "units": "m"},
"number_of_turbines": {
"val": modeling_options["layout"]["N_turbines"],
"units": None,
},
"number_of_blades": {
"val": modeling_options["costs"]["num_blades"],
"units": None,
},
"hub_height": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"hub_height"
],
"units": "m",
},
"turbine_rotor_diameter": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"rotor_diameter"
],
"units": "m",
},
"tower_length": {
"val": modeling_options["costs"]["tower_length"],
"units": "m",
},
"tower_mass": {"val": modeling_options["costs"]["tower_mass"], "units": "t"},
"nacelle_mass": {
"val": modeling_options["costs"]["nacelle_mass"],
"units": "t",
},
"blade_mass": {"val": modeling_options["costs"]["blade_mass"], "units": "t"},
"turbine_capex": {
"val": modeling_options["costs"]["turbine_capex"],
"units": "USD/kW",
},
"site_mean_windspeed": {
"val": modeling_options["costs"]["site_mean_windspeed"],
"units": "m/s",
},
"turbine_rated_windspeed": {
"val": modeling_options["costs"]["turbine_rated_windspeed"],
"units": "m/s",
},
"commissioning_cost_kW": {
"val": modeling_options["costs"]["commissioning_cost_kW"],
"units": "USD/kW",
},
"decommissioning_cost_kW": {
"val": modeling_options["costs"]["decommissioning_cost_kW"],
"units": "USD/kW",
},
"plant_substation_distance": {
"val": modeling_options["costs"]["plant_substation_distance"],
"units": "km",
},
"interconnection_distance": {
"val": modeling_options["costs"]["interconnection_distance"],
"units": "km",
},
"site_distance": {
"val": modeling_options["costs"]["site_distance"],
"units": "km",
},
"site_distance_to_landfall": {
"val": modeling_options["costs"]["site_distance_to_landfall"],
"units": "km",
},
"port_cost_per_month": {
"val": modeling_options["costs"]["port_cost_per_month"],
"units": "USD/month",
},
"construction_insurance": {
"val": modeling_options["costs"]["construction_insurance"],
"units": "USD/kW",
},
"construction_financing": {
"val": modeling_options["costs"]["construction_financing"],
"units": "USD/kW",
},
"contingency": {
"val": modeling_options["costs"]["contingency"],
"units": "USD/kW",
},
"site_auction_price": {
"val": modeling_options["costs"]["site_auction_price"],
"units": "USD",
},
"site_assessment_cost": {
"val": modeling_options["costs"]["site_assessment_cost"],
"units": "USD",
},
"construction_plan_cost": {
"val": modeling_options["costs"]["construction_plan_cost"],
"units": "USD",
},
"installation_plan_cost": {
"val": modeling_options["costs"]["installation_plan_cost"],
"units": "USD",
},
"boem_review_cost": {
"val": modeling_options["costs"]["boem_review_cost"],
"units": "USD",
},
}
# Add floating-foundation specific keys if applicable
if modeling_options["floating"]:
variable_mapping.update(
{
"num_mooring_lines": {
"val": modeling_options["costs"]["num_mooring_lines"],
"units": None,
},
"mooring_line_mass": {
"val": modeling_options["costs"]["mooring_line_mass"],
"units": "kg",
},
"mooring_line_diameter": {
"val": modeling_options["costs"]["mooring_line_diameter"],
"units": "m",
},
"mooring_line_length": {
"val": modeling_options["costs"]["mooring_line_length"],
"units": "m",
},
"anchor_mass": {
"val": modeling_options["costs"]["anchor_mass"],
"units": "kg",
},
"transition_piece_mass": {
"val": modeling_options["costs"]["transition_piece_mass"],
"units": "t",
},
"transition_piece_cost": {
"val": modeling_options["costs"]["transition_piece_cost"],
"units": "USD",
},
"floating_substructure_cost": {
"val": modeling_options["costs"]["floating_substructure_cost"],
"units": "USD",
},
}
)
# Add fixed-foundation (mooring) specific keys if applicable
else:
variable_mapping.update(
{
"monopile_mass": {
"val": modeling_options["costs"]["monopile_mass"],
"units": "t",
},
"monopile_cost": {
"val": modeling_options["costs"]["monopile_cost"],
"units": "USD",
},
"monopile_length": {
"val": modeling_options["costs"]["monopile_length"],
"units": "m",
},
"monopile_diameter": {
"val": modeling_options["costs"]["monopile_diameter"],
"units": "m",
},
"transition_piece_mass": {
"val": modeling_options["costs"]["transition_piece_mass"],
"units": "t",
},
"transition_piece_cost": {
"val": modeling_options["costs"]["transition_piece_cost"],
"units": "USD",
},
}
)
# TODO include jacket-type foundation
# # if jacket
# prob.set_val(
# comp2promotion_map["orbit.orbit.jacket_r_foot"],
# modeling_options["turbine"]["costs"]["jacket_r_foot"])
# prob.set_val(
# comp2promotion_map["orbit.orbit.jacket_length"],
# modeling_options["turbine"]["costs"]["jacket_length"])
# prob.set_val(
# comp2promotion_map["orbit.orbit.jacket_mass"],
# modeling_options["turbine"]["costs"]["jacket_mass"])
# prob.set_val(
# comp2promotion_map["orbit.orbit.jacket_cost"],
# modeling_options["turbine"]["costs"]["jacket_cost"])
# prob.set_val(
# comp2promotion_map["orbit.orbit.transition_piece_mass"],
# modeling_options["turbine"]["costs"]["transition_piece_mass"])
# prob.set_val(
# comp2promotion_map["orbit.orbit.transition_piece_cost"],
# modeling_options["turbine"]["costs"]["transition_piece_cost"])
return variable_mapping
[docs]
def FinanceSE_setup_latents(modeling_options):
"""
A function to set up the FinanceSE latent variables using modeling options.
Parameters
----------
prob : openmdao.api.Problem
an OpenMDAO problem for which we want to setup the FinanceSE latent
variables
modeling_options : dict
a modeling options dictionary
"""
# Define the mapping between OpenMDAO variable names and modeling_options keys
variable_mapping = {
"turbine_number": {
"val": int(modeling_options["layout"]["N_turbines"]),
"units": None,
},
"machine_rating": {
"val": modeling_options["windIO_plant"]["wind_farm"]["turbine"][
"performance"
]["rated_power"],
"units": "W",
},
"tcc_per_kW": {
"val": modeling_options["costs"]["tcc_per_kW"],
"units": "USD/kW",
},
"opex_per_kW": {
"val": modeling_options["costs"]["opex_per_kW"],
"units": "USD/kW/year",
},
}
return variable_mapping
