List of available technology templates

All technologies that are available as templates are listed below, as well as their respective technology models (i.e., types of technologies that follow similar constraints, which are explained here). The list can also be produced by:

adopt.show_available_technologies()

Technology name	Technology model (Tec_type)
DAC_Adsorption	DAC_Adsorption
Hydro_Reservoir	Hydro_Open
PumpedHydro_Open	HydroOpen
PumpedHydro_Closed	STOR
SteamReformer_CCS	CONV2
EthyleneCompression	CONV3
CrackerFurnace	CONV3
EthyleneSeparation	CONV3
EthyleneCompression_Electric	CONV3
CrackerFurnace_Electric	CONV3
SteamReformer	CONV2
GasTurbine_H2_250	GasTurbine_H2_250
GasTurbine_NG_100	GasTurbine_NG_100
GasTurbine_NG_250	GasTurbine_NG_250
GasTurbine_simple_CCS	CONV1
GasTurbine_H2_400	GasTurbine_H2_400
GasTurbine_simple	CONV1
GasTurbine_H2_10	GasTurbine_H2_10
GasTurbine_NG_400	GasTurbine_NG_400
CombinedCycle_fixed_size	CCPP
GasTurbine_H2_100	GasTurbine_H2_100
GasTurbine_NG_10	GasTurbine_NG_10
SteamTurbine	CONV2
PermanentStorage_CO2_simple	SINK
Storage_NG	STOR
TemporaryStorage_CO2	STOR
Storage_Battery	STOR
Storage_H2_Cavern	STOR
Storage_Ethylene	STOR
Storage_HotWater	STOR
Storage_PCM	STOR
Storage_H2	STOR
WindTurbine_Onshore_4000	RES
WindTurbine_Onshore_2500	RES
SolarThermal	RES
WindTurbine_Offshore_9500	RES
WindTurbine_Onshore_1500	RES
Photovoltaic	RES
WindTurbine_Offshore_11000	RES
WindTurbine_Offshore_6000	RES
CO2_Compressor	CONV1
HeatPump_SeaWaterSourced	HeatPump_WaterSourced
Furnace_H2	CONV2
HeatPump_AirSourced	HeatPump_AirSourced
HeatPump_GroundSourced	HeatPump_GroundSourced
Boiler_Small_H2	CONV2
Boiler_Industrial_NG	CONV2
Furnace_NG	CONV2
Boiler_Small_NG	CONV2
Boiler_El	CONV2
HeatPump_WaterSourced	HeatPump_WaterSourced

List of available network templates

All networks that are available as a template are listed below. The list can also be produced by:

adopt.show_available_networks()

Network name
hydrogenRailway	fluid
electricityOnshore	electricity
electricitySimple	electricity
CO2_Pipeline	fluid
hydrogenTruck	fluid
hydrogenPipelineOnshore	fluid
electricityOffshore	electricity
CO2Truck	fluid
CO2Ship	fluid
heat	fluid
hydrogenShip	fluid
hydrogenPipelineOffshore	fluid
hydrogenSimple	fluid
CO2Railway	fluid

Calculating detailed technology and network costs

For a number of technologies and networks, there are detailed cost models available. The main functions to generate them are documented here. Below, you can also find further information about each of the implemented cost models.

Examplary use:

from adopt_net0 import database as td

# Show all available cost models
td.help()

# Show help for a specific cost model
tec = "DAC_Adsorption"
td.help(component_name=tec)

# Define options
options = {"currency_out": "EUR",
        "financial_year_out": 2020,
        "discount_rate": 0.1,
        "cumulative_capacity_installed_t_per_a": 10000}

# Calculate indicators and print them
financial_inds = td.calculate_indicators(tec, options)
print(financial_inds)

# Write to a json file in specified PATH
td.write_json(tec, PATH, options)

calculate_indicators(component_name: str, options: dict) → dict

Calculates financial parameters based on the component and the provided options

If no options are provided, the default options are used. Calculates:

lifetime
discount rate
capex
fixed opex
variable opex
levelized costs (if available)

Parameters:

component_name (str) – Name of the technology/network
options (dict) – options used in the calculations

Returns:

dictionary containing financial parameters

Return type:

dict

help(component_name: str = None)

Provides help on available cost models of technologies and networks.

If no argument is provided, it prints all available cost models.
If a component name is provided, it prints detailed information about that component.

Parameters:: component_name (str) – Name of the technology/network (optional)

write_json(component_name: str, directory: str, options)

Writes a json file of the component to the specified directory

Parameters:

component_name (str) – Name of the technology/network
directory (str) – directory to write to
options (dict) – options used in the calculations

Returns:

Detailed technology cost models

Photovoltaic

class PV_CostModel(tec_name)

Photovoltaic energy

Possible options are:

If source = “IRENA”

cost model is based on IRENA (2023): Renewable power generation costs in 2023 for utility scale photovoltaics
region can be chosen among different countries

If source = “NREL”

cost model is based on NREL (2024): 2024 Annual Technology Baseline (ATB) for Wind Turbine Technology 1
projection_year: future year for which to estimate cost (possible values: 2022-2050)
projection_type: can be “Advanced”, “Moderate”, or “Conservative”
pv_type: can be “utility” or “rooftop commercial” or “rooftop residential”

If source = “DEA”

cost model is based on Danish Energy Agency (2025): Technology Data for Generation of Electricity and District Heating
projection_year: future year for which to estimate cost (possible values: 2022-2050)
pv_type: can be “utility” or “rooftop commercial” or “rooftop residential”

Financial indicators are:

unit_capex in [currency]/MWh
fixed capex as fraction of up-front capex
variable opex in [currency]/MWh
levelized cost in [currency]/MWh
lifetime in years

Wind energy

class WindEnergy_CostModel(tec_name)

Wind energy (onshore and offshore)

Possible options are:

nameplate_capacity_MW: Capacity of a wind turbine in MW
terrain: Onshore or offshore wind turbine

If source = “IRENA”

cost model is based on IRENA (2023): Renewable power generation costs in 2023
region can be chosen among different countries (‘Brazil’, ‘Canada’, ‘China’, ‘France’, ‘Germany’, ‘India’, ‘Japan’, ‘Spain’, ‘Sweden’, ‘United Kingdom’, ‘United States’, ‘Australia’, ‘Ireland’)

If source = “NREL”

cost model is based on NREL (2024): 2024 Annual Technology Baseline (ATB) for Wind Turbine Technology 1
projection_year: future year for which to estimate cost (possible values: 2030, 2040, 2050)
projection_type: can be “Advanced”, “Moderate”, or “Conservative”
mounting_type: can be “fixed” or “floating” for offshore turbines

If source = “DEA”

cost model is based on Danish Energy Agency (2025): Technology Data for Generation of Electricity and District Heating
projection_year: future year for which to estimate cost (possible values: 2022-2050)
mounting_type: can be “fixed” or “floating” for offshore turbines

Financial indicators are:

unit_capex in [currency]/turbine
fixed capex as fraction of annualized capex
variable opex in [currency]/MWh
levelized cost in [currency]/MWh
lifetime in years

Heat Pump

class HeatPump_CostModel(tec_name)

Heat Pump cost model

Possible options are:

If source = “DEA”

cost model is based on Danish Energy Agency (2025): Technology Data for Generation of Electricity and District Heating
projection_year: future year for which to estimate cost (possible values: 2022-2050)
hp_type: can be “air_sourced_1MW” or “air_sourced_3MW” or “air_sourced_10MW” or “seawater_20MW”

Financial indicators are:

unit_capex in [currency]/MW (el)
fixed capex as fraction of annualized capex
variable opex in [currency]/MWh (el)
levelized cost in [currency]/MWh (el)
lifetime in years

Solid Sorbent Direct Air Capture

class Dac_SolidSorbent_CostModel(tec_name)

DAC (Adsorption)

Possible options are:

If source = “Sievert”

cost model is based on Sievert, K., Schmidt, T. S., & Steffen, B. (2024). Considering technology characteristics to project future costs of direct air capture. Joule, 8(4), 979-999, https://doi.org/10.1016/j.joule.2024.02.005.
cumulative_capacity_installed_t_per_a: total global installed capturing capacity in t/a. Determines the cost reduction due to learning.
average_productivity_per_module_kg_per_h: average productivity of a DAC module in kg/h (default is at 20 degree, 43% humidity)
capacity_factor: used to calculate levelized cost of removal

Financial indicators are:

module_capex in [currency]/module
fixed capex as fraction of annualized capex
variable opex in [currency]/ton
levelized cost in [currency]/ton without energy costs
lifetime in years

CO2 compression

class CO2_Compression_CostModel(tec_name)

CO2 Compression

Possible options are:

If source = “Oeuvray”

cost and energy consumption model is based on Oeuvray, P., Burger, J., Roussanaly, S., Mazzotti, M., Becattini, V. (2024): Multi-criteria assessment of inland and offshore carbon dioxide transport options, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2024.140781. - cumulative_capacity_installed_t_per_a: total global installed capturing capacity in t/a. Determines the cost reduction due to learning.
massflow_min_kg_per_s: minimal mass flow rate of CO2 in kg/s to evaluate for costs
massflow_max_kg_per_s: maximal mass flow rate of CO2 in kg/s to evaluate for costs
massflow_evaluation_points: for how many points should costs be calculated between massflow_min_kg_per_s, massflow_max_kg_per_s (includes min and max)
p_inlet_bar: inlet pressure in bar (beginning of pipeline)
p_outlet_bar: outlet pressure in bar (end of pipeline)
capex_model: for 1 linear cost through origin, for 3 linear with intercept

Financial indicators are:

unit_capex in [currency]/t/a
fixed capex as fraction of annualized capex
variable opex in [currency]/ton
lifetime in years

Technical indicators are:

energyconsumption in MWh/t compressed

Detailed network cost models

CO2 pipeline

class CO2_Pipeline_CostModel(tec_name)

CO2 Pipeline

Calculates CO2 transport costs and compression energy.

Possible options are:

If source = “Oeuvray”

cost and energy consumption model is based on Oeuvray, P., Burger, J., Roussanaly, S., Mazzotti, M., Becattini, V. (2024): Multi-criteria assessment of inland and offshore carbon dioxide transport options, Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2024.140781.
length_km: Length of pipeline in km
timeframe: determines which steel grades are available, can be ‘short-term’, ‘mid-term’, or ‘long-term’
massflow_min_kg_per_s: minimal mass flow rate of CO2 in kg/s to evaluate for costs
massflow_max_kg_per_s: maximal mass flow rate of CO2 in kg/s to evaluate for costs
massflow_evaluation_points: for how many points should costs be calculated between massflow_min_kg_per_s, massflow_max_kg_per_s (includes min and max)
terrain: ‘Offshore’ or ‘Onshore’, determines right of way cost and if recompression is possible (not possible for “Offshore”)
electricity_price_eur_per_mw: used to minimize levelized cost (EUR/MWh)
operating_hours_per_a: number of operating hours per year
p_inlet_bar: inlet pressure in bar (beginning of pipeline)
p_outlet_bar: outlet pressure in bar (end of pipeline)

Financial indicators are:

gamma1, gamma2, gamma3, gamma4 in [currency] (equivalent to the cost parameters of a network)
fixed opex as fraction of up-front capex
variable opex in [currency]/ton
lifetime in years
levelized_cost in [currency]/t

Technical indicators are:

energyconsumption in MWh/t compressed