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test_compile_cost_assumptions.py
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# SPDX-FileCopyrightText: Contributors to technology-data <https://github.com/pypsa/technology-data>
#
# SPDX-License-Identifier: GPL-3.0-only
# coding: utf-8
import copy
import pathlib
import sys
import numpy as np
import pandas as pd
import pytest
sys.path.append("./scripts")
from compile_cost_assumptions import (
add_carbon_capture,
add_description,
add_gas_storage,
annuity,
clean_up_units,
convert_units,
dea_sheet_names,
geometric_series,
get_data_from_DEA,
get_excel_sheets,
get_sheet_location,
prepare_inflation_rate,
set_round_trip_efficiency,
set_specify_assumptions,
)
path_cwd = pathlib.Path.cwd()
snakemake_input_dictionary = {
"inflation_rate": "inputs/prc_hicp_aind__custom_9928419_spreadsheet.xlsx",
"pypsa_costs": "inputs/costs_PyPSA.csv",
"fraunhofer_costs": "inputs/Fraunhofer_ISE_costs.csv",
"fraunhofer_energy_prices": "inputs/Fraunhofer_ISE_energy_prices.csv",
"fraunhofer_vehicles_costs": "inputs/Fraunhofer_ISE_vehicles_costs.csv",
"EWG_costs": "inputs/EWG_costs.csv",
"dea_transport": "inputs/energy_transport_data_sheet_dec_2017.xlsx",
"dea_vehicles": "inputs/data_sheets_for_commercial_freight_and_passenger_transport_0.xlsx",
"dea_renewable_fuels": "inputs/data_sheets_for_renewable_fuels.xlsx",
"dea_storage": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"dea_generation": "inputs/technology_data_for_el_and_dh.xlsx",
"dea_heating": "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
"dea_industrial": "inputs/technology_data_for_industrial_process_heat.xlsx",
"dea_ship": "inputs/data_sheets_for_maritime_commercial_freight_and_passenger_transport.xlsx",
"dea_ccts": "inputs/technology_data_for_carbon_capture_transport_storage.xlsx",
"pnnl_energy_storage": "inputs/pnnl-energy-storage-database.xlsx",
"manual_input": "inputs/manual_input.csv",
}
@pytest.mark.parametrize("source", ["", "dea"])
def test_clean_up_units(mock_input_data, mock_output_data, source):
"""
The test verifies what is returned by clean_up_units.
"""
expected_df = mock_output_data(source)
output_df = clean_up_units(
mock_input_data.copy(deep=True), value_column="value", source=source
)
comparison_df = output_df.compare(expected_df)
print(comparison_df)
assert comparison_df.empty
def test_get_excel_sheets():
"""
The test verifies what is returned by get_excel_sheets.
"""
reference_output_dictionary = {
"inputs/energy_transport_data_sheet_dec_2017.xlsx": 16,
"inputs/data_sheets_for_commercial_freight_and_passenger_transport_0.xlsx": 19,
"inputs/data_sheets_for_renewable_fuels.xlsx": 45,
"inputs/technology_data_catalogue_for_energy_storage.xlsx": 15,
"inputs/technology_data_for_el_and_dh.xlsx": 72,
"inputs/technologydatafor_heating_installations_marts_2018.xlsx": 29,
"inputs/technology_data_for_industrial_process_heat.xlsx": 32,
"inputs/data_sheets_for_maritime_commercial_freight_and_passenger_transport.xlsx": 22,
"inputs/technology_data_for_carbon_capture_transport_storage.xlsx": 31,
}
excel_files = [
v for k, v in snakemake_input_dictionary.items() if "dea" in k.casefold()
]
output_dict = get_excel_sheets(excel_files)
comparison_dictionary = {}
for key, value in output_dict.items():
comparison_dictionary[key] = len(value)
assert reference_output_dictionary == comparison_dictionary
def test_get_sheet_location():
"""
The test verifies what is returned by get_sheet_location.
"""
reference_output_dictionary = {
"onwind": "inputs/technology_data_for_el_and_dh.xlsx",
"offwind": "inputs/technology_data_for_el_and_dh.xlsx",
"solar-utility": "inputs/technology_data_for_el_and_dh.xlsx",
"solar-utility single-axis tracking": "inputs/technology_data_for_el_and_dh.xlsx",
"solar-rooftop residential": "inputs/technology_data_for_el_and_dh.xlsx",
"solar-rooftop commercial": "inputs/technology_data_for_el_and_dh.xlsx",
"OCGT": "inputs/technology_data_for_el_and_dh.xlsx",
"CCGT": "inputs/technology_data_for_el_and_dh.xlsx",
"oil": "inputs/technology_data_for_el_and_dh.xlsx",
"biomass CHP": "inputs/technology_data_for_el_and_dh.xlsx",
"biomass EOP": "inputs/technology_data_for_el_and_dh.xlsx",
"biomass HOP": "inputs/technology_data_for_el_and_dh.xlsx",
"central coal CHP": "inputs/technology_data_for_el_and_dh.xlsx",
"central gas CHP": "inputs/technology_data_for_el_and_dh.xlsx",
"central gas CHP CC": "inputs/technology_data_for_el_and_dh.xlsx",
"central solid biomass CHP": "inputs/technology_data_for_el_and_dh.xlsx",
"central solid biomass CHP CC": "inputs/technology_data_for_el_and_dh.xlsx",
"central solid biomass CHP powerboost CC": "inputs/technology_data_for_el_and_dh.xlsx",
"central air-sourced heat pump": "inputs/technology_data_for_el_and_dh.xlsx",
"central geothermal-sourced heat pump": "inputs/technology_data_for_el_and_dh.xlsx",
"central geothermal heat source": "inputs/technology_data_for_el_and_dh.xlsx",
"central excess-heat-sourced heat pump": "inputs/technology_data_for_el_and_dh.xlsx",
"central water-sourced heat pump": "inputs/technology_data_for_el_and_dh.xlsx",
"central ground-sourced heat pump": "inputs/technology_data_for_el_and_dh.xlsx",
"central resistive heater": "inputs/technology_data_for_el_and_dh.xlsx",
"central gas boiler": "inputs/technology_data_for_el_and_dh.xlsx",
"decentral gas boiler": "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
"direct firing gas": "inputs/technology_data_for_industrial_process_heat.xlsx",
"direct firing gas CC": "inputs/technology_data_for_industrial_process_heat.xlsx",
"direct firing solid fuels": "inputs/technology_data_for_industrial_process_heat.xlsx",
"direct firing solid fuels CC": "inputs/technology_data_for_industrial_process_heat.xlsx",
"decentral ground-sourced heat pump": "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
"decentral air-sourced heat pump": "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
"central water pit storage": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"central water tank storage": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"decentral water tank storage": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"fuel cell": "inputs/technology_data_for_el_and_dh.xlsx",
"hydrogen storage underground": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"hydrogen storage tank type 1 including compressor": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"micro CHP": "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
"biogas": "inputs/data_sheets_for_renewable_fuels.xlsx",
"biogas CC": "inputs/data_sheets_for_renewable_fuels.xlsx",
"biogas upgrading": "inputs/data_sheets_for_renewable_fuels.xlsx",
"battery": "inputs/technology_data_catalogue_for_energy_storage.xlsx",
"industrial heat pump medium temperature": "inputs/technology_data_for_industrial_process_heat.xlsx",
"industrial heat pump high temperature": "inputs/technology_data_for_industrial_process_heat.xlsx",
"electric boiler steam": "inputs/technology_data_for_industrial_process_heat.xlsx",
"gas boiler steam": "inputs/technology_data_for_industrial_process_heat.xlsx",
"solid biomass boiler steam": "inputs/technology_data_for_industrial_process_heat.xlsx",
"solid biomass boiler steam CC": "inputs/technology_data_for_industrial_process_heat.xlsx",
"biomass boiler": "inputs/technologydatafor_heating_installations_marts_2018.xlsx",
"electrolysis": "inputs/data_sheets_for_renewable_fuels.xlsx",
"direct air capture": "inputs/technology_data_for_carbon_capture_transport_storage.xlsx",
"biomass CHP capture": "inputs/technology_data_for_carbon_capture_transport_storage.xlsx",
"cement capture": "inputs/technology_data_for_carbon_capture_transport_storage.xlsx",
"BioSNG": "inputs/data_sheets_for_renewable_fuels.xlsx",
"BtL": "inputs/data_sheets_for_renewable_fuels.xlsx",
"biomass-to-methanol": "inputs/data_sheets_for_renewable_fuels.xlsx",
"biogas plus hydrogen": "inputs/data_sheets_for_renewable_fuels.xlsx",
"methanolisation": "inputs/data_sheets_for_renewable_fuels.xlsx",
"Fischer-Tropsch": "inputs/data_sheets_for_renewable_fuels.xlsx",
"central hydrogen CHP": "inputs/technology_data_for_el_and_dh.xlsx",
"Haber-Bosch": "inputs/data_sheets_for_renewable_fuels.xlsx",
"air separation unit": "inputs/data_sheets_for_renewable_fuels.xlsx",
"waste CHP": "inputs/technology_data_for_el_and_dh.xlsx",
"waste CHP CC": "inputs/technology_data_for_el_and_dh.xlsx",
"biochar pyrolysis": "inputs/data_sheets_for_renewable_fuels.xlsx",
"electrolysis small": "inputs/data_sheets_for_renewable_fuels.xlsx",
"random tech": "Sheet not found",
}
dea_sheet_names_dict = copy.deepcopy(dea_sheet_names)
dea_sheet_names_dict["random tech"] = "random sheet"
excel_files = [
v for k, v in snakemake_input_dictionary.items() if "dea" in k.casefold()
]
output_dict = get_excel_sheets(excel_files)
sheet_location_dictionary = {}
for tech, dea_tech in dea_sheet_names_dict.items():
technology_location = get_sheet_location(
tech, dea_sheet_names_dict, output_dict
)
sheet_location_dictionary[tech] = technology_location
assert sheet_location_dictionary == reference_output_dictionary
def test_get_data_from_dea(config):
"""
The test verifies what is returned by get_data_from_DEA.
"""
reference_output_dictionary = {
"onwind": (4, 9),
"offwind": (4, 9),
"solar-utility": (4, 9),
"solar-utility single-axis tracking": (4, 9),
"solar-rooftop residential": (4, 9),
"solar-rooftop commercial": (4, 9),
"OCGT": (8, 9),
"CCGT": (8, 9),
"oil": (8, 9),
"biomass CHP": (13, 9),
"biomass EOP": (13, 9),
"biomass HOP": (9, 9),
"central coal CHP": (7, 9),
"central gas CHP": (8, 9),
"central gas CHP CC": (8, 9),
"central solid biomass CHP": (13, 9),
"central solid biomass CHP CC": (13, 9),
"central solid biomass CHP powerboost CC": (13, 9),
"central air-sourced heat pump": (6, 9),
"central geothermal-sourced heat pump": (8, 9),
"central geothermal heat source": (8, 9),
"central excess-heat-sourced heat pump": (6, 9),
"central water-sourced heat pump": (6, 9),
"central ground-sourced heat pump": (5, 9),
"central resistive heater": (7, 9),
"central gas boiler": (6, 9),
"decentral gas boiler": (8, 9),
"direct firing gas": (7, 9),
"direct firing gas CC": (7, 9),
"direct firing solid fuels": (7, 9),
"direct firing solid fuels CC": (7, 9),
"decentral ground-sourced heat pump": (9, 9),
"decentral air-sourced heat pump": (9, 9),
"central water pit storage": (10, 9),
"central water tank storage": (10, 9),
"decentral water tank storage": (9, 9),
"fuel cell": (8, 9),
"hydrogen storage underground": (10, 9),
"hydrogen storage tank type 1 including compressor": (10, 9),
"micro CHP": (8, 9),
"biogas": (4, 9),
"biogas CC": (4, 9),
"biogas upgrading": (4, 9),
"battery": (13, 9),
"industrial heat pump medium temperature": (6, 9),
"industrial heat pump high temperature": (6, 9),
"electric boiler steam": (7, 9),
"gas boiler steam": (7, 9),
"solid biomass boiler steam": (7, 9),
"solid biomass boiler steam CC": (7, 9),
"biomass boiler": (6, 9),
"electrolysis": (7, 9),
"direct air capture": (8, 9),
"biomass CHP capture": (10, 9),
"cement capture": (10, 9),
"BioSNG": (6, 9),
"BtL": (6, 9),
"biomass-to-methanol": (8, 9),
"biogas plus hydrogen": (6, 9),
"methanolisation": (7, 9),
"Fischer-Tropsch": (6, 9),
"central hydrogen CHP": (8, 9),
"Haber-Bosch": (10, 9),
"air separation unit": (7, 9),
"waste CHP": (16, 9),
"waste CHP CC": (16, 9),
"biochar pyrolysis": (7, 9),
"electrolysis small": (7, 9),
"random tech": (0, 0),
}
excel_files = [
v for k, v in snakemake_input_dictionary.items() if "dea" in k.casefold()
]
input_dea_files_dict = get_excel_sheets(excel_files)
dea_sheet_names_dict = copy.deepcopy(dea_sheet_names)
dea_sheet_names_dict["random tech"] = "random sheet"
output_dictionary = get_data_from_DEA(
config["years"],
dea_sheet_names_dict,
input_dea_files_dict,
expectation=config["expectation"],
)
comparison_dictionary = {}
for key, value in output_dictionary.items():
comparison_dictionary[key] = value.shape
assert comparison_dictionary == reference_output_dictionary
def test_set_specify_assumptions():
"""
The test verifies what is returned by set_specify_assumptions.
"""
input_df = pd.DataFrame(
{
"technology": [
"central resistive heater",
"decentral gas boiler",
"decentral gas boiler",
"decentral gas boiler",
"biogas upgrading",
"biogas upgrading",
"solar-rooftop",
"heat pump",
],
"parameter": [
"Nominal investment, 400/690 V; 1-5 MW",
"Heat efficiency, annual average, net",
"Possible additional specific investment",
"Technical lifetime",
"investment",
"Technical lifetime",
"PV module conversion efficiency [p.u.]",
"Heat efficiency, annual average, net, radiators",
],
"2020": [1.0] * 8,
"source": ["source"] * 8,
"unit": ["unit"] * 8,
}
).set_index(["technology", "parameter"])
reference_output_df = pd.DataFrame(
{
"technology": [
"biogas upgrading",
"biogas upgrading",
"decentral gas boiler",
"decentral gas boiler connection",
"decentral gas boiler connection",
"heat pump",
],
"parameter": [
"Technical lifetime",
"investment (upgrading, methane redution and grid injection)",
"Technical lifetime",
"Possible additional specific investment",
"Technical lifetime",
"Heat efficiency, annual average, net, radiators, existing one family house",
],
"2020": [1.0, 1.0, 1.0, 1.0, 50.0, 1.0],
"source": ["source"] * 6,
"unit": ["unit"] * 6,
}
)
list_of_years = ["2020"]
output_df = set_specify_assumptions(list_of_years, input_df)
output_df = output_df.reset_index(drop=False).rename(
columns={"level_0": "technology", "level_1": "parameter"}
)
comparison_df = output_df.compare(reference_output_df)
assert comparison_df.empty
def test_set_round_trip_efficiency():
"""
The test verifies what is returned by set_round_trip_efficiency.
"""
input_df = pd.DataFrame(
{
"technology": [
"hydrogen storage underground",
"hydrogen storage tank type 1 including compressor",
]
+ ["battery"] * 5,
"parameter": [
"Round trip efficiency",
"Round trip efficiency",
"Round trip efficiency DC",
"Output capacity expansion cost",
"Technical lifetime",
"Fixed O&M",
"Energy storage expansion cost",
],
"2020": [1.0] * 7,
"source": ["source"] * 7,
"unit": ["unit"] * 7,
}
).set_index(["technology", "parameter"])
reference_output_df = pd.DataFrame(
{
"technology": ["battery inverter"] * 4
+ ["battery storage"] * 2
+ [
"hydrogen storage tank type 1 including compressor",
"hydrogen storage underground",
],
"parameter": [
"Fixed O&M",
"Output capacity expansion cost investment",
"Round trip efficiency DC",
"Technical lifetime",
"Energy storage expansion cost investment",
"Technical lifetime",
"Round trip efficiency",
"Round trip efficiency",
],
"2020": [
1.0,
1.0,
1.0,
10.0,
1.0,
1.0,
100.0,
100.0,
],
"source": ["source"] * 3 + ["source, Note K."] + ["source"] * 4,
"unit": ["unit"] * 8,
}
)
list_of_years = ["2020"]
output_df = set_round_trip_efficiency(list_of_years, input_df)
output_df = output_df.reset_index(drop=False).rename(
columns={"level_0": "technology", "level_1": "parameter"}
)
comparison_df = output_df.compare(reference_output_df)
assert comparison_df.empty
def test_add_description():
"""
The test verifies what is returned by add_description.
"""
technology_series = pd.Series(
[
"offwind",
"technology_name_2",
]
)
parameter_series = pd.Series(
[
"investment",
"parameter_name_2",
]
)
input_df = pd.DataFrame(
{
"2020": [1.0, 1.0],
"source": [
"source",
"source",
],
"unit": [
"unit",
"unit",
],
"further description": [
"text",
"text",
],
}
).set_index([technology_series, parameter_series])
reference_output_df = pd.DataFrame(
{
"technology": ["offwind", "technology_name_2"],
"parameter": ["investment", "parameter_name_2"],
"2020": [1.0, 1.0],
"unit": [
"unit",
"unit",
],
"source": [
"source",
"source",
],
"further description": [
"21 Offshore turbines: text grid connection costs subtracted from investment costs",
": text",
],
}
)
list_of_years = ["2020"]
output_df = add_description(list_of_years, input_df).reset_index()
comparison_df = output_df.compare(reference_output_df)
assert comparison_df.empty
def test_convert_units(config):
"""
The test verifies what is returned by convert_units.
"""
technology_series = pd.Series(
[
"technology_name_1",
"technology_name_2",
]
)
parameter_series = pd.Series(
[
"efficiency",
"efficiency-heat",
]
)
input_df = pd.DataFrame(
{
"2020": [100.0, 100.0],
"2025": [100.0, 100.0],
"2030": [100.0, 100.0],
"2035": [100.0, 100.0],
"2040": [100.0, 100.0],
"2045": [100.0, 100.0],
"2050": [100.0, 100.0],
"source": [
"source",
"source",
],
"unit": [
"unit",
"unit",
],
"further description": [
"text",
"text",
],
}
).set_index([technology_series, parameter_series])
reference_output_df = pd.DataFrame(
{
"technology": ["technology_name_1", "technology_name_2"],
"parameter": ["efficiency", "efficiency-heat"],
"2020": [1.0, 1.0],
"2025": [1.0, 1.0],
"2030": [1.0, 1.0],
"2035": [1.0, 1.0],
"2040": [1.0, 1.0],
"2045": [1.0, 1.0],
"2050": [1.0, 1.0],
"source": [
"source",
"source",
],
"unit": [
"per unit",
"per unit",
],
"further description": [
"text",
"text",
],
}
)
list_of_years = [str(x) for x in config["years"]]
output_df = (
convert_units(list_of_years, input_df)
.reset_index()
.rename(columns={"level_0": "technology", "level_1": "parameter"})
)
comparison_df = output_df.compare(reference_output_df)
assert comparison_df.empty
@pytest.mark.parametrize(
"discount_rate_value, expected_annuity", [(0.07, 1.07), (-0.1, 1.0)]
)
def test_annuity(discount_rate_value, expected_annuity):
"""
The test verifies what is returned by annuity.
"""
assert annuity(n=1.0, r=discount_rate_value) == expected_annuity
@pytest.mark.parametrize(
"nom_val, den_val, n_terms, start_val, expected_val",
[(1.0, 6.5, 3, 0, 1.18), (1.0, 2.0, 3, 0, 1.75)],
)
def test_geometric_series(nom_val, den_val, n_terms, start_val, expected_val):
"""
The test verifies what is returned by annuity.
"""
assert (
np.round(geometric_series(nom_val, den_val, n_terms, start_val), 2)
== expected_val
)
def test_prepare_inflation_rate(mock_inflation_data):
"""
The test verifies what is returned by prepare_inflation_rate.
"""
output_series = prepare_inflation_rate(mock_inflation_data).round(decimals=3)
reference_output_series = pd.Series(
[0.02, 0.015, 0.025, 0.018],
index=[2001, 2002, 2003, 2004],
name="European Union - 27 countries (from 2020)",
)
comparison_series = output_series.compare(reference_output_series)
assert comparison_series.size == 0
def test_add_gas_storage(config):
"""
The test verifies what is returned by add_gas_storage.
"""
input_file_path = pathlib.Path(
path_cwd, "inputs", "technology_data_catalogue_for_energy_storage.xlsx"
)
list_of_years = ["2020"]
technology_series = pd.Series(
[
"gas_storage",
"gas_storage",
"gas storage charger",
"gas storage discharger",
"gas_storage",
]
)
parameter_series = pd.Series(
[
"investment",
"lifetime",
"investment",
"investment",
"FOM",
]
)
technology_dataframe = pd.DataFrame(
{
"2020": [np.nan] * 5,
}
).set_index([technology_series, parameter_series])
output_df = add_gas_storage(input_file_path, list_of_years, technology_dataframe)
cleaned_df = (
output_df.dropna(how="all")
.reset_index(drop=False)
.rename(columns={"level_0": "technology", "level_1": "parameter"})
)
reference_output_df = pd.DataFrame(
{
"technology": [
"gas storage charger",
"gas storage discharger",
"gas storage",
"gas storage",
"gas storage",
],
"parameter": ["investment"] * 3 + ["lifetime", "FOM"],
"2020": [
14.33885157711495,
4.77961719237165,
0.03290254335105841,
100.0,
3.5918748566291763,
],
"source": ["Danish Energy Agency"] * 3
+ ["TODO no source"]
+ ["Danish Energy Agency"],
"further description": [
"150 Underground Storage of Gas, Process equipment (units converted)"
]
* 2
+ [
"150 Underground Storage of Gas, Establishment of one cavern (units converted)"
]
+ [
"estimation: most underground storage are already build, they do have a long lifetime"
]
+ [
"150 Underground Storage of Gas, Operation and Maintenance, salt cavern (units converted)"
],
"unit": ["EUR/kW"] * 2 + ["EUR/kWh"] + ["years", "%"],
"currency_year": [2015.0, np.nan, 2015.0, np.nan, np.nan],
}
)
comparison_df = cleaned_df.compare(reference_output_df)
assert comparison_df.empty
def test_add_carbon_capture(config):
"""
The test verifies what is returned by add_carbon_capture.
"""
list_of_years = ["2020"]
technology_series = pd.Series(
["cement capture"] * 9
+ ["biomass CHP capture"] * 9
+ ["direct air capture"] * 9,
)
input_parameter_series = pd.Series(
[
"Ax) CO2 capture rate, net",
"Specific investment",
"Fixed O&M",
"C2) Eletricity input ",
"C1) Heat input ",
"C1) Heat out ",
"CO₂ compression and dehydration - Electricity input",
"CO₂ compression and dehydration - Heat out",
"lifetime",
]
* 3,
)
technology_dataframe = pd.DataFrame(
{
"2020": [50, 100, 10, 40, 90, 9, 30, 80, 10] * 3,
"source": ["source"] * 27,
}
).set_index([technology_series, input_parameter_series])
output_parameter_series = pd.Series(
[
"capture_rate",
"investment",
"FOM",
"electricity-input",
"heat-input",
"heat-output",
"compression-electricity-input",
"compression-heat-output",
"lifetime",
]
* 3,
)
new_technology_dataframe = pd.DataFrame(
{
"2020": [np.nan] * 27,
"source": ["source"] * 27,
}
).set_index([technology_series, output_parameter_series])
output_df = add_carbon_capture(
list_of_years, dea_sheet_names, new_technology_dataframe, technology_dataframe
)
assert output_df.loc["cement capture", "capture_rate"].equals(
pd.Series(
[0.5, "source", "per unit", "401.c Post comb - Cement kiln"],
name="(cement capture, capture_rate)",
index=["2020", "source", "unit", "further description"],
)
)
assert output_df.loc["cement capture", "FOM"].equals(
pd.Series(
[10.0, "source", "%/year", "401.c Post comb - Cement kiln"],
name="(cement capture, FOM)",
index=["2020", "source", "unit", "further description"],
)
)
assert output_df.loc["cement capture", "compression-heat-output"].equals(
pd.Series(
[80.0, "source", "MWh/tCO2", "401.c Post comb - Cement kiln"],
name="(cement capture, compression-heat-output)",
index=["2020", "source", "unit", "further description"],
)
)
assert output_df.loc["cement capture", "lifetime"].equals(
pd.Series(
[np.nan, "source", np.nan, "401.c Post comb - Cement kiln"],
name="(cement capture, lifetime)",
index=["2020", "source", "unit", "further description"],
)
)
assert output_df.loc["biomass CHP capture", "electricity-input"].equals(
pd.Series(
[40.0, "source", "MWh/tCO2", "401.a Post comb - small CHP"],
name="(cement capture, lifetime)",
index=["2020", "source", "unit", "further description"],
)
)
assert output_df.loc["direct air capture", "investment"].equals(
pd.Series(
[100000000.0, "source", "EUR/(tCO2/h)", "403.a Direct air capture"],
name="(cement capture, lifetime)",
index=["2020", "source", "unit", "further description"],
)
)