Main POSTED tutorial for python¶

Prerequisits¶

Dependencies¶

First, we import some general-purpose libraries. The python-side of posted depends on pandas for working with dataframes. Here we also use plotly and itables for plotting and inspecting data, but posted does not depend on those and other tools could be used instead. The package igraph is an optional dependency used for representing the interlinkages in value chains. The package matplotlib is only used for plotting igraphs, which is again optional.

In [1]:

import pandas as pd

import plotly
pd.options.plotting.backend = "plotly"

# for process chains only
import igraph as ig
import matplotlib.pyplot as plt

import pandas as pd import plotly pd.options.plotting.backend = "plotly" # for process chains only import igraph as ig import matplotlib.pyplot as plt

Importing POSTED¶

The posted package has to be installed in the python environment. If it is not installed yet, you can easily install it from the GitHub source code using pip.

In [2]:

try:
    import posted
except ImportError:
    ! pip install git+https://github.com:PhilippVerpoort/posted.git@develop

try: import posted except ImportError: ! pip install git+https://github.com:PhilippVerpoort/posted.git@develop

Import specific functions and classes from POSTED that will be used later.

In [3]:

from posted.tedf import TEDF
from posted.noslag import DataSet
from posted.units import Q, U
from posted.team import CalcVariable, LCOX, FSCP, ProcessChain, annuity_factor

from posted.tedf import TEDF from posted.noslag import DataSet from posted.units import Q, U from posted.team import CalcVariable, LCOX, FSCP, ProcessChain, annuity_factor

Use some basic plotly and pandas functions for plotting and output analysis

NOSLAG¶

Electrolysis CAPEX¶

Let's compare CAPEX data for electrolysis in years 2020–2050 for Alkaline and PEM across different sources (Danish Energy Agency, Breyer, Fraunhofer, IRENA) for different electrolyser plant sizes.

In [4]:

# select data from TEDFs
df_elh2 = DataSet('Tech|Electrolysis').select(
        period=[2020, 2030, 2040, 2050],
        subtech=['AEL', 'PEM'],
        override={'Tech|Electrolysis|Output Capacity|Hydrogen': 'kW;LHV'},
        source=['DEARF23', 'Vartiainen22', 'Holst21', 'IRENA22'],
        size=['1 MW', '5 MW', '100 MW'],
        extrapolate_period=False
    ).query(f"variable=='Tech|Electrolysis|CAPEX'")

# display a few examples
display(df_elh2.sample(15).sort_index())

# sort data and plot
df_elh2.assign(size_sort=lambda df: df['size'].str.split(' ', n=1, expand=True).iloc[:, 0].astype(int)) \
       .sort_values(by=['size_sort', 'period']) \
       .plot.line(x='period', y='value', color='source', facet_col='size', facet_row='subtech')

# select data from TEDFs df_elh2 = DataSet('Tech|Electrolysis').select( period=[2020, 2030, 2040, 2050], subtech=['AEL', 'PEM'], override={'Tech|Electrolysis|Output Capacity|Hydrogen': 'kW;LHV'}, source=['DEARF23', 'Vartiainen22', 'Holst21', 'IRENA22'], size=['1 MW', '5 MW', '100 MW'], extrapolate_period=False ).query(f"variable=='Tech|Electrolysis|CAPEX'") # display a few examples display(df_elh2.sample(15).sort_index()) # sort data and plot df_elh2.assign(size_sort=lambda df: df['size'].str.split(' ', n=1, expand=True).iloc[:, 0].astype(int)) \ .sort_values(by=['size_sort', 'period']) \ .plot.line(x='period', y='value', color='source', facet_col='size', facet_row='subtech')

	subtech	size	source	variable	reference_variable	region	period	unit	value
0	AEL	1 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2020	USD_2005/kW	1121.0
1	AEL	1 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2030	USD_2005/kW	833.4
37	AEL	100 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2020	USD_2005/kW	972.3
38	AEL	100 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2030	USD_2005/kW	658.0
53	AEL	100 MW	Holst21	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2020	USD_2005/kW	955.4
54	AEL	100 MW	Holst21	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2030	USD_2005/kW	604.0
64	AEL	100 MW	IRENA22	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2050	USD_2005/kW	236.2
73	AEL	100 MW	Vartiainen22	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2030	USD_2005/kW	340.9
74	AEL	100 MW	Vartiainen22	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2040	USD_2005/kW	190.7
96	AEL	5 MW	Holst21	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2020	USD_2005/kW	1367.0
107	AEL	5 MW	IRENA22	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2050	USD_2005/kW	236.2
119	PEM	1 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2020	USD_2005/kW	1586.0
121	PEM	1 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2040	USD_2005/kW	658.0
150	PEM	100 MW	DEARF23	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2020	USD_2005/kW	1586.0
167	PEM	100 MW	Holst21	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Output Capacity|Hydrogen	World	2030	USD_2005/kW	684.4

Based on those many sources and cases (size and subtechnology), we can now aggregate the data for further use.

In [5]:

DataSet('Tech|Electrolysis').aggregate(
        period=[2020, 2030, 2040, 2050],
        subtech=['AEL', 'PEM'],
        override={'Tech|Electrolysis|Output Capacity|Hydrogen': 'kW;LHV'},
        source=['DEARF23', 'Vartiainen22', 'Holst21', 'IRENA22'],
        size=['1 MW', '5 MW', '100 MW'],
        agg=['subtech', 'size', 'source'],
        extrapolate_period=False,
    ).team.varsplit('Tech|Electrolysis|*variable') \
    .query(f"variable.isin({['CAPEX', 'Output Capacity|Hydrogen']})")

DataSet('Tech|Electrolysis').aggregate( period=[2020, 2030, 2040, 2050], subtech=['AEL', 'PEM'], override={'Tech|Electrolysis|Output Capacity|Hydrogen': 'kW;LHV'}, source=['DEARF23', 'Vartiainen22', 'Holst21', 'IRENA22'], size=['1 MW', '5 MW', '100 MW'], agg=['subtech', 'size', 'source'], extrapolate_period=False, ).team.varsplit('Tech|Electrolysis|*variable') \ .query(f"variable.isin({['CAPEX', 'Output Capacity|Hydrogen']})")

Out[5]:

	variable	region	period	unit	value
0	CAPEX	World	2020	USD_2005	1046.0
1	CAPEX	World	2030	USD_2005	737.4
2	CAPEX	World	2040	USD_2005	586.1
3	CAPEX	World	2050	USD_2005	499.3
12	Output Capacity|Hydrogen	World	2020	kW	1.0
13	Output Capacity|Hydrogen	World	2030	kW	1.0
14	Output Capacity|Hydrogen	World	2040	kW	1.0
15	Output Capacity|Hydrogen	World	2050	kW	1.0

Energy demand of green vs. blue hydrogen production¶

Next, let's compare the energy demand of methane reforming (for blue hydrogen) and different types of electrolysis (for green hydrogen).

In [6]:

pd.concat([
        DataSet('Tech|Methane Reforming').aggregate(period=2030, source='Lewis22'),
        DataSet('Tech|Electrolysis').aggregate(period=2030, agg=['source', 'size']),
    ]) \
    .reset_index(drop=True) \
    .team.varsplit('Tech|?tech|Input|?fuel') \
    .assign(tech=lambda df: df.apply(lambda row: f"{row['tech']}<br>({row['subtech']})" if pd.isnull(row['capture_rate']) else f"{row['tech']}<br>({row['subtech']}, {row['capture_rate']} CR)", axis=1)) \
    .plot.bar(x='tech', y='value', color='fuel') \
    .update_layout(
        xaxis_title='Technologies',
        yaxis_title='Energy demand  ( MWh<sub>LHV</sub> / MWh<sub>LHV</sub> H<sub>2</sub> )',
        legend_title='Energy carriers',
    )

pd.concat([ DataSet('Tech|Methane Reforming').aggregate(period=2030, source='Lewis22'), DataSet('Tech|Electrolysis').aggregate(period=2030, agg=['source', 'size']), ]) \ .reset_index(drop=True) \ .team.varsplit('Tech|?tech|Input|?fuel') \ .assign(tech=lambda df: df.apply(lambda row: f"{row['tech']}
({row['subtech']})" if pd.isnull(row['capture_rate']) else f"{row['tech']}
({row['subtech']}, {row['capture_rate']} CR)", axis=1)) \ .plot.bar(x='tech', y='value', color='fuel') \ .update_layout( xaxis_title='Technologies', yaxis_title='Energy demand ( MWh_LHV / MWh_LHV H₂ )', legend_title='Energy carriers', )

Energy demand of iron direct reduction¶

Next, let's compare the energy demand of iron direct reduction (production of low-carbon crude iron) across sources.

In [7]:

DataSet('Tech|Iron Direct Reduction') \
    .aggregate(period=2030, mode='h2', agg=[]) \
    .team.varsplit('Tech|Iron Direct Reduction|Input|?fuel') \
    .query(f"fuel != 'Iron Ore'") \
    .team.varcombine('{fuel} ({component})') \
    .plot.bar(x='source', y='value', color='variable') \
    .update_layout(
        xaxis_title='Sources',
        yaxis_title='Energy demand  ( MWh<sub>LHV</sub> / t<sub>DRI</sub> )',
        legend_title='Energy carriers'
    )

DataSet('Tech|Iron Direct Reduction') \ .aggregate(period=2030, mode='h2', agg=[]) \ .team.varsplit('Tech|Iron Direct Reduction|Input|?fuel') \ .query(f"fuel != 'Iron Ore'") \ .team.varcombine('{fuel} ({component})') \ .plot.bar(x='source', y='value', color='variable') \ .update_layout( xaxis_title='Sources', yaxis_title='Energy demand ( MWh_LHV / t_DRI )', legend_title='Energy carriers' )

We can also compare the energy demand for operation with hydrogen or with fossil gas for only one source.

In [8]:

DataSet('Tech|Iron Direct Reduction') \
    .select(period=2030, source='Jacobasch21') \
    .team.varsplit('Tech|Iron Direct Reduction|Input|?fuel') \
    .query(f"fuel.isin({['Electricity', 'Fossil Gas', 'Hydrogen']})") \
    .plot.bar(x='mode', y='value', color='fuel') \
    .update_layout(
        xaxis_title='Mode of operation',
        yaxis_title='Energy demand  ( MWh<sub>LHV</sub> / t<sub>DRI</sub> )',
        legend_title='Energy carriers'
    )

DataSet('Tech|Iron Direct Reduction') \ .select(period=2030, source='Jacobasch21') \ .team.varsplit('Tech|Iron Direct Reduction|Input|?fuel') \ .query(f"fuel.isin({['Electricity', 'Fossil Gas', 'Hydrogen']})") \ .plot.bar(x='mode', y='value', color='fuel') \ .update_layout( xaxis_title='Mode of operation', yaxis_title='Energy demand ( MWh_LHV / t_DRI )', legend_title='Energy carriers' )

Energy demand of Haber-Bosch synthesis¶

Finally, let's compare the energy demand of Haber-Bosch synthesis between an integrated SMR plant and a plant running on green hydrogen.

In [9]:

pd.concat([
        DataSet('Tech|Haber-Bosch with ASU').aggregate(period=2024, agg='component'),
        DataSet('Tech|Haber-Bosch with Reforming').aggregate(period=2024, agg='component')
    ]) \
    .reset_index(drop=True) \
    .team.varsplit('Tech|?tech|*variable') \
    .query(f"variable.str.startswith('Input|')") \
    .plot.bar(x='source', y='value', color='variable') \
    .update_layout(
        xaxis_title='Sources',
        yaxis_title='Energy demand  ( MWh<sub>LHV</sub> / t<sub>NH<sub>3</sub></sub> )',
        legend_title='Energy carriers'
    )

pd.concat([ DataSet('Tech|Haber-Bosch with ASU').aggregate(period=2024, agg='component'), DataSet('Tech|Haber-Bosch with Reforming').aggregate(period=2024, agg='component') ]) \ .reset_index(drop=True) \ .team.varsplit('Tech|?tech|*variable') \ .query(f"variable.str.startswith('Input|')") \ .plot.bar(x='source', y='value', color='variable') \ .update_layout( xaxis_title='Sources', yaxis_title='Energy demand ( MWh_LHV / t_NH3 )', legend_title='Energy carriers' )

TEAM¶

CalcVariable¶

New variables can be calculated manually via the CalcVariable class. The next example demonstrates this for calculating the levelised cost of hydrogen.

In [10]:

assumptions = pd.DataFrame.from_records([
    {'elec_price_case': f"Case {i}", 'variable': 'Price|Electricity', 'unit': 'EUR_2020/MWh', 'value': 30 + (i-1)*25}
    for i in range(1, 4)
] + [
    {'variable': 'Tech|Electrolysis|OCF', 'value': 50, 'unit': 'pct'},
    {'variable': 'Annuity Factor', 'value': annuity_factor(Q('5 pct'), Q('18 a')).m, 'unit': '1/a'},
])
display(assumptions)

assumptions = pd.DataFrame.from_records([ {'elec_price_case': f"Case {i}", 'variable': 'Price|Electricity', 'unit': 'EUR_2020/MWh', 'value': 30 + (i-1)*25} for i in range(1, 4) ] + [ {'variable': 'Tech|Electrolysis|OCF', 'value': 50, 'unit': 'pct'}, {'variable': 'Annuity Factor', 'value': annuity_factor(Q('5 pct'), Q('18 a')).m, 'unit': '1/a'}, ]) display(assumptions)

	elec_price_case	variable	unit	value
0	Case 1	Price|Electricity	EUR_2020/MWh	30.000000
1	Case 2	Price|Electricity	EUR_2020/MWh	55.000000
2	Case 3	Price|Electricity	EUR_2020/MWh	80.000000
3	NaN	Tech|Electrolysis|OCF	pct	50.000000
4	NaN	Annuity Factor	1/a	0.085546

In [11]:

df_calc = pd.concat([
        DataSet('Tech|Electrolysis').aggregate(period=[2030, 2040, 2050], subtech=['AEL', 'PEM'], agg=['size', 'source']),
        assumptions,
    ]).team.perform(CalcVariable(**{
        'LCOX|Green Hydrogen|Capital Cost': lambda x: (x['Annuity Factor'] * x['Tech|Electrolysis|CAPEX'] / x['Tech|Electrolysis|Output Capacity|Hydrogen'] / x['Tech|Electrolysis|OCF']),
        'LCOX|Green Hydrogen|OM Cost Fixed': lambda x: x['Tech|Electrolysis|OPEX Fixed'] / x['Tech|Electrolysis|Output Capacity|Hydrogen'] / x['Tech|Electrolysis|OCF'],
        'LCOX|Green Hydrogen|Input Cost|Electricity': lambda x: x['Price|Electricity'] * x['Tech|Electrolysis|Input|Electricity'] / x['Tech|Electrolysis|Output|Hydrogen'],
    }), only_new=True) \
    .team.unit_convert(to='EUR_2020/MWh')

display(df_calc.sample(15).sort_index())

df_calc = pd.concat([ DataSet('Tech|Electrolysis').aggregate(period=[2030, 2040, 2050], subtech=['AEL', 'PEM'], agg=['size', 'source']), assumptions, ]).team.perform(CalcVariable(**{ 'LCOX|Green Hydrogen|Capital Cost': lambda x: (x['Annuity Factor'] * x['Tech|Electrolysis|CAPEX'] / x['Tech|Electrolysis|Output Capacity|Hydrogen'] / x['Tech|Electrolysis|OCF']), 'LCOX|Green Hydrogen|OM Cost Fixed': lambda x: x['Tech|Electrolysis|OPEX Fixed'] / x['Tech|Electrolysis|Output Capacity|Hydrogen'] / x['Tech|Electrolysis|OCF'], 'LCOX|Green Hydrogen|Input Cost|Electricity': lambda x: x['Price|Electricity'] * x['Tech|Electrolysis|Input|Electricity'] / x['Tech|Electrolysis|Output|Hydrogen'], }), only_new=True) \ .team.unit_convert(to='EUR_2020/MWh') display(df_calc.sample(15).sort_index())

	subtech	region	period	elec_price_case	variable	value	unit
1	AEL	World	2030.0	Case 2	LCOX|Green Hydrogen|Capital Cost	12.824046	EUR_2020/MWh
10	PEM	World	2030.0	Case 2	LCOX|Green Hydrogen|Capital Cost	19.839159	EUR_2020/MWh
12	PEM	World	2040.0	Case 1	LCOX|Green Hydrogen|Capital Cost	17.258175	EUR_2020/MWh
13	PEM	World	2040.0	Case 2	LCOX|Green Hydrogen|Capital Cost	17.258175	EUR_2020/MWh
19	AEL	World	2030.0	Case 2	LCOX|Green Hydrogen|OM Cost Fixed	5.247678	EUR_2020/MWh
21	AEL	World	2040.0	Case 1	LCOX|Green Hydrogen|OM Cost Fixed	4.887642	EUR_2020/MWh
22	AEL	World	2040.0	Case 2	LCOX|Green Hydrogen|OM Cost Fixed	4.887642	EUR_2020/MWh
25	AEL	World	2050.0	Case 2	LCOX|Green Hydrogen|OM Cost Fixed	4.670413	EUR_2020/MWh
31	PEM	World	2040.0	Case 2	LCOX|Green Hydrogen|OM Cost Fixed	5.901375	EUR_2020/MWh
37	AEL	World	2030.0	Case 2	LCOX|Green Hydrogen|Input Cost|Electricity	82.170000	EUR_2020/MWh
39	AEL	World	2040.0	Case 1	LCOX|Green Hydrogen|Input Cost|Electricity	43.830000	EUR_2020/MWh
43	AEL	World	2050.0	Case 2	LCOX|Green Hydrogen|Input Cost|Electricity	78.760000	EUR_2020/MWh
45	PEM	World	2030.0	Case 1	LCOX|Green Hydrogen|Input Cost|Electricity	45.690000	EUR_2020/MWh
50	PEM	World	2040.0	Case 3	LCOX|Green Hydrogen|Input Cost|Electricity	120.560000	EUR_2020/MWh
52	PEM	World	2050.0	Case 2	LCOX|Green Hydrogen|Input Cost|Electricity	81.950000	EUR_2020/MWh

In [12]:

df_calc.team.varsplit('LCOX|Green Hydrogen|?component') \
    .sort_values(by=['elec_price_case', 'value']) \
    .plot.bar(x='period', y='value', color='component', facet_col='elec_price_case', facet_row='subtech')

df_calc.team.varsplit('LCOX|Green Hydrogen|?component') \ .sort_values(by=['elec_price_case', 'value']) \ .plot.bar(x='period', y='value', color='component', facet_col='elec_price_case', facet_row='subtech')

Pivot¶

POSTED uses the pivot dataframe method to bring the data into a usable format.

In [13]:

pd.concat([
        DataSet('Tech|Electrolysis').aggregate(period=[2030, 2040, 2050], subtech=['AEL', 'PEM'], agg=['size', 'source']),
        assumptions,
    ]).team.pivot_wide().pint.dequantify()

pd.concat([ DataSet('Tech|Electrolysis').aggregate(period=[2030, 2040, 2050], subtech=['AEL', 'PEM'], agg=['size', 'source']), assumptions, ]).team.pivot_wide().pint.dequantify()

Out[13]:

			variable	Tech|Electrolysis|CAPEX	Tech|Electrolysis|Input|Electricity	Tech|Electrolysis|Input|Water	Tech|Electrolysis|OPEX Fixed	Tech|Electrolysis|Output Capacity|Hydrogen	Tech|Electrolysis|Output|Hydrogen	Tech|Electrolysis|Total Input Capacity|Electricity	Tech|Electrolysis|Total Output Capacity|Hydrogen	Price|Electricity	Tech|Electrolysis|OCF	Annuity Factor
			unit	USD_2005	MWh	t	USD_2005/a	MWh/a	MWh	MWh/a	MWh/a	EUR_2020/MWh	pct	1/a
subtech	region	period	elec_price_case
AEL	World	2030.0	Case 1	74.53	1.494	0.2848	2.609	1.0	1.0	309500.0	3179000.0	30.0	50.0	0.085546
			Case 2	74.53	1.494	0.2848	2.609	1.0	1.0	309500.0	3179000.0	55.0	50.0	0.085546
			Case 3	74.53	1.494	0.2848	2.609	1.0	1.0	309500.0	3179000.0	80.0	50.0	0.085546
		2040.0	Case 1	59.19	1.461	0.2848	2.430	1.0	1.0	309500.0	3179000.0	30.0	50.0	0.085546
			Case 2	59.19	1.461	0.2848	2.430	1.0	1.0	309500.0	3179000.0	55.0	50.0	0.085546
			Case 3	59.19	1.461	0.2848	2.430	1.0	1.0	309500.0	3179000.0	80.0	50.0	0.085546
		2050.0	Case 1	47.46	1.432	0.2848	2.322	1.0	1.0	309500.0	3179000.0	30.0	50.0	0.085546
			Case 2	47.46	1.432	0.2848	2.322	1.0	1.0	309500.0	3179000.0	55.0	50.0	0.085546
			Case 3	47.46	1.432	0.2848	2.322	1.0	1.0	309500.0	3179000.0	80.0	50.0	0.085546
PEM	World	2030.0	Case 1	115.30	1.523	0.2850	3.621	1.0	1.0	309500.0	3179000.0	30.0	50.0	0.085546
			Case 2	115.30	1.523	0.2850	3.621	1.0	1.0	309500.0	3179000.0	55.0	50.0	0.085546
			Case 3	115.30	1.523	0.2850	3.621	1.0	1.0	309500.0	3179000.0	80.0	50.0	0.085546
		2040.0	Case 1	100.30	1.507	0.2850	2.934	1.0	1.0	309500.0	3179000.0	30.0	50.0	0.085546
			Case 2	100.30	1.507	0.2850	2.934	1.0	1.0	309500.0	3179000.0	55.0	50.0	0.085546
			Case 3	100.30	1.507	0.2850	2.934	1.0	1.0	309500.0	3179000.0	80.0	50.0	0.085546
		2050.0	Case 1	96.00	1.490	0.2850	2.737	1.0	1.0	309500.0	3179000.0	30.0	50.0	0.085546
			Case 2	96.00	1.490	0.2850	2.737	1.0	1.0	309500.0	3179000.0	55.0	50.0	0.085546
			Case 3	96.00	1.490	0.2850	2.737	1.0	1.0	309500.0	3179000.0	80.0	50.0	0.085546

LCOX of blue and green hydrogen¶

POSTED also contains predefined methods for calculating LCOX. Here we apply it to blue and green hydrogen.

In [14]:

df_lcox_bluegreen = pd.concat([
        pd.DataFrame.from_records([
            {'elec_price_case': f"Case {i}", 'variable': 'Price|Electricity', 'unit': 'EUR_2020/MWh', 'value': 30 + (i-1)*25}
            for i in range(1, 4)
        ]),
        pd.DataFrame.from_records([
            {'ng_price_case': 'High' if i-1 else 'Low', 'variable': 'Price|Fossil Gas', 'unit': 'EUR_2020/MWh', 'value': 40 if i-1 else 20}
            for i in range(1, 3)
        ]),
        DataSet('Tech|Electrolysis').aggregate(period=2030, subtech=['AEL', 'PEM'], agg=['size', 'subtech', 'source']),
        DataSet('Tech|Methane Reforming').aggregate(period=2030, capture_rate=['55.70%', '94.50%'])
            .team.varsplit('Tech|Methane Reforming|*comp')
            .team.varcombine('{variable} {subtech} ({capture_rate})|{comp}')
    ]) \
    .team.perform(
        LCOX('Output|Hydrogen', 'Electrolysis', name='Green Hydrogen', interest_rate=0.1, book_lifetime=18),
        LCOX('Output|Hydrogen', 'Methane Reforming SMR (55.70%)', name='Blue Hydrogen (Low CR)', interest_rate=0.1, book_lifetime=18),
        LCOX('Output|Hydrogen', 'Methane Reforming ATR (94.50%)', name='Blue Hydrogen (High CR)', interest_rate=0.1, book_lifetime=18),
        only_new=True,
    ) \
    .team.unit_convert(to='EUR_2022/MWh')

display(df_lcox_bluegreen)

df_lcox_bluegreen = pd.concat([ pd.DataFrame.from_records([ {'elec_price_case': f"Case {i}", 'variable': 'Price|Electricity', 'unit': 'EUR_2020/MWh', 'value': 30 + (i-1)*25} for i in range(1, 4) ]), pd.DataFrame.from_records([ {'ng_price_case': 'High' if i-1 else 'Low', 'variable': 'Price|Fossil Gas', 'unit': 'EUR_2020/MWh', 'value': 40 if i-1 else 20} for i in range(1, 3) ]), DataSet('Tech|Electrolysis').aggregate(period=2030, subtech=['AEL', 'PEM'], agg=['size', 'subtech', 'source']), DataSet('Tech|Methane Reforming').aggregate(period=2030, capture_rate=['55.70%', '94.50%']) .team.varsplit('Tech|Methane Reforming|*comp') .team.varcombine('{variable} {subtech} ({capture_rate})|{comp}') ]) \ .team.perform( LCOX('Output|Hydrogen', 'Electrolysis', name='Green Hydrogen', interest_rate=0.1, book_lifetime=18), LCOX('Output|Hydrogen', 'Methane Reforming SMR (55.70%)', name='Blue Hydrogen (Low CR)', interest_rate=0.1, book_lifetime=18), LCOX('Output|Hydrogen', 'Methane Reforming ATR (94.50%)', name='Blue Hydrogen (High CR)', interest_rate=0.1, book_lifetime=18), only_new=True, ) \ .team.unit_convert(to='EUR_2022/MWh') display(df_lcox_bluegreen)

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Water

	elec_price_case	ng_price_case	region	period	variable	value	unit
0	Case 1	High	World	2030.0	LCOX|Green Hydrogen|Capital	11.746556	EUR_2022/MWh
1	Case 1	Low	World	2030.0	LCOX|Green Hydrogen|Capital	11.746556	EUR_2022/MWh
2	Case 2	High	World	2030.0	LCOX|Green Hydrogen|Capital	11.746556	EUR_2022/MWh
3	Case 2	Low	World	2030.0	LCOX|Green Hydrogen|Capital	11.746556	EUR_2022/MWh
4	Case 3	High	World	2030.0	LCOX|Green Hydrogen|Capital	11.746556	EUR_2022/MWh
...	...	...	...	...	...	...	...
73	Case 1	Low	World	2030.0	LCOX|Blue Hydrogen (High CR)|Input Cost|Fossil...	30.112121	EUR_2022/MWh
74	Case 2	High	World	2030.0	LCOX|Blue Hydrogen (High CR)|Input Cost|Fossil...	60.224242	EUR_2022/MWh
75	Case 2	Low	World	2030.0	LCOX|Blue Hydrogen (High CR)|Input Cost|Fossil...	30.112121	EUR_2022/MWh
76	Case 3	High	World	2030.0	LCOX|Blue Hydrogen (High CR)|Input Cost|Fossil...	60.224242	EUR_2022/MWh
77	Case 3	Low	World	2030.0	LCOX|Blue Hydrogen (High CR)|Input Cost|Fossil...	30.112121	EUR_2022/MWh

78 rows × 7 columns

In [15]:

df_lcox_bluegreen.team.varsplit('LCOX|?fuel|*comp') \
    .plot.bar(x='fuel', y='value', color='comp', facet_col='elec_price_case', facet_row='ng_price_case')

df_lcox_bluegreen.team.varsplit('LCOX|?fuel|*comp') \ .plot.bar(x='fuel', y='value', color='comp', facet_col='elec_price_case', facet_row='ng_price_case')

LCOX of methanol¶

Let's calculate the levelised cost of green methanol (from electrolytic hydrogen). First we can do this simply based on a hydrogen price (i.e. without accounting for electrolysis).

In [16]:

df_lcox_meoh = pd.concat([
        DataSet('Tech|Methanol Synthesis').aggregate(period=[2030, 2050]),
        pd.DataFrame.from_records([
            {'period': 2030, 'variable': 'Price|Hydrogen', 'unit': 'EUR_2022/MWh', 'value': 120},
            {'period': 2050, 'variable': 'Price|Hydrogen', 'unit': 'EUR_2022/MWh', 'value': 80},
            {'period': 2030, 'variable': 'Price|Captured CO2', 'unit': 'EUR_2022/t', 'value': 150},
            {'period': 2050, 'variable': 'Price|Captured CO2', 'unit': 'EUR_2022/t', 'value': 100},
        ]),
    ]) \
    .team.perform(LCOX(
        'Output|Methanol', 'Methanol Synthesis', name='Green Methanol',
        interest_rate=0.1, book_lifetime=10.0), only_new=True,
    ) \
    .team.unit_convert('EUR_2022/MWh')

display(df_lcox_meoh)

df_lcox_meoh = pd.concat([ DataSet('Tech|Methanol Synthesis').aggregate(period=[2030, 2050]), pd.DataFrame.from_records([ {'period': 2030, 'variable': 'Price|Hydrogen', 'unit': 'EUR_2022/MWh', 'value': 120}, {'period': 2050, 'variable': 'Price|Hydrogen', 'unit': 'EUR_2022/MWh', 'value': 80}, {'period': 2030, 'variable': 'Price|Captured CO2', 'unit': 'EUR_2022/t', 'value': 150}, {'period': 2050, 'variable': 'Price|Captured CO2', 'unit': 'EUR_2022/t', 'value': 100}, ]), ]) \ .team.perform(LCOX( 'Output|Methanol', 'Methanol Synthesis', name='Green Methanol', interest_rate=0.1, book_lifetime=10.0), only_new=True, ) \ .team.unit_convert('EUR_2022/MWh') display(df_lcox_meoh)

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/noslag.py:368: UserWarning:

Unknown variable, so dropping rows:
36    Emissions|CO2
37    Emissions|CO2
Name: variable, dtype: object

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Output|Heat

	region	period	variable	value	unit
0	World	2030	LCOX|Green Methanol|Capital	10.583450	EUR_2022/MWh
1	World	2050	LCOX|Green Methanol|Capital	9.831386	EUR_2022/MWh
2	World	2030	LCOX|Green Methanol|OM Fixed	3.231184	EUR_2022/MWh
3	World	2050	LCOX|Green Methanol|OM Fixed	2.984565	EUR_2022/MWh
4	World	2030	LCOX|Green Methanol|OM Variable	3.385836	EUR_2022/MWh
5	World	2050	LCOX|Green Methanol|OM Variable	3.385836	EUR_2022/MWh
6	World	2030	LCOX|Green Methanol|Input Cost|Captured CO2	38.355000	EUR_2022/MWh
7	World	2050	LCOX|Green Methanol|Input Cost|Captured CO2	25.570000	EUR_2022/MWh
8	World	2030	LCOX|Green Methanol|Input Cost|Hydrogen	140.760000	EUR_2022/MWh
9	World	2050	LCOX|Green Methanol|Input Cost|Hydrogen	93.840000	EUR_2022/MWh

In [17]:

df_lcox_meoh.team.varsplit('LCOX|Green Methanol|*component') \
    .plot.bar(x='period', y='value', color='component')

df_lcox_meoh.team.varsplit('LCOX|Green Methanol|*component') \ .plot.bar(x='period', y='value', color='component')

Next, we can calculate the LCOX of green methanol for a the value chain consisting of electrolysis, low-temperature direct air capture, and methanol synthesis. The heat for the direct air capture will be provided by an industrial heat pump.

In [18]:

pc_green_meoh = ProcessChain(
    'Green Methanol',
    {'Methanol Synthesis': {'Methanol': Q('1 MWh')}},
    'Heatpump for DAC -> Heat => Direct Air Capture -> Captured CO2 => Methanol Synthesis;Electrolysis -> Hydrogen => Methanol Synthesis -> Methanol',
)

g, lay = pc_green_meoh.igraph()
fig, ax = plt.subplots()
ax.set_title(pc_green_meoh.name)
ig.plot(g, target=ax, layout=lay, vertex_label=[n.replace(' ', '\n') for n in g.vs['name']], edge_label=[n.replace(' ', '\n') for n in g.es['name']], vertex_label_size=8, edge_label_size=6)

pc_green_meoh = ProcessChain( 'Green Methanol', {'Methanol Synthesis': {'Methanol': Q('1 MWh')}}, 'Heatpump for DAC -> Heat => Direct Air Capture -> Captured CO2 => Methanol Synthesis;Electrolysis -> Hydrogen => Methanol Synthesis -> Methanol', ) g, lay = pc_green_meoh.igraph() fig, ax = plt.subplots() ax.set_title(pc_green_meoh.name) ig.plot(g, target=ax, layout=lay, vertex_label=[n.replace(' ', '\n') for n in g.vs['name']], edge_label=[n.replace(' ', '\n') for n in g.es['name']], vertex_label_size=8, edge_label_size=6)

Out[18]:

<igraph.drawing.matplotlib.graph.GraphArtist at 0x77a690c46a40>

In [19]:

df_lcox_meoh_pc = pd.concat([
        DataSet('Tech|Electrolysis').aggregate(period=[2030, 2050], subtech=['AEL', 'PEM'], size=['1 MW', '100 MW'], agg=['subtech', 'size', 'source']),
        DataSet('Tech|Direct Air Capture').aggregate(period=[2030, 2050], subtech='LT-DAC'),
        DataSet('Tech|Heatpump for DAC').aggregate(period=[2030, 2050]),
        DataSet('Tech|Methanol Synthesis').aggregate(period=[2030, 2050]),
        pd.DataFrame.from_records([
            {'period': 2030, 'variable': 'Price|Electricity', 'unit': 'EUR_2022/MWh', 'value': 50},
            {'period': 2050, 'variable': 'Price|Electricity', 'unit': 'EUR_2022/MWh', 'value': 30},
        ]),
    ]) \
    .team.perform(pc_green_meoh) \
    .team.perform(LCOX(
        'Methanol Synthesis|Output|Methanol', process_chain='Green Methanol',
        interest_rate=0.1, book_lifetime=10.0,
    ), only_new=True) \
    .team.unit_convert('EUR_2022/MWh')

display(df_lcox_meoh_pc)

df_lcox_meoh_pc = pd.concat([ DataSet('Tech|Electrolysis').aggregate(period=[2030, 2050], subtech=['AEL', 'PEM'], size=['1 MW', '100 MW'], agg=['subtech', 'size', 'source']), DataSet('Tech|Direct Air Capture').aggregate(period=[2030, 2050], subtech='LT-DAC'), DataSet('Tech|Heatpump for DAC').aggregate(period=[2030, 2050]), DataSet('Tech|Methanol Synthesis').aggregate(period=[2030, 2050]), pd.DataFrame.from_records([ {'period': 2030, 'variable': 'Price|Electricity', 'unit': 'EUR_2022/MWh', 'value': 50}, {'period': 2050, 'variable': 'Price|Electricity', 'unit': 'EUR_2022/MWh', 'value': 30}, ]), ]) \ .team.perform(pc_green_meoh) \ .team.perform(LCOX( 'Methanol Synthesis|Output|Methanol', process_chain='Green Methanol', interest_rate=0.1, book_lifetime=10.0, ), only_new=True) \ .team.unit_convert('EUR_2022/MWh') display(df_lcox_meoh_pc)

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/noslag.py:368: UserWarning:

Unknown variable, so dropping rows:
36    Emissions|CO2
37    Emissions|CO2
Name: variable, dtype: object

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/.venv/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1601: UnitStrippedWarning:

The unit of the quantity is stripped when downcasting to ndarray.

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Heat, Output|Captured CO2

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Water, Output|Hydrogen

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Output|Heat

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Captured CO2, Input|Hydrogen, Output|Heat, Output|Methanol

	region	period	variable	value	unit
0	World	2030	LCOX|Green Methanol|Direct Air Capture|OM Fixed	6.333591	EUR_2022/MWh
1	World	2050	LCOX|Green Methanol|Direct Air Capture|OM Fixed	5.237687	EUR_2022/MWh
2	World	2030	LCOX|Green Methanol|Direct Air Capture|OM Vari...	11.528534	EUR_2022/MWh
3	World	2050	LCOX|Green Methanol|Direct Air Capture|OM Vari...	11.528534	EUR_2022/MWh
4	World	2030	LCOX|Green Methanol|Direct Air Capture|Input C...	13.206905	EUR_2022/MWh
5	World	2050	LCOX|Green Methanol|Direct Air Capture|Input C...	7.924143	EUR_2022/MWh
6	World	2030	LCOX|Green Methanol|Electrolysis|Capital	16.639994	EUR_2022/MWh
7	World	2050	LCOX|Green Methanol|Electrolysis|Capital	10.030329	EUR_2022/MWh
8	World	2030	LCOX|Green Methanol|Electrolysis|OM Fixed	4.045965	EUR_2022/MWh
9	World	2050	LCOX|Green Methanol|Electrolysis|OM Fixed	3.059801	EUR_2022/MWh
10	World	2030	LCOX|Green Methanol|Electrolysis|Input Cost|El...	87.623100	EUR_2022/MWh
11	World	2050	LCOX|Green Methanol|Electrolysis|Input Cost|El...	50.603220	EUR_2022/MWh
12	World	2030	LCOX|Green Methanol|Heatpump for DAC|Capital	8.246405	EUR_2022/MWh
13	World	2050	LCOX|Green Methanol|Heatpump for DAC|Capital	7.408040	EUR_2022/MWh
14	World	2030	LCOX|Green Methanol|Heatpump for DAC|OM Fixed	1.013412	EUR_2022/MWh
15	World	2050	LCOX|Green Methanol|Heatpump for DAC|OM Fixed	0.910103	EUR_2022/MWh
16	World	2030	LCOX|Green Methanol|Heatpump for DAC|OM Variable	1.279063	EUR_2022/MWh
17	World	2050	LCOX|Green Methanol|Heatpump for DAC|OM Variable	1.203866	EUR_2022/MWh
18	World	2030	LCOX|Green Methanol|Heatpump for DAC|Input Cos...	0.009858	EUR_2022/MWh
19	World	2050	LCOX|Green Methanol|Heatpump for DAC|Input Cos...	0.005494	EUR_2022/MWh
20	World	2030	LCOX|Green Methanol|Methanol Synthesis|Capital	10.583450	EUR_2022/MWh
21	World	2050	LCOX|Green Methanol|Methanol Synthesis|Capital	9.831386	EUR_2022/MWh
22	World	2030	LCOX|Green Methanol|Methanol Synthesis|OM Fixed	3.231184	EUR_2022/MWh
23	World	2050	LCOX|Green Methanol|Methanol Synthesis|OM Fixed	2.984565	EUR_2022/MWh
24	World	2030	LCOX|Green Methanol|Methanol Synthesis|OM Vari...	3.385836	EUR_2022/MWh
25	World	2050	LCOX|Green Methanol|Methanol Synthesis|OM Vari...	3.385836	EUR_2022/MWh

In [20]:

df_lcox_meoh_pc.team.varsplit('LCOX|Green Methanol|?process|*component') \
    .plot.bar(x='period', y='value', color='component', hover_data='process')

df_lcox_meoh_pc.team.varsplit('LCOX|Green Methanol|?process|*component') \ .plot.bar(x='period', y='value', color='component', hover_data='process')

LCOX of green ethylene (from green methanol)¶

In [21]:

pc_green_ethylene = ProcessChain(
    'Green Ethylene',
    {'Electric Arc Furnace': {'Ethylene': Q('1t')}},
    'Electrolysis -> Hydrogen => Methanol Synthesis; Heatpump for DAC -> Heat => Direct Air Capture -> Captured CO2 => Methanol Synthesis -> Methanol => Methanol to Olefines -> Ethylene',
)

g, lay = pc_green_ethylene.igraph()
fig, ax = plt.subplots()
ax.set_title(pc_green_ethylene.name)
ig.plot(g, target=ax, layout=lay, vertex_label=[n.replace(' ', '\n') for n in g.vs['name']], edge_label=[n.replace(' ', '\n') for n in g.es['name']], vertex_label_size=8, edge_label_size=6)

pc_green_ethylene = ProcessChain( 'Green Ethylene', {'Electric Arc Furnace': {'Ethylene': Q('1t')}}, 'Electrolysis -> Hydrogen => Methanol Synthesis; Heatpump for DAC -> Heat => Direct Air Capture -> Captured CO2 => Methanol Synthesis -> Methanol => Methanol to Olefines -> Ethylene', ) g, lay = pc_green_ethylene.igraph() fig, ax = plt.subplots() ax.set_title(pc_green_ethylene.name) ig.plot(g, target=ax, layout=lay, vertex_label=[n.replace(' ', '\n') for n in g.vs['name']], edge_label=[n.replace(' ', '\n') for n in g.es['name']], vertex_label_size=8, edge_label_size=6)

Out[21]:

<igraph.drawing.matplotlib.graph.GraphArtist at 0x77a69275c910>

LCOX of green steel¶

In [22]:

pc_green_steel = ProcessChain(
    'Green Steel (H2-DR)',
    {'Steel Hot Rolling': {'Steel Hot-rolled Coil': Q('1t')}},
    'Electrolysis -> Hydrogen => Iron Direct Reduction -> Directly Reduced Iron => Electric Arc Furnace -> Steel Liquid => Steel Casting -> Steel Slab => Steel Hot Rolling -> Steel Hot-rolled Coil',
)

g, lay = pc_green_steel.igraph()
fig, ax = plt.subplots()
ax.set_title(pc_green_steel.name)
ig.plot(g, target=ax, layout=lay, vertex_label=[n.replace(' ', '\n') for n in g.vs['name']], edge_label=[n.replace(' ', '\n') for n in g.es['name']], vertex_label_size=8, edge_label_size=6)

pc_green_steel = ProcessChain( 'Green Steel (H2-DR)', {'Steel Hot Rolling': {'Steel Hot-rolled Coil': Q('1t')}}, 'Electrolysis -> Hydrogen => Iron Direct Reduction -> Directly Reduced Iron => Electric Arc Furnace -> Steel Liquid => Steel Casting -> Steel Slab => Steel Hot Rolling -> Steel Hot-rolled Coil', ) g, lay = pc_green_steel.igraph() fig, ax = plt.subplots() ax.set_title(pc_green_steel.name) ig.plot(g, target=ax, layout=lay, vertex_label=[n.replace(' ', '\n') for n in g.vs['name']], edge_label=[n.replace(' ', '\n') for n in g.es['name']], vertex_label_size=8, edge_label_size=6)

Out[22]:

<igraph.drawing.matplotlib.graph.GraphArtist at 0x77a6927b2ec0>

In [23]:

df_lcox_green_steel = pd.concat([
        DataSet('Tech|Electrolysis').aggregate(period=2030, subtech=['AEL', 'PEM'], size=['1 MW', '100 MW'], agg=['subtech', 'size', 'source'], override={'Tech|ELH2|Output Capacity|Hydrogen': 'kW;LHV'}),
        DataSet('Tech|Iron Direct Reduction').aggregate(period=2030, mode='h2'),
        DataSet('Tech|Electric Arc Furnace').aggregate(period=2030, mode='Primary'),
        DataSet('Tech|Steel Casting').aggregate(period=2030),
        DataSet('Tech|Steel Hot Rolling').aggregate(period=2030),
        pd.DataFrame({'price_case': range(30, 60, 10), 'variable': 'Price|Electricity', 'unit': 'EUR_2020/MWh', 'value': range(30, 60, 10)}),
    ]) \
    .team.perform(pc_green_steel) \
    .team.perform(LCOX(
        'Steel Hot Rolling|Output|Steel Hot-rolled Coil', process_chain='Green Steel (H2-DR)',
        interest_rate=0.1, book_lifetime=10.0,
    ), only_new=True) \
    .team.unit_convert('EUR_2022/t')

display(df_lcox_green_steel)

df_lcox_green_steel = pd.concat([ DataSet('Tech|Electrolysis').aggregate(period=2030, subtech=['AEL', 'PEM'], size=['1 MW', '100 MW'], agg=['subtech', 'size', 'source'], override={'Tech|ELH2|Output Capacity|Hydrogen': 'kW;LHV'}), DataSet('Tech|Iron Direct Reduction').aggregate(period=2030, mode='h2'), DataSet('Tech|Electric Arc Furnace').aggregate(period=2030, mode='Primary'), DataSet('Tech|Steel Casting').aggregate(period=2030), DataSet('Tech|Steel Hot Rolling').aggregate(period=2030), pd.DataFrame({'price_case': range(30, 60, 10), 'variable': 'Price|Electricity', 'unit': 'EUR_2020/MWh', 'value': range(30, 60, 10)}), ]) \ .team.perform(pc_green_steel) \ .team.perform(LCOX( 'Steel Hot Rolling|Output|Steel Hot-rolled Coil', process_chain='Green Steel (H2-DR)', interest_rate=0.1, book_lifetime=10.0, ), only_new=True) \ .team.unit_convert('EUR_2022/t') display(df_lcox_green_steel)

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/noslag.py:651: HarmoniseMappingFailure:

No OCF value matching a OPEX Fixed Specific value found.
    period     reheating component region  \
73    2030  w/ reheating    Labour  World   

                                         variable  \
73  Tech|Electric Arc Furnace|OPEX Fixed Specific   

                               reference_variable  source      value  
73  Tech|Electric Arc Furnace|Output|Steel Liquid  Vogl18  62.628147  

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/noslag.py:651: HarmoniseMappingFailure:

No OCF value matching a OPEX Fixed Specific value found.
    period      reheating component region  \
74    2030  w/o reheating    Labour  World   

                                         variable  \
74  Tech|Electric Arc Furnace|OPEX Fixed Specific   

                               reference_variable  source      value  
74  Tech|Electric Arc Furnace|Output|Steel Liquid  Vogl18  62.628147  

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/.venv/lib/python3.10/site-packages/pandas/core/dtypes/cast.py:1601: UnitStrippedWarning:

The unit of the quantity is stripped when downcasting to ndarray.

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Alloys, Input|Coal, Input|Directly Reduced Iron, Input|Fossil Gas, Input|Graphite Electrode, Input|Heat, Input|Lime, Input|Nitrogen, Input|Oxygen, Input|Steel Scrap, Output|Steel Liquid

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Water, Output|Hydrogen

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Fossil Gas, Input|Heat, Input|Hydrogen, Input|Iron Ore, Output|Directly Reduced Iron

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Heat, Input|Steel Liquid, Output|Steel Slab

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/team.py:582: UserWarning:

The following inputs/outputs are not used in LCOX, because they are neither the reference nor is an associated price given: Input|Heat, Input|Steel Slab, Output|Steel Hot-rolled Coil

	region	period	reheating	price_case	variable	value	unit
0	World	2030.0	w/ reheating	30.0	LCOX|Green Steel (H2-DR)|Electric Arc Furnace|...	51.314770	EUR_2022/t
1	World	2030.0	w/ reheating	40.0	LCOX|Green Steel (H2-DR)|Electric Arc Furnace|...	51.314770	EUR_2022/t
2	World	2030.0	w/ reheating	50.0	LCOX|Green Steel (H2-DR)|Electric Arc Furnace|...	51.314770	EUR_2022/t
3	World	2030.0	w/o reheating	30.0	LCOX|Green Steel (H2-DR)|Electric Arc Furnace|...	51.314770	EUR_2022/t
4	World	2030.0	w/o reheating	40.0	LCOX|Green Steel (H2-DR)|Electric Arc Furnace|...	51.314770	EUR_2022/t
...	...	...	...	...	...	...	...
79	World	2030.0	w/ reheating	40.0	LCOX|Green Steel (H2-DR)|Steel Hot Rolling|Inp...	4.101336	EUR_2022/t
80	World	2030.0	w/ reheating	50.0	LCOX|Green Steel (H2-DR)|Steel Hot Rolling|Inp...	5.126670	EUR_2022/t
81	World	2030.0	w/o reheating	30.0	LCOX|Green Steel (H2-DR)|Steel Hot Rolling|Inp...	3.076002	EUR_2022/t
82	World	2030.0	w/o reheating	40.0	LCOX|Green Steel (H2-DR)|Steel Hot Rolling|Inp...	4.101336	EUR_2022/t
83	World	2030.0	w/o reheating	50.0	LCOX|Green Steel (H2-DR)|Steel Hot Rolling|Inp...	5.126670	EUR_2022/t

84 rows × 7 columns

In [24]:

df_lcox_green_steel.team.varsplit('LCOX|Green Steel (H2-DR)|?process|*component') \
    .plot.bar(x='price_case', y='value', color='component', hover_data='process', facet_col='reheating')

df_lcox_green_steel.team.varsplit('LCOX|Green Steel (H2-DR)|?process|*component') \ .plot.bar(x='price_case', y='value', color='component', hover_data='process', facet_col='reheating')

LCOX of cement w/ and w/o CC¶

In [25]:

df_lcox_cement = pd.concat([
        DataSet('Tech|Cement Production').aggregate(period=2030),
        pd.DataFrame.from_records([
            {'variable': 'Price|Electricity', 'unit': 'EUR_2022/MWh', 'value': 50},
            {'variable': 'Price|Coal', 'unit': 'EUR_2022/GJ', 'value': 3},
            {'variable': 'Price|Oxygen', 'unit': 'EUR_2022/t', 'value': 30},
            {'variable': 'Price|Captured CO2', 'unit': 'EUR_2022/t', 'value': -30},
        ]),
    ]) \
    .team.perform(LCOX(
        'Output|Cement', 'Cement Production',
        interest_rate=0.1, book_lifetime=10.0,
    ), only_new=True) \
    .team.unit_convert('EUR_2022/t')

display(df_lcox_cement)

df_lcox_cement = pd.concat([ DataSet('Tech|Cement Production').aggregate(period=2030), pd.DataFrame.from_records([ {'variable': 'Price|Electricity', 'unit': 'EUR_2022/MWh', 'value': 50}, {'variable': 'Price|Coal', 'unit': 'EUR_2022/GJ', 'value': 3}, {'variable': 'Price|Oxygen', 'unit': 'EUR_2022/t', 'value': 30}, {'variable': 'Price|Captured CO2', 'unit': 'EUR_2022/t', 'value': -30}, ]), ]) \ .team.perform(LCOX( 'Output|Cement', 'Cement Production', interest_rate=0.1, book_lifetime=10.0, ), only_new=True) \ .team.unit_convert('EUR_2022/t') display(df_lcox_cement)

/home/philippv/Documents/4-projects/10-posted/01-vcs/posted/python/posted/noslag.py:368: UserWarning:

Unknown variable, so dropping rows:
37    Emissions|CO2
38    Emissions|CO2
39    Emissions|CO2
40    Emissions|CO2
41    Emissions|CO2
42    Emissions|CO2
43    Emissions|CO2
44    Emissions|CO2
Name: variable, dtype: object

	subtech	region	period	variable	value	unit
0	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|Capital	65.362053	EUR_2022/t
1	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|Capital	31.433808	EUR_2022/t
2	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Capital	67.076978	EUR_2022/t
3	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Capital	62.653250	EUR_2022/t
4	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|OM Variable	34.077011	EUR_2022/t
5	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|OM Variable	22.564951	EUR_2022/t
6	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|OM Variable	34.306596	EUR_2022/t
7	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|OM Variable	33.125872	EUR_2022/t
8	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Coal	11.998800	EUR_2022/t
9	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|Input Cost|Coal	7.147440	EUR_2022/t
10	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Coal	19.234800	EUR_2022/t
11	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Coal	15.660000	EUR_2022/t
12	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Electricity	6.395000	EUR_2022/t
13	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|Input Cost|Electricity	4.838000	EUR_2022/t
14	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Electricity	NaN	EUR_2022/t
15	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Electricity	2.121500	EUR_2022/t
16	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Oxygen	8.382000	EUR_2022/t
17	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|Input Cost|Oxygen	11.250000	EUR_2022/t
18	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Oxygen	11.250000	EUR_2022/t
19	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Input Cost|Oxygen	9.705000	EUR_2022/t
20	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Captured...	21.996000	EUR_2022/t
21	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Captured...	0.000000	EUR_2022/t
22	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Captured...	27.720000	EUR_2022/t
23	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Captured...	25.488000	EUR_2022/t
24	Integrated CaL (w/ CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Electricity	NaN	EUR_2022/t
25	Standard (w/o CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Electricity	NaN	EUR_2022/t
26	Tail-end CaL 20% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Electricity	-4.055000	EUR_2022/t
27	Tail-end CaL 50% IL (w/ CC)	World	2030.0	LCOX|Cement Production|Output Revenue|Electricity	NaN	EUR_2022/t

We first sort the dataframe by total LCOX for each subtech.

In [26]:

df_lcox_cement.team.varsplit('?variable|?process|*component') \
    .groupby('subtech') \
    .apply(lambda df: df.assign(order=df['value'].sum()), include_groups=False) \
    .sort_values(by='order') \
    .reset_index() \
    .plot.bar(x='subtech', y='value', color='component', hover_data='process')

df_lcox_cement.team.varsplit('?variable|?process|*component') \ .groupby('subtech') \ .apply(lambda df: df.assign(order=df['value'].sum()), include_groups=False) \ .sort_values(by='order') \ .reset_index() \ .plot.bar(x='subtech', y='value', color='component', hover_data='process')