Run EnergyScope with LCA impact scores¶
In this notebook, we show how to use LCA data in EnergyScope. We use the core model of EnergyScope as an illustrative example. Before following this tutorial, make sure you have followed the "Generation of LCA impact scores" tutorial to generate the .dat and .mod files of LCA parameters and constraints to be used in EnergyScope.
We show how to:
- easily add the LCA .mod and .dat files to the core version
- run the model
- format the results
- visualise the results using mescal's ⧉ visualisation features
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%pip install mescal==1.2.4
%pip install energyscope==2.1.2
%pip install mescal==1.2.4
%pip install energyscope==2.1.2
Once you have generated your set of LCA metrics, you are ready to run EnergyScope with LCA variables as objective function or constraints.
Import packages¶
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from energyscope.models import core, Model
from energyscope.energyscope import Energyscope
from mescal import Plot
import pandas as pd
from energyscope.models import core, Model
from energyscope.energyscope import Energyscope
from mescal import Plot
import pandas as pd
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INPUT_DATA_FOLDER = 'path/to/your/input/files/' # put the path of your data folder here
LCA_RESULTS_FOLDER = 'path/to/your/lca/results/files/' # put the path of your LCA results folder here
license_uuid = 'xxx' # put your AMPL licence UUID here
INPUT_DATA_FOLDER = 'path/to/your/input/files/' # put the path of your data folder here
LCA_RESULTS_FOLDER = 'path/to/your/lca/results/files/' # put the path of your LCA results folder here
license_uuid = 'xxx' # put your AMPL licence UUID here
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lifetime = pd.read_csv(INPUT_DATA_FOLDER+'lifetime.csv')
impact_scores_lca = pd.read_csv(LCA_RESULTS_FOLDER+'impact_scores.csv') # LCA impact scores
impact_scores_direct = pd.read_csv(LCA_RESULTS_FOLDER+'impact_scores_direct_emissions.csv') # Impact scores for direct emissions
lifetime = pd.read_csv(INPUT_DATA_FOLDER+'lifetime.csv')
impact_scores_lca = pd.read_csv(LCA_RESULTS_FOLDER+'impact_scores.csv') # LCA impact scores
impact_scores_direct = pd.read_csv(LCA_RESULTS_FOLDER+'impact_scores_direct_emissions.csv') # Impact scores for direct emissions
Run EnergyScope¶
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LCA_model = Model([
('mod', LCA_RESULTS_FOLDER+'objectives_lca.mod'),
# ('mod', LCA_RESULTS_FOLDER+'objectives_lca_direct.mod'),
('dat', LCA_RESULTS_FOLDER+'techs_lca.dat'),
# ('dat', LCA_RESULTS_FOLDER+'techs_lca_direct.dat'),
])
LCA_model = Model([
('mod', LCA_RESULTS_FOLDER+'objectives_lca.mod'),
# ('mod', LCA_RESULTS_FOLDER+'objectives_lca_direct.mod'),
('dat', LCA_RESULTS_FOLDER+'techs_lca.dat'),
# ('dat', LCA_RESULTS_FOLDER+'techs_lca_direct.dat'),
])
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core = core.__add__(LCA_model) # add the LCA model to the core model
core = core.__add__(LCA_model) # add the LCA model to the core model
Make sure to replace the license UUID with your own, you can access it directly from the AMPL portal ⧉. If you don't have an account please create one, and favor your academic address to get access to the free academic license.
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energyscope_lca = Energyscope(
model=core,
solver_options={'solver': 'gurobi','gurobi_options':'outlev=1 NumericFocus=2 method=2'},
modules=['gurobi'],
license_uuid=license_uuid,
)
energyscope_lca = Energyscope(
model=core,
solver_options={'solver': 'gurobi','gurobi_options':'outlev=1 NumericFocus=2 method=2'},
modules=['gurobi'],
license_uuid=license_uuid,
)
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results_tc = energyscope_lca.calc()
results_tc = energyscope_lca.calc()
Gurobi 11.0.0: Set parameter LogToConsole to value 1
tech:outlev = 1
Set parameter NumericFocus to value 2
alg:numericfocus = 2
Set parameter Method to value 2
alg:method = 2
Set parameter InfUnbdInfo to value 1
Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (mac64[arm] - Darwin 23.5.0 23F79)
CPU model: Apple M2 Pro
Thread count: 10 physical cores, 10 logical processors, using up to 10 threads
Optimize a model with 491079 rows, 268733 columns and 1576042 nonzeros
Model fingerprint: 0x489b5254
Coefficient statistics:
Matrix range [1e-06, 7e+04]
Objective range [1e+00, 1e+00]
Bounds range [2e-02, 6e+01]
RHS range [1e+00, 4e+04]
Presolve removed 243247 rows and 128451 columns
Presolve time: 1.11s
Presolved: 247832 rows, 140282 columns, 798471 nonzeros
Elapsed ordering time = 5s
Ordering time: 5.65s
Barrier statistics:
Dense cols : 98
AA' NZ : 3.806e+06
Factor NZ : 1.871e+07 (roughly 300 MB of memory)
Factor Ops : 7.223e+09 (less than 1 second per iteration)
Threads : 10
Objective Residual
Iter Primal Dual Primal Dual Compl Time
0 1.26214894e+07 -1.08114072e+06 1.99e+05 7.82e-14 1.00e+06 8s
1 1.35339311e+07 -2.45874867e+06 1.48e+05 8.36e+02 9.01e+05 8s
2 1.38220425e+07 -4.82323219e+06 1.43e+05 8.19e+02 8.29e+05 9s
3 1.55994536e+07 -5.80812720e+07 9.43e+04 6.18e+02 6.47e+05 10s
4 1.69376975e+07 -8.75794573e+07 6.41e+04 2.55e+02 2.99e+05 10s
5 1.67070071e+07 -9.53004389e+07 4.38e+04 8.43e+01 1.23e+05 11s
6 1.49631224e+07 -9.21859993e+07 2.77e+04 2.79e+01 5.37e+04 11s
7 1.24321302e+07 -9.04378015e+07 1.43e+04 1.76e+01 3.09e+04 11s
8 1.17871098e+07 -8.62747350e+07 1.17e+04 1.15e+01 2.23e+04 12s
9 9.83264710e+06 -8.06615974e+07 5.15e+03 5.91e+00 1.11e+04 12s
10 8.27717146e+06 -7.19404442e+07 1.52e+03 2.11e+00 3.83e+03 13s
11 7.07560975e+06 -4.98458760e+07 5.99e+02 4.49e-01 1.06e+03 13s
12 4.69555157e+06 -2.83726138e+07 1.19e+02 1.45e-01 3.27e+02 14s
13 2.73919161e+06 -8.60861758e+06 2.36e+01 3.31e-02 8.11e+01 14s
14 1.58495883e+06 -5.04289313e+06 9.81e+00 1.89e-02 4.50e+01 15s
15 1.15002761e+06 -3.73458586e+06 5.92e+00 1.38e-02 3.26e+01 15s
16 1.00327382e+06 -2.27155989e+06 4.78e+00 8.30e-03 2.07e+01 15s
17 6.05300251e+05 -1.23203779e+06 2.28e+00 4.45e-03 1.13e+01 16s
18 4.64584361e+05 -6.32348880e+05 1.58e+00 2.30e-03 6.34e+00 16s
19 3.55194859e+05 -5.01622560e+05 1.08e+00 1.88e-03 5.02e+00 17s
20 2.94192951e+05 -3.71479342e+05 7.98e-01 1.44e-03 3.88e+00 17s
21 2.02704421e+05 -3.00907689e+05 4.08e-01 1.18e-03 3.00e+00 17s
22 1.67462697e+05 -1.68280621e+05 2.63e-01 6.95e-04 1.92e+00 18s
23 1.16017225e+05 -7.11479718e+04 6.35e-02 3.46e-04 1.04e+00 18s
24 1.05167669e+05 -3.62786352e+04 2.30e-02 2.23e-04 7.56e-01 18s
25 1.01090645e+05 -2.44646310e+04 1.27e-02 1.82e-04 6.60e-01 19s
26 9.11274044e+04 -9.36211225e+03 1.07e-02 1.30e-04 5.18e-01 19s
27 8.07349691e+04 2.69477488e+03 8.50e-03 9.13e-05 3.95e-01 20s
28 7.16514186e+04 1.64619174e+04 6.44e-03 5.70e-05 2.76e-01 20s
29 6.10425774e+04 2.63738488e+04 3.78e-03 3.10e-05 1.74e-01 21s
30 5.53785573e+04 3.41295145e+04 2.51e-03 1.81e-05 1.03e-01 21s
31 5.16638429e+04 3.70952997e+04 1.75e-03 1.40e-05 7.05e-02 21s
32 4.97160337e+04 3.87140228e+04 1.34e-03 1.14e-05 5.27e-02 22s
33 4.85231204e+04 3.98536285e+04 1.10e-03 8.79e-06 4.07e-02 22s
34 4.79416363e+04 4.02236940e+04 9.73e-04 7.78e-06 3.61e-02 22s
35 4.77836802e+04 4.05966990e+04 9.42e-04 6.74e-06 3.31e-02 23s
36 4.74617778e+04 4.10556830e+04 8.75e-04 5.49e-06 2.89e-02 23s
37 4.65818999e+04 4.14538277e+04 7.01e-04 4.24e-06 2.30e-02 23s
38 4.60620737e+04 4.16875188e+04 6.00e-04 3.52e-06 1.95e-02 24s
39 4.56003673e+04 4.20036758e+04 5.14e-04 2.62e-06 1.58e-02 24s
40 4.51788339e+04 4.22294120e+04 4.33e-04 1.99e-06 1.28e-02 25s
41 4.49533642e+04 4.23101110e+04 3.89e-04 1.77e-06 1.15e-02 25s
42 4.45119295e+04 4.24128387e+04 3.06e-04 1.48e-06 9.21e-03 26s
43 4.40936860e+04 4.25302794e+04 2.28e-04 1.12e-06 6.88e-03 26s
44 4.37293726e+04 4.26569933e+04 1.57e-04 7.37e-07 4.67e-03 27s
45 4.35295399e+04 4.27327619e+04 1.18e-04 5.05e-07 3.42e-03 27s
46 4.32563072e+04 4.27846262e+04 6.58e-05 3.32e-07 2.04e-03 28s
47 4.31874802e+04 4.28275410e+04 5.29e-05 1.93e-07 1.50e-03 29s
48 4.31408174e+04 4.28390582e+04 4.43e-05 1.53e-07 1.24e-03 29s
49 4.31149968e+04 4.28486203e+04 3.96e-05 1.21e-07 1.08e-03 29s
50 4.30727773e+04 4.28537318e+04 3.20e-05 1.04e-07 8.91e-04 30s
51 4.30174189e+04 4.28618969e+04 2.21e-05 7.55e-08 6.30e-04 31s
52 4.29811217e+04 4.28680043e+04 1.63e-05 5.30e-08 4.55e-04 33s
53 4.29621596e+04 4.28738129e+04 1.31e-05 3.13e-08 3.47e-04 33s
54 4.29403716e+04 4.28761294e+04 9.32e-06 2.40e-08 2.52e-04 34s
55 4.29298139e+04 4.28773018e+04 7.62e-06 1.94e-08 2.05e-04 35s
56 4.29251256e+04 4.28778535e+04 6.80e-06 1.77e-08 1.84e-04 36s
57 4.29217064e+04 4.28786685e+04 6.23e-06 1.46e-08 1.67e-04 37s
58 4.29166251e+04 4.28787407e+04 5.43e-06 1.42e-08 1.47e-04 38s
59 4.29109658e+04 4.28801101e+04 4.46e-06 1.01e-08 1.19e-04 39s
60 4.29047023e+04 4.28808103e+04 3.40e-06 8.05e-09 9.18e-05 40s
61 4.29007751e+04 4.28810904e+04 2.77e-06 7.19e-09 7.57e-05 42s
62 4.28978158e+04 4.28815872e+04 2.28e-06 5.68e-09 6.22e-05 43s
63 4.28958179e+04 4.28817601e+04 1.96e-06 5.01e-09 5.38e-05 44s
64 4.28949524e+04 4.28819003e+04 1.81e-06 4.68e-09 5.00e-05 44s
65 4.28904490e+04 4.28823731e+04 1.07e-06 3.38e-09 3.10e-05 45s
66 4.28879606e+04 4.28828284e+04 6.59e-07 2.12e-09 1.97e-05 46s
67 4.28871286e+04 4.28830120e+04 5.26e-07 1.62e-09 1.57e-05 47s
68 4.28867831e+04 4.28830540e+04 4.69e-07 1.52e-09 1.43e-05 48s
69 4.28863795e+04 4.28832572e+04 4.05e-07 1.11e-09 1.19e-05 49s
70 4.28860712e+04 4.28833410e+04 3.54e-07 9.48e-10 1.04e-05 50s
71 4.28856095e+04 4.28834629e+04 2.78e-07 7.07e-10 8.14e-06 50s
72 4.28853010e+04 4.28835296e+04 2.31e-07 5.67e-10 6.71e-06 51s
73 4.28850324e+04 4.28835725e+04 1.88e-07 4.87e-10 5.53e-06 52s
74 4.28847724e+04 4.28836136e+04 1.47e-07 4.03e-10 4.40e-06 52s
75 4.28845461e+04 4.28837236e+04 1.11e-07 1.44e-10 3.08e-06 53s
76 4.28843701e+04 4.28837595e+04 8.38e-08 8.27e-11 2.28e-06 54s
77 4.28842334e+04 4.28837720e+04 6.21e-08 6.53e-11 1.72e-06 55s
78 4.28841378e+04 4.28837761e+04 4.78e-08 5.88e-11 1.35e-06 56s
79 4.28840293e+04 4.28838001e+04 3.11e-08 1.36e-11 8.49e-07 56s
80 4.28838919e+04 4.28838121e+04 1.02e-08 1.14e-12 2.95e-07 57s
81 4.28838632e+04 4.28838158e+04 6.14e-09 1.56e-12 1.75e-07 57s
82 4.28838236e+04 4.28838169e+04 6.79e-09 2.39e-12 2.49e-08 57s
83 4.28838184e+04 4.28838176e+04 1.00e-09 3.98e-12 2.80e-09 58s
84 4.28838178e+04 4.28838177e+04 8.06e-10 7.28e-12 2.82e-10 58s
Barrier solved model in 84 iterations and 57.89 seconds (74.59 work units)
Optimal objective 4.28838178e+04
Crossover log...
53749 DPushes remaining with DInf 0.0000000e+00 58s
5830 DPushes remaining with DInf 0.0000000e+00 60s
0 DPushes remaining with DInf 0.0000000e+00 65s
3062 PPushes remaining with PInf 2.7143602e-02 65s
2641 PPushes remaining with PInf 1.5617888e-02 65s
0 PPushes remaining with PInf 0.0000000e+00 66s
Push phase complete: Pinf 0.0000000e+00, Dinf 8.0106546e+01 66s
Iteration Objective Primal Inf. Dual Inf. Time
39744 4.2883818e+04 0.000000e+00 8.010655e+01 66s
Extra simplex iterations after uncrush: 1
39962 4.2883818e+04 0.000000e+00 0.000000e+00 67s
Solved in 39962 iterations and 67.15 seconds (86.38 work units)
Optimal objective 4.288381774e+04
Gurobi 11.0.0: optimal solution; objective 42883.81774
39962 simplex iteration(s)
84 barrier iteration(s)
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results_tc.variables['TotalLCIA']
results_tc.variables['TotalLCIA']
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| TotalLCIA | Run | |
|---|---|---|
| TTEQ | 1480.600133 | 0 |
| TTHH | 1137.142874 | 0 |
You might want to change the objective function with an environmental objective, such as the life-cycle greenhouse gas emissions, or the total damage on human health/ecosystem quality. Alternatively, you can also add a constraint on the total life-cycle impact, to be lower than a certain threshold.
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# To change the objective function
# energyscope_lca.es_model.eval("""
# drop obj;
# minimize new_obj: TotalLCIA_TTHH;
# """)
# To change the objective function
# energyscope_lca.es_model.eval("""
# drop obj;
# minimize new_obj: TotalLCIA_TTHH;
# """)
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# To add upper limits on environmental impacts
reduction_target = 0.5 # for instance, 50% reduction target of damages
scenario_file_path = LCA_RESULTS_FOLDER + 'scenario.dat'
with open(scenario_file_path, 'w', encoding='utf-8', newline='\n') as f:
f.write(f"let limit_lcia['TTHH'] := {reduction_target} * {results_tc.variables['TotalLCIA'].loc['TTHH'].iloc[0]};\n")
f.write(f"let limit_lcia['TTEQ'] := {reduction_target} * {results_tc.variables['TotalLCIA'].loc['TTEQ'].iloc[0]};\n")
# To add upper limits on environmental impacts
reduction_target = 0.5 # for instance, 50% reduction target of damages
scenario_file_path = LCA_RESULTS_FOLDER + 'scenario.dat'
with open(scenario_file_path, 'w', encoding='utf-8', newline='\n') as f:
f.write(f"let limit_lcia['TTHH'] := {reduction_target} * {results_tc.variables['TotalLCIA'].loc['TTHH'].iloc[0]};\n")
f.write(f"let limit_lcia['TTEQ'] := {reduction_target} * {results_tc.variables['TotalLCIA'].loc['TTEQ'].iloc[0]};\n")
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core = core.__add__(Model([('dat', scenario_file_path)])) # add the scenario file to the core model
core = core.__add__(Model([('dat', scenario_file_path)])) # add the scenario file to the core model
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energyscope_lca = Energyscope(
model=core,
solver_options={'solver': 'gurobi', 'gurobi_options':'outlev=1 NumericFocus=2 method=2'},
modules=['gurobi'],
license_uuid=license_uuid,
)
energyscope_lca = Energyscope(
model=core,
solver_options={'solver': 'gurobi', 'gurobi_options':'outlev=1 NumericFocus=2 method=2'},
modules=['gurobi'],
license_uuid=license_uuid,
)
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results_lca = energyscope_lca.calc()
results_lca = energyscope_lca.calc()
Gurobi 11.0.0: Set parameter LogToConsole to value 1
tech:outlev = 1
Set parameter NumericFocus to value 2
alg:numericfocus = 2
Set parameter Method to value 2
alg:method = 2
Set parameter InfUnbdInfo to value 1
Gurobi Optimizer version 11.0.0 build v11.0.0rc2 (mac64[arm] - Darwin 23.5.0 23F79)
CPU model: Apple M2 Pro
Thread count: 10 physical cores, 10 logical processors, using up to 10 threads
Optimize a model with 491079 rows, 268733 columns and 1576042 nonzeros
Model fingerprint: 0xdcb6a7e9
Coefficient statistics:
Matrix range [1e-06, 7e+04]
Objective range [1e+00, 1e+00]
Bounds range [2e-02, 7e+02]
RHS range [1e+00, 4e+04]
Presolve removed 243244 rows and 128162 columns
Presolve time: 1.09s
Presolved: 247835 rows, 140571 columns, 825165 nonzeros
Ordering time: 3.75s
Barrier statistics:
Dense cols : 98
AA' NZ : 3.844e+06
Factor NZ : 1.872e+07 (roughly 300 MB of memory)
Factor Ops : 7.224e+09 (less than 1 second per iteration)
Threads : 10
Objective Residual
Iter Primal Dual Primal Dual Compl Time
0 2.53843025e+06 -2.99215692e+05 3.27e+06 3.55e-14 1.00e+06 6s
1 3.04076457e+06 -7.18144107e+05 2.82e+06 8.33e+02 9.89e+05 6s
2 3.18107906e+06 -3.60235627e+06 2.48e+06 6.92e+02 7.95e+05 6s
3 4.56493266e+06 -1.18222818e+07 1.98e+06 5.19e+02 6.15e+05 7s
4 5.54382554e+06 -2.08075497e+07 9.34e+05 3.58e+02 4.06e+05 7s
5 5.47355389e+06 -3.09204199e+07 4.47e+05 1.57e+02 1.83e+05 8s
6 4.57233413e+06 -3.60665673e+07 2.64e+05 8.59e+01 1.02e+05 8s
7 3.83989050e+06 -4.28818121e+07 1.81e+05 5.05e+01 6.35e+04 8s
8 2.96318349e+06 -4.36282973e+07 1.14e+05 4.50e+01 5.32e+04 9s
9 2.36873470e+06 -5.37880547e+07 7.84e+04 2.54e+01 3.30e+04 9s
10 1.93445861e+06 -5.87250366e+07 5.74e+04 1.93e+01 2.50e+04 9s
11 1.80936991e+06 -5.98724256e+07 5.11e+04 1.58e+01 2.03e+04 9s
12 1.60562656e+06 -6.31849344e+07 3.95e+04 1.06e+01 1.49e+04 10s
13 1.47890144e+06 -5.97169202e+07 2.86e+04 7.98e+00 1.10e+04 10s
14 1.54082883e+06 -5.44646230e+07 2.31e+04 6.59e+00 8.84e+03 11s
15 1.70192735e+06 -5.14513691e+07 1.84e+04 6.16e+00 7.95e+03 11s
16 2.64073408e+06 -3.61262867e+07 9.28e+03 3.60e+00 4.69e+03 12s
17 3.22595919e+06 -2.47893877e+07 4.72e+03 2.06e+00 2.75e+03 12s
18 3.36953008e+06 -1.90952308e+07 3.12e+03 1.20e+00 1.64e+03 13s
19 3.46994899e+06 -1.77935448e+07 1.79e+03 1.05e+00 1.41e+03 13s
20 3.48520576e+06 -1.13073450e+07 1.33e+03 4.62e-01 6.56e+02 13s
21 3.30532899e+06 -5.78248305e+06 5.72e+02 1.72e-01 2.52e+02 13s
22 2.86627597e+06 -2.39424076e+06 2.40e+02 5.81e-02 9.02e+01 14s
23 2.00349506e+06 -1.41483522e+06 1.09e+02 3.27e-02 4.95e+01 14s
24 1.17009444e+06 -5.01615907e+05 3.23e+01 1.00e-02 1.69e+01 14s
25 6.39351203e+05 -2.74630866e+05 1.29e+01 5.12e-03 8.62e+00 15s
26 3.91972204e+05 -1.93791942e+05 6.91e+00 3.08e-03 5.15e+00 15s
27 3.63207139e+05 -1.25667056e+05 6.39e+00 1.74e-03 3.45e+00 15s
28 3.59261701e+05 -1.05506447e+05 6.20e+00 1.48e-03 3.11e+00 16s
29 2.51020289e+05 -8.13710586e+04 3.88e+00 1.17e-03 2.28e+00 16s
30 2.01903969e+05 -6.02203511e+04 3.01e+00 9.11e-04 1.77e+00 16s
31 1.76234214e+05 -3.68403815e+04 2.42e+00 7.25e-04 1.42e+00 17s
32 1.61180575e+05 -4.70450996e+03 1.77e+00 5.16e-04 1.06e+00 17s
33 1.47262965e+05 8.09243903e+03 1.46e+00 4.32e-04 8.83e-01 18s
34 1.26371833e+05 2.26577691e+04 9.82e-01 3.35e-04 6.64e-01 18s
35 1.19276413e+05 3.71262364e+04 8.59e-01 2.38e-04 5.05e-01 19s
36 1.10073313e+05 4.55089831e+04 7.10e-01 1.77e-04 3.94e-01 19s
37 1.01343133e+05 4.94201905e+04 5.83e-01 1.42e-04 3.25e-01 20s
38 9.34773402e+04 5.14798974e+04 4.84e-01 1.20e-04 2.77e-01 21s
39 8.92751601e+04 5.27248989e+04 4.31e-01 1.10e-04 2.48e-01 22s
40 8.35868864e+04 5.34854887e+04 3.44e-01 9.65e-05 2.12e-01 22s
41 7.83983589e+04 5.51154446e+04 2.56e-01 7.37e-05 1.63e-01 23s
42 7.46263093e+04 5.65402182e+04 2.00e-01 5.31e-05 1.22e-01 23s
43 7.09943711e+04 5.74514802e+04 1.47e-01 3.84e-05 9.00e-02 24s
44 6.97634736e+04 5.79259480e+04 1.30e-01 3.16e-05 7.67e-02 25s
45 6.87002534e+04 5.82711939e+04 1.16e-01 2.63e-05 6.61e-02 25s
46 6.76451941e+04 5.85570027e+04 1.02e-01 2.10e-05 5.56e-02 26s
47 6.70852871e+04 5.87171482e+04 9.53e-02 1.79e-05 4.98e-02 26s
48 6.68973058e+04 5.87808962e+04 9.28e-02 1.68e-05 4.77e-02 27s
49 6.57836808e+04 5.89188917e+04 7.89e-02 1.41e-05 4.04e-02 27s
50 6.46067235e+04 5.90295130e+04 6.41e-02 1.19e-05 3.34e-02 28s
51 6.35207274e+04 5.91090125e+04 5.03e-02 1.03e-05 2.73e-02 28s
52 6.28148607e+04 5.92760786e+04 4.06e-02 7.10e-06 2.06e-02 29s
53 6.18438911e+04 5.93640618e+04 2.72e-02 5.54e-06 1.50e-02 29s
54 6.12747598e+04 5.94494550e+04 1.95e-02 4.25e-06 1.12e-02 30s
55 6.07002614e+04 5.95266244e+04 1.15e-02 3.20e-06 7.63e-03 31s
56 6.05388849e+04 5.95731258e+04 9.36e-03 2.54e-06 6.15e-03 31s
57 6.03611503e+04 5.96082897e+04 6.94e-03 2.07e-06 4.86e-03 32s
58 6.02612497e+04 5.96747256e+04 5.51e-03 1.28e-06 3.43e-03 32s
59 6.02110801e+04 5.97017132e+04 4.76e-03 1.07e-06 2.92e-03 33s
60 6.01656906e+04 5.97281576e+04 4.15e-03 8.43e-07 2.43e-03 33s
61 6.01295901e+04 5.97474492e+04 3.65e-03 6.75e-07 2.05e-03 34s
62 6.00592385e+04 5.97539844e+04 2.68e-03 6.21e-07 1.70e-03 34s
63 6.00274421e+04 5.97714957e+04 2.25e-03 4.92e-07 1.39e-03 35s
64 6.00063199e+04 5.97832268e+04 1.96e-03 4.04e-07 1.18e-03 35s
65 5.99869217e+04 5.97951690e+04 1.70e-03 3.28e-07 9.91e-04 36s
66 5.99728583e+04 5.98009488e+04 1.51e-03 2.91e-07 8.82e-04 37s
67 5.99554247e+04 5.98085292e+04 1.29e-03 2.38e-07 7.40e-04 37s
68 5.99346101e+04 5.98180026e+04 1.01e-03 1.79e-07 5.74e-04 37s
69 5.99303783e+04 5.98212785e+04 9.52e-04 1.59e-07 5.29e-04 38s
70 5.99240814e+04 5.98273948e+04 8.73e-04 1.24e-07 4.54e-04 39s
71 5.99089482e+04 5.98292787e+04 6.79e-04 1.14e-07 3.80e-04 39s
72 5.99062123e+04 5.98312079e+04 6.48e-04 1.01e-07 3.52e-04 40s
73 5.98943187e+04 5.98321969e+04 5.07e-04 9.46e-08 2.97e-04 41s
74 5.98856417e+04 5.98347163e+04 4.05e-04 7.83e-08 2.42e-04 41s
75 5.98818806e+04 5.98376863e+04 3.59e-04 6.16e-08 2.05e-04 42s
76 5.98727580e+04 5.98398429e+04 2.50e-04 5.00e-08 1.54e-04 43s
77 5.98701402e+04 5.98411146e+04 2.19e-04 4.34e-08 1.35e-04 43s
78 5.98682990e+04 5.98425256e+04 1.98e-04 3.58e-08 1.17e-04 44s
79 5.98627424e+04 5.98440672e+04 1.32e-04 2.85e-08 8.58e-05 45s
80 5.98591239e+04 5.98456393e+04 9.12e-05 2.12e-08 6.18e-05 46s
81 5.98584576e+04 5.98460685e+04 8.42e-05 1.91e-08 5.65e-05 47s
82 5.98577825e+04 5.98474989e+04 7.70e-05 1.28e-08 4.50e-05 49s
83 5.98533242e+04 5.98488364e+04 2.84e-05 6.45e-09 1.99e-05 51s
84 5.98518840e+04 5.98493358e+04 1.41e-05 4.31e-09 1.16e-05 52s
85 5.98516221e+04 5.98493826e+04 1.15e-05 4.10e-09 1.03e-05 53s
86 5.98514260e+04 5.98495159e+04 9.66e-06 3.56e-09 8.81e-06 54s
87 5.98513880e+04 5.98496263e+04 9.30e-06 3.12e-09 8.04e-06 55s
88 5.98512869e+04 5.98496390e+04 8.33e-06 3.07e-09 7.59e-06 55s
89 5.98512330e+04 5.98496906e+04 7.80e-06 2.84e-09 7.09e-06 56s
90 5.98511585e+04 5.98498498e+04 7.05e-06 2.18e-09 5.90e-06 57s
91 5.98510341e+04 5.98499731e+04 5.81e-06 1.68e-09 4.73e-06 57s
92 5.98509843e+04 5.98499917e+04 5.34e-06 1.61e-09 4.45e-06 58s
93 5.98509258e+04 5.98500172e+04 4.78e-06 1.51e-09 4.09e-06 59s
94 5.98506864e+04 5.98500758e+04 2.44e-06 1.28e-09 2.88e-06 59s
95 5.98505216e+04 5.98502473e+04 8.96e-07 6.20e-10 1.31e-06 60s
96 5.98504547e+04 5.98503186e+04 3.22e-07 3.45e-10 6.69e-07 62s
97 5.98504496e+04 5.98503280e+04 2.82e-07 3.09e-10 5.98e-07 62s
98 5.98504415e+04 5.98503416e+04 2.19e-07 2.58e-10 4.93e-07 63s
99 5.98504310e+04 5.98503926e+04 1.42e-07 5.76e-11 1.69e-07 63s
100 5.98504127e+04 5.98504074e+04 2.12e-08 6.47e-12 2.26e-08 64s
101 5.98504097e+04 5.98504091e+04 2.00e-09 8.79e-13 2.38e-09 64s
102 5.98504094e+04 5.98504093e+04 2.58e-10 2.13e-12 2.39e-10 65s
Barrier solved model in 102 iterations and 65.29 seconds (92.79 work units)
Optimal objective 5.98504094e+04
Crossover log...
42281 DPushes remaining with DInf 2.6466504e-02 66s
2272 DPushes remaining with DInf 0.0000000e+00 70s
0 DPushes remaining with DInf 0.0000000e+00 75s
3459 PPushes remaining with PInf 3.2086930e-05 75s
2433 PPushes remaining with PInf 1.8013505e-03 75s
0 PPushes remaining with PInf 0.0000000e+00 76s
Push phase complete: Pinf 0.0000000e+00, Dinf 6.0393358e-01 76s
Iteration Objective Primal Inf. Dual Inf. Time
27352 5.9850411e+04 0.000000e+00 6.039336e-01 76s
27374 5.9850409e+04 0.000000e+00 0.000000e+00 76s
Solved in 27374 iterations and 76.18 seconds (109.67 work units)
Optimal objective 5.985040933e+04
Gurobi 11.0.0: optimal solution; objective 59850.40933
27374 simplex iteration(s)
102 barrier iteration(s)
In [15]:
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results_lca.variables['TotalLCIA']
results_lca.variables['TotalLCIA']
Out[15]:
| TotalLCIA | Run | |
|---|---|---|
| TTEQ | 740.300067 | 0 |
| TTHH | 423.584077 | 0 |
Formatting the results¶
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results = results_lca # choose among results_tc and results_lca
results = results_lca # choose among results_tc and results_lca
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df_annual_prod = pd.merge(results.variables['F_t'].reset_index(), results.parameters['t_op'].reset_index(), left_on=['index1', 'index2'], right_on=['index0', 'index1'], suffixes=('', '_'))
df_annual_prod['Annual_Prod'] = df_annual_prod['F_t'] * df_annual_prod['t_op']
df_annual_prod = df_annual_prod.groupby(['index0', 'Run'])['Annual_Prod'].sum().reset_index()
df_installed_cap = results.variables['F'].reset_index()
df_annual_prod = pd.merge(results.variables['F_t'].reset_index(), results.parameters['t_op'].reset_index(), left_on=['index1', 'index2'], right_on=['index0', 'index1'], suffixes=('', '_'))
df_annual_prod['Annual_Prod'] = df_annual_prod['F_t'] * df_annual_prod['t_op']
df_annual_prod = df_annual_prod.groupby(['index0', 'Run'])['Annual_Prod'].sum().reset_index()
df_installed_cap = results.variables['F'].reset_index()
In [18]:
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df_annual_res = df_annual_prod[df_annual_prod.index0.isin(list(results.sets['RESOURCES'].RESOURCES))].rename(columns={'index0':'index', 'Annual_Prod':'Annual_Res'})
df_annual_prod = df_annual_prod[~df_annual_prod.index0.isin(list(results.sets['RESOURCES'].RESOURCES))].rename(columns={'index0':'index'})
df_annual_res = df_annual_prod[df_annual_prod.index0.isin(list(results.sets['RESOURCES'].RESOURCES))].rename(columns={'index0':'index', 'Annual_Prod':'Annual_Res'})
df_annual_prod = df_annual_prod[~df_annual_prod.index0.isin(list(results.sets['RESOURCES'].RESOURCES))].rename(columns={'index0':'index'})
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esm_results_tech = pd.merge(
df_annual_prod,
df_installed_cap,
on=['index', 'Run']
).rename(columns={'index':'Name', 'Annual_Prod': 'Production', 'F': 'Capacity'})
esm_results_res = df_annual_res.rename(columns={'index': 'Name', 'Annual_Res': 'Import'})
esm_results_tech = pd.merge(
df_annual_prod,
df_installed_cap,
on=['index', 'Run']
).rename(columns={'index':'Name', 'Annual_Prod': 'Production', 'F': 'Capacity'})
esm_results_res = df_annual_res.rename(columns={'index': 'Name', 'Annual_Res': 'Import'})
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esm_results_tech = esm_results_tech[esm_results_tech.Capacity != 0]
esm_results_tech.head()
esm_results_tech = esm_results_tech[esm_results_tech.Capacity != 0]
esm_results_tech.head()
Out[20]:
| Name | Run | Production | Capacity | |
|---|---|---|---|---|
| 2 | ATM_CCS | 0 | 249.819476 | 1.642478 |
| 4 | BEV_BATT | 0 | 0.000000 | 91.160423 |
| 10 | BOAT_FREIGHT_NG | 0 | 966.908558 | 30.521103 |
| 14 | BUS_COACH_HYDIESEL | 0 | 551.338240 | 6.381230 |
| 15 | CAR_BEV | 0 | 2756.691201 | 191.436889 |
In [21]:
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esm_results_res = esm_results_res[esm_results_res.Import != 0]
esm_results_res.head()
esm_results_res = esm_results_res[esm_results_res.Import != 0]
esm_results_res.head()
Out[21]:
| Name | Run | Import | |
|---|---|---|---|
| 7 | BIODIESEL | 0 | 110.267648 |
| 29 | CO2_ATM | 0 | 249.819476 |
| 31 | CO2_EMISSIONS | 0 | 435.516977 |
| 64 | ELEC_EXPORT | 0 | 671.424283 |
| 76 | H2_RE | 0 | 6180.293383 |
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esm_results_tech.to_csv(LCA_RESULTS_FOLDER+'esm_results_tech.csv', index=False)
esm_results_res.to_csv(LCA_RESULTS_FOLDER+'esm_results_res.csv', index=False)
esm_results_tech.to_csv(LCA_RESULTS_FOLDER+'esm_results_tech.csv', index=False)
esm_results_res.to_csv(LCA_RESULTS_FOLDER+'esm_results_res.csv', index=False)
Visualise the results¶
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plot = Plot(
df_impact_scores=impact_scores_lca,
esm_results_tech=esm_results_tech[~esm_results_tech.Name.isin(['ATM_CCS'])], # negative impacts are not displayed in the following plots
esm_results_res=esm_results_res,
lifetime=lifetime,
)
plot = Plot(
df_impact_scores=impact_scores_lca,
esm_results_tech=esm_results_tech[~esm_results_tech.Name.isin(['ATM_CCS'])], # negative impacts are not displayed in the following plots
esm_results_res=esm_results_res,
lifetime=lifetime,
)
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plot.plot_results(
impact_categories_list=[
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Ecosystem quality',
'Total ecosystem quality',
),
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Human health',
'Total human health',
),
],
normalized=True,
)
plot.plot_results(
impact_categories_list=[
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Ecosystem quality',
'Total ecosystem quality',
),
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Human health',
'Total human health',
),
],
normalized=True,
)
In [25]:
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plot.plot_results(
impact_categories_list=[
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Ecosystem quality',
'Total ecosystem quality',
),
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Human health',
'Total human health',
),
],
normalized=True,
split_by='Name',
N_highest_contributors=10,
)
plot.plot_results(
impact_categories_list=[
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Ecosystem quality',
'Total ecosystem quality',
),
(
'IMPACT World+ Damage 2.1 for ecoinvent v3.10',
'Human health',
'Total human health',
),
],
normalized=True,
split_by='Name',
N_highest_contributors=10,
)
Due to IAM projections (in the present case, scenario SSP2-RCP26 run in TIAM-UCL) in the LCA database, some important changes occur in LCA datasets. For instance, diesel production has switched to biodiesel (from used cooking oil with CCS). Consequently, CO2 emissions from diesel combustion are biogenic (i.e., their characterization factor is 0) and the impact of TRUCK_DIESEL mostly comes from its construction.