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

In [ ]:

%pip install mescal==1.2.3
%pip install energyscope==2.1.2

%pip install mescal==1.2.3 %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¶

In [1]:

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

In [2]:

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

In [3]:

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¶

In [5]:

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'), ])

In [6]:

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.

In [7]:

energyscope_lca = Energyscope(
    model=core,
    solver_options={'solver': 'gurobi','gurobi_options':'outlev=1 NumericFocus=2 method=2'},
    notebook=True,
    modules=['gurobi'],
    license_uuid=license_uuid,
)

energyscope_lca = Energyscope( model=core, solver_options={'solver': 'gurobi','gurobi_options':'outlev=1 NumericFocus=2 method=2'}, notebook=True, modules=['gurobi'], license_uuid=license_uuid, )

In [8]:

results_tc = energyscope_lca.calc()

results_tc = energyscope_lca.calc()

Gurobi 13.0.0: 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 13.0.0 build v13.0.0rc1 (win64 - Windows 11+.0 (26200.2))

CPU model: 12th Gen Intel(R) Core(TM) i7-1255U, instruction set [SSE2|AVX|AVX2]
Thread count: 10 physical cores, 12 logical processors, using up to 12 threads

Non-default parameters:
Method  2
InfUnbdInfo  1
NumericFocus  2

Optimize a model with 491079 rows, 268733 columns and 1576042 nonzeros (Min)
Model fingerprint: 0xd356ac1f
Model has 1 linear objective coefficients
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 244066 rows and 129177 columns
Presolve time: 2.06s
Presolved: 247013 rows, 139556 columns, 796682 nonzeros

Elapsed ordering time = 5s
Ordering time: 6.22s

Barrier statistics:
 Dense cols : 98
 AA' NZ     : 3.799e+06
 Factor NZ  : 1.783e+07 (roughly 300 MB of memory)
 Factor Ops : 5.419e+09 (less than 1 second per iteration)
 Threads    : 10

                  Objective                Residual
Iter       Primal          Dual         Primal    Dual     Compl     Time
   0   1.16221582e+07 -1.07972572e+06  4.56e+05 1.98e+00  1.00e+06    10s
   1   1.25699879e+07 -2.23258072e+06  3.39e+05 8.36e+02  9.02e+05    11s
   2   1.28525617e+07 -4.29550079e+06  3.27e+05 8.19e+02  8.30e+05    12s
   3   1.45819284e+07 -5.45424209e+07  2.15e+05 6.27e+02  6.54e+05    14s
   4   1.57985628e+07 -8.06542194e+07  1.47e+05 2.94e+02  3.34e+05    15s
   5   1.58143484e+07 -9.28941450e+07  1.01e+05 1.07e+02  1.43e+05    16s
   6   1.37132584e+07 -9.54890885e+07  5.11e+04 3.77e+01  5.86e+04    17s
   7   1.21412725e+07 -9.33472784e+07  3.35e+04 2.17e+01  3.57e+04    17s
   8   1.02479985e+07 -8.87644492e+07  1.65e+04 1.35e+01  2.09e+04    18s
   9   8.81154076e+06 -7.87875007e+07  7.25e+03 5.73e+00  9.06e+03    19s
  10   7.41870842e+06 -6.61055957e+07  1.66e+03 1.95e+00  2.90e+03    19s
  11   6.33868760e+06 -4.49264671e+07  4.91e+02 5.32e-01  8.72e+02    20s
  12   5.09632545e+06 -2.98611201e+07  1.89e+02 2.72e-01  4.45e+02    21s
  13   3.61146627e+06 -8.63298365e+06  8.14e+01 6.63e-02  1.18e+02    22s
  14   2.21995915e+06 -5.21091545e+06  4.02e+01 3.74e-02  6.61e+01    23s
  15   1.65819263e+06 -2.73769548e+06  2.60e+01 1.91e-02  3.55e+01    24s
  16   1.16413210e+06 -1.34312887e+06  1.69e+01 9.06e-03  1.82e+01    25s
  17   5.50330444e+05 -9.12655663e+05  5.97e+00 6.30e-03  1.13e+01    26s
  18   4.12065297e+05 -4.76476645e+05  4.11e+00 3.35e-03  6.35e+00    26s
  19   3.51659255e+05 -3.94296399e+05  3.35e+00 2.80e-03  5.30e+00    27s
  20   2.64045270e+05 -2.43919304e+05  2.30e+00 1.81e-03  3.51e+00    28s
  21   1.61786892e+05 -9.42045456e+04  1.13e+00 8.03e-04  1.66e+00    29s
  22   1.12369936e+05 -3.84244935e+04  6.24e-01 4.45e-04  9.53e-01    30s
  23   1.03996174e+05 -1.75539668e+03  5.50e-01 2.34e-04  6.03e-01    32s
  24   9.13755464e+04  2.91937287e+03  4.54e-01 2.00e-04  5.08e-01    33s
  25   8.73187535e+04  1.26145454e+04  4.16e-01 1.42e-04  4.06e-01    34s
  26   7.71796901e+04  2.05984982e+04  3.08e-01 1.03e-04  3.04e-01    34s
  27   6.34278768e+04  2.79827910e+04  1.77e-01 6.68e-05  1.93e-01    36s
  28   5.66737788e+04  3.38869069e+04  1.14e-01 3.72e-05  1.21e-01    37s
  29   5.32144210e+04  3.83798497e+04  8.40e-02 1.47e-05  7.23e-02    38s
  30   5.01388129e+04  4.05325357e+04  5.78e-02 1.01e-05  4.46e-02    39s
  31   4.92983443e+04  4.10253007e+04  5.08e-02 8.37e-06  3.78e-02    39s
  32   4.92060856e+04  4.12656878e+04  4.98e-02 7.40e-06  3.56e-02    40s
  33   4.84848815e+04  4.14784610e+04  4.38e-02 6.49e-06  3.13e-02    41s
  34   4.66655203e+04  4.18354447e+04  2.95e-02 4.88e-06  2.19e-02    42s
  35   4.59158134e+04  4.22228092e+04  2.37e-02 3.10e-06  1.62e-02    43s
  36   4.53685484e+04  4.23797586e+04  1.96e-02 2.42e-06  1.31e-02    44s
  37   4.50601261e+04  4.24216492e+04  1.73e-02 2.21e-06  1.16e-02    46s
  38   4.44990860e+04  4.25543765e+04  1.34e-02 1.57e-06  8.53e-03    47s
  39   4.40060849e+04  4.26904328e+04  9.25e-03 9.40e-07  5.67e-03    48s
  40   4.35846484e+04  4.27413718e+04  5.76e-03 6.72e-07  3.69e-03    49s
  41   4.34374269e+04  4.27894049e+04  4.61e-03 4.35e-07  2.76e-03    51s
  42   4.32781731e+04  4.28230999e+04  3.28e-03 2.66e-07  1.90e-03    52s
  43   4.32062320e+04  4.28423280e+04  2.66e-03 1.73e-07  1.49e-03    52s
  44   4.31751965e+04  4.28481539e+04  2.39e-03 1.48e-07  1.33e-03    53s
  45   4.31023011e+04  4.28523670e+04  1.77e-03 1.28e-07  1.02e-03    54s
  46   4.30142357e+04  4.28624214e+04  1.02e-03 8.28e-08  6.22e-04    54s
  47   4.29896240e+04  4.28722208e+04  8.36e-04 3.97e-08  4.62e-04    56s
  48   4.29437318e+04  4.28741728e+04  4.59e-04 3.22e-08  2.77e-04    57s
  49   4.29351813e+04  4.28769598e+04  3.94e-04 2.25e-08  2.29e-04    58s
  50   4.29330983e+04  4.28772479e+04  3.78e-04 2.15e-08  2.19e-04    59s
  51   4.29312516e+04  4.28777019e+04  3.64e-04 2.01e-08  2.10e-04    60s
  52   4.29174894e+04  4.28788118e+04  2.54e-04 1.62e-08  1.52e-04    61s
  53   4.29116030e+04  4.28800171e+04  2.11e-04 1.19e-08  1.23e-04    63s
  54   4.29086736e+04  4.28804657e+04  1.88e-04 1.04e-08  1.09e-04    64s
  55   4.29058082e+04  4.28807984e+04  1.66e-04 9.32e-09  9.68e-05    66s
  56   4.29038163e+04  4.28811254e+04  1.52e-04 8.27e-09  8.76e-05    67s
  57   4.29008870e+04  4.28816871e+04  1.30e-04 5.85e-09  7.35e-05    69s
  58   4.28992344e+04  4.28824342e+04  1.17e-04 3.49e-09  6.35e-05    70s
  59   4.28954488e+04  4.28826129e+04  8.71e-05 3.03e-09  4.86e-05    71s
  60   4.28921329e+04  4.28828962e+04  6.30e-05 2.01e-09  3.48e-05    73s
  61   4.28912646e+04  4.28830338e+04  5.63e-05 1.71e-09  3.10e-05    76s
  62   4.28905126e+04  4.28831294e+04  5.14e-05 1.50e-09  2.78e-05    78s
  63   4.28888688e+04  4.28832359e+04  3.87e-05 1.20e-09  2.12e-05    81s
  64   4.28874818e+04  4.28833201e+04  2.85e-05 9.81e-10  1.57e-05    83s
  65   4.28870771e+04  4.28833635e+04  2.54e-05 8.87e-10  1.40e-05    85s
  66   4.28864400e+04  4.28834973e+04  2.03e-05 6.29e-10  1.11e-05    88s
  67   4.28860879e+04  4.28836333e+04  1.74e-05 3.63e-10  9.17e-06    90s
  68   4.28852039e+04  4.28836858e+04  1.03e-05 2.58e-10  5.68e-06    92s
  69   4.28849379e+04  4.28837337e+04  8.34e-06 1.50e-10  4.49e-06    95s
  70   4.28844716e+04  4.28837400e+04  4.60e-06 1.39e-10  2.74e-06    97s
  71   4.28843262e+04  4.28837646e+04  3.52e-06 8.73e-11  2.10e-06    99s
  72   4.28840292e+04  4.28837858e+04  1.40e-06 4.50e-11  9.14e-07   100s
  73   4.28838815e+04  4.28838115e+04  4.02e-07 5.06e-12  2.60e-07   102s
  74   4.28838282e+04  4.28838150e+04  6.23e-08 2.20e-12  4.97e-08   103s
  75   4.28838191e+04  4.28838175e+04  7.71e-09 3.64e-12  6.01e-09   104s
  76   4.28838179e+04  4.28838177e+04  1.17e-09 2.73e-12  6.08e-10   105s

Barrier solved model in 76 iterations and 105.20 seconds (60.13 work units)
Optimal objective 4.28838179e+04

Crossover log...

   53590 DPushes remaining with DInf 0.0000000e+00               106s
   13503 DPushes remaining with DInf 0.0000000e+00               111s
    8328 DPushes remaining with DInf 0.0000000e+00               115s
    3315 DPushes remaining with DInf 0.0000000e+00               120s
    1563 DPushes remaining with DInf 0.0000000e+00               125s
     553 DPushes remaining with DInf 0.0000000e+00               130s
       0 DPushes remaining with DInf 0.0000000e+00               134s

    3817 PPushes remaining with PInf 1.0675306e-04               134s
    3059 PPushes remaining with PInf 7.3430441e-02               135s
       0 PPushes remaining with PInf 0.0000000e+00               139s

  Push phase complete: Pinf 0.0000000e+00, Dinf 1.2346462e+02    139s

Iteration    Objective       Primal Inf.    Dual Inf.      Time
   40664    4.2883818e+04   0.000000e+00   1.234646e+02    139s
   40765    4.2883818e+04   0.000000e+00   2.660964e-02    140s
   41270    4.2883818e+04   0.000000e+00   3.090717e+01    146s
   41674    4.2883818e+04   0.000000e+00   4.082870e-03    150s
Crossover time: 45.17 seconds (16.75 work units)
   41697    4.2883818e+04   0.000000e+00   0.000000e+00    151s

Solved in 41697 iterations and 151.16 seconds (77.11 work units)
Optimal objective  4.288381774e+04

AMPL MP initial flat model has 268733 variables (0 integer, 0 binary);
Objectives: 1 linear; 
Constraints:  491079 linear;

AMPL MP final model has 268733 variables (0 integer, 0 binary);
Objectives: 1 linear; 
Constraints:  491079 linear;


Gurobi 13.0.0: optimal solution; objective 42883.81774
41697 simplex iterations
76 barrier iterations

In [11]:

results_tc.variables['TotalLCIA']

results_tc.variables['TotalLCIA']

Out[11]:

	TotalLCIA	Run
TTEQ	1682.413198	0
TTHH	1200.001631	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.

In [12]:

# 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; # """)

In [14]:

# 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")

In [15]:

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

In [19]:

energyscope_lca = Energyscope(
    model=core,
    solver_options={'solver': 'gurobi', 'gurobi_options':'outlev=1 NumericFocus=2 method=2'},
    notebook=True,
    modules=['gurobi'],
    license_uuid=license_uuid,
)

energyscope_lca = Energyscope( model=core, solver_options={'solver': 'gurobi', 'gurobi_options':'outlev=1 NumericFocus=2 method=2'}, notebook=True, modules=['gurobi'], license_uuid=license_uuid, )

In [20]:

results_lca = energyscope_lca.calc()

results_lca = energyscope_lca.calc()

Gurobi 13.0.0: 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 13.0.0 build v13.0.0rc1 (win64 - Windows 11+.0 (26200.2))

CPU model: 12th Gen Intel(R) Core(TM) i7-1255U, instruction set [SSE2|AVX|AVX2]
Thread count: 10 physical cores, 12 logical processors, using up to 12 threads

Non-default parameters:
Method  2
InfUnbdInfo  1
NumericFocus  2

Optimize a model with 491079 rows, 268733 columns and 1576042 nonzeros (Min)
Model fingerprint: 0x4c617831
Model has 1 linear objective coefficients
Coefficient statistics:
  Matrix range     [1e-06, 7e+04]
  Objective range  [1e+00, 1e+00]
  Bounds range     [2e-02, 8e+02]
  RHS range        [1e+00, 4e+04]
Presolve removed 243488 rows and 128315 columns
Presolve time: 1.36s
Presolved: 247591 rows, 140418 columns, 824522 nonzeros

Elapsed ordering time = 5s
Ordering time: 6.15s

Barrier statistics:
 Dense cols : 98
 AA' NZ     : 3.840e+06
 Factor NZ  : 1.875e+07 (roughly 300 MB of memory)
 Factor Ops : 7.249e+09 (less than 1 second per iteration)
 Threads    : 10

                  Objective                Residual
Iter       Primal          Dual         Primal    Dual     Compl     Time
   0   2.01588285e+06 -3.78949316e+05  3.23e+06 1.44e+00  1.00e+06     9s
   1   2.02961667e+06 -1.05983728e+06  2.98e+06 8.46e+02  9.54e+05    10s
   2   2.23051767e+06 -3.13164283e+06  2.56e+06 7.72e+02  8.64e+05    11s
   3   2.43833112e+06 -8.41836569e+06  2.31e+06 5.48e+02  6.50e+05    12s
   4   3.49303007e+06 -3.83871563e+07  1.74e+06 3.10e+02  4.27e+05    13s
   5   4.01758930e+06 -4.57182269e+07  7.44e+05 1.78e+02  2.18e+05    14s
   6   3.57671925e+06 -5.06294971e+07  4.04e+05 8.48e+01  1.03e+05    15s
   7   2.84239390e+06 -5.64359613e+07  2.24e+05 4.62e+01  5.76e+04    16s
   8   1.94953735e+06 -5.92023907e+07  1.24e+05 3.65e+01  4.14e+04    17s
   9   1.43348058e+06 -6.93652223e+07  7.97e+04 1.80e+01  2.24e+04    18s
  10   1.26019437e+06 -7.33994190e+07  6.73e+04 1.00e+01  1.48e+04    19s
  11   1.11193558e+06 -7.29707611e+07  5.64e+04 8.98e+00  1.30e+04    19s
  12   9.63042807e+05 -7.36628269e+07  4.56e+04 5.15e+00  8.81e+03    20s
  13   8.86370732e+05 -7.24436364e+07  4.01e+04 3.71e+00  7.07e+03    21s
  14   8.51428345e+05 -6.84825339e+07  3.76e+04 3.00e+00  6.19e+03    22s
  15   7.84686759e+05 -6.46982236e+07  3.31e+04 2.47e+00  5.29e+03    24s
  16   6.91361134e+05 -6.28136961e+07  2.69e+04 1.35e+00  3.70e+03    25s
  17   4.49366280e+05 -6.02187500e+07  1.23e+04 9.06e-01  2.02e+03    26s
  18   3.81110012e+05 -4.88322998e+07  8.59e+03 5.40e-01  1.32e+03    28s
  19   3.40650916e+05 -4.18598616e+07  6.61e+03 3.19e-01  9.31e+02    29s
  20   3.00223030e+05 -3.20743234e+07  4.58e+03 1.81e-01  5.91e+02    30s
  21   2.82822856e+05 -3.31500836e+07  3.77e+03 1.74e-01  5.26e+02    30s
  22   2.78521091e+05 -2.86597124e+07  3.58e+03 1.38e-01  4.56e+02    31s
  23   2.76667981e+05 -2.65037933e+07  3.51e+03 1.19e-01  4.22e+02    32s
  24   2.58638053e+05 -2.23392473e+07  2.84e+03 8.95e-02  3.32e+02    33s
  25   2.25562724e+05 -1.52150571e+07  1.51e+03 4.73e-02  1.83e+02    35s
  26   1.99570407e+05 -9.58948378e+06  7.44e+02 2.56e-02  9.42e+01    36s
  27   1.86018668e+05 -5.70309392e+06  4.41e+02 1.31e-02  5.09e+01    37s
  28   1.75733403e+05 -4.07464749e+06  2.49e+02 8.51e-03  3.31e+01    38s
  29   1.71191179e+05 -2.60940412e+06  1.67e+02 5.26e-03  2.08e+01    39s
  30   1.66183298e+05 -2.24691648e+06  7.85e+01 4.50e-03  1.74e+01    40s
  31   1.62339436e+05 -1.84721924e+06  3.66e+01 3.70e-03  1.44e+01    41s
  32   1.54651690e+05 -7.21601541e+05  1.51e+01 1.51e-03  6.04e+00    42s
  33   1.45908550e+05 -3.98730974e+05  8.26e+00 8.93e-04  3.62e+00    43s
  34   1.32958641e+05 -2.35946189e+05  4.56e+00 5.87e-04  2.38e+00    44s
  35   1.28069610e+05 -2.00562235e+05  3.62e+00 5.22e-04  2.11e+00    45s
  36   1.24593099e+05 -6.94358849e+04  3.07e+00 2.80e-04  1.20e+00    46s
  37   1.20426474e+05 -2.45895484e+04  2.54e+00 1.98e-04  8.69e-01    47s
  38   1.17969371e+05 -6.82798570e+03  2.29e+00 1.65e-04  7.38e-01    48s
  39   1.14139346e+05  1.67815900e+04  1.94e+00 1.25e-04  5.64e-01    49s
  40   1.08363105e+05  2.34332988e+04  1.61e+00 1.10e-04  4.93e-01    50s
  41   1.04315530e+05  2.86214891e+04  1.45e+00 9.90e-05  4.43e-01    52s
  42   1.01741814e+05  3.14864315e+04  1.36e+00 9.27e-05  4.14e-01    53s
  43   9.86515393e+04  3.53800475e+04  1.26e+00 8.34e-05  3.75e-01    55s
  44   9.74846806e+04  3.76370184e+04  1.22e+00 7.77e-05  3.55e-01    56s
  45   9.44766076e+04  4.13981461e+04  1.12e+00 6.83e-05  3.17e-01    58s
  46   9.19686597e+04  4.39776972e+04  1.04e+00 6.15e-05  2.90e-01    59s
  47   8.93603861e+04  4.60022128e+04  9.51e-01 5.61e-05  2.66e-01    60s
  48   8.76859143e+04  4.77486744e+04  8.79e-01 5.18e-05  2.45e-01    62s
  49   8.62813695e+04  4.92868516e+04  8.24e-01 4.82e-05  2.28e-01    63s
  50   8.56027759e+04  5.13966220e+04  7.84e-01 4.31e-05  2.07e-01    65s
  51   8.48957004e+04  5.19816373e+04  7.54e-01 4.15e-05  2.00e-01    66s
  52   8.32394680e+04  5.41928043e+04  6.86e-01 3.54e-05  1.74e-01    67s
  53   8.17250808e+04  5.59114211e+04  5.84e-01 2.76e-05  1.45e-01    69s
  54   7.87495275e+04  5.77675718e+04  4.50e-01 2.28e-05  1.18e-01    70s
  55   7.65910015e+04  5.92651673e+04  3.59e-01 1.90e-05  9.66e-02    71s
  56   7.36448198e+04  6.01303203e+04  2.43e-01 1.67e-05  7.76e-02    73s
  57   7.19088527e+04  6.22074540e+04  1.87e-01 1.11e-05  5.42e-02    74s
  58   7.05686900e+04  6.27428127e+04  1.42e-01 9.49e-06  4.46e-02    76s
  59   7.01475919e+04  6.33239274e+04  1.29e-01 7.82e-06  3.82e-02    77s
  60   6.93732340e+04  6.35928516e+04  1.06e-01 7.05e-06  3.31e-02    78s
  61   6.89873744e+04  6.40345232e+04  9.49e-02 5.74e-06  2.79e-02    80s
  62   6.84844975e+04  6.43410345e+04  8.14e-02 4.73e-06  2.34e-02    81s
  63   6.78596306e+04  6.45634167e+04  6.38e-02 3.96e-06  1.90e-02    83s
  64   6.75753884e+04  6.46952693e+04  5.53e-02 3.51e-06  1.68e-02    84s
  65   6.73778881e+04  6.48756450e+04  4.98e-02 2.91e-06  1.44e-02    86s
  66   6.71801464e+04  6.49678697e+04  4.43e-02 2.59e-06  1.28e-02    87s
  67   6.69549892e+04  6.50665396e+04  3.79e-02 2.20e-06  1.09e-02    89s
  68   6.67230183e+04  6.51738241e+04  3.13e-02 1.80e-06  8.99e-03    91s
  69   6.65736783e+04  6.52317274e+04  2.75e-02 1.55e-06  7.81e-03    92s
  70   6.64142693e+04  6.53209532e+04  2.30e-02 1.15e-06  6.16e-03    94s
  71   6.62266310e+04  6.53731434e+04  1.77e-02 9.21e-07  4.86e-03    95s
  72   6.60582708e+04  6.54437549e+04  1.27e-02 6.04e-07  3.38e-03    96s
  73   6.59096102e+04  6.54840234e+04  8.30e-03 4.39e-07  2.36e-03    98s
  74   6.58326701e+04  6.55028840e+04  6.16e-03 3.68e-07  1.87e-03    99s
  75   6.58071568e+04  6.55173581e+04  5.42e-03 3.13e-07  1.62e-03   100s
  76   6.57869278e+04  6.55323894e+04  4.84e-03 2.55e-07  1.39e-03   102s
  77   6.57552578e+04  6.55482722e+04  3.93e-03 2.00e-07  1.11e-03   103s
  78   6.57239353e+04  6.55730469e+04  3.02e-03 1.13e-07  7.49e-04   105s
  79   6.56918208e+04  6.55807579e+04  2.13e-03 8.75e-08  5.54e-04   106s
  80   6.56800478e+04  6.55833962e+04  1.80e-03 7.89e-08  4.85e-04   108s
  81   6.56739060e+04  6.55857870e+04  1.65e-03 7.19e-08  4.41e-04   109s
  82   6.56604181e+04  6.55889154e+04  1.27e-03 6.18e-08  3.62e-04   111s
  83   6.56544422e+04  6.55930475e+04  1.11e-03 4.96e-08  3.04e-04   113s
  84   6.56462729e+04  6.55950294e+04  8.97e-04 4.31e-08  2.55e-04   115s
  85   6.56347720e+04  6.56000346e+04  5.94e-04 2.83e-08  1.71e-04   117s
  86   6.56298669e+04  6.56020877e+04  4.66e-04 2.18e-08  1.35e-04   119s
  87   6.56254123e+04  6.56038452e+04  3.62e-04 1.65e-08  1.04e-04   121s
  88   6.56211635e+04  6.56052583e+04  2.57e-04 1.24e-08  7.63e-05   122s
  89   6.56169905e+04  6.56067541e+04  1.58e-04 8.06e-09  4.90e-05   124s
  90   6.56133437e+04  6.56081336e+04  7.56e-05 4.33e-09  2.52e-05   126s
  91   6.56121677e+04  6.56085089e+04  5.07e-05 3.26e-09  1.79e-05   128s
  92   6.56108899e+04  6.56088374e+04  2.33e-05 2.23e-09  1.06e-05   129s
  93   6.56102570e+04  6.56092051e+04  1.11e-05 1.16e-09  5.45e-06   130s
  94   6.56099389e+04  6.56093319e+04  5.37e-06 7.76e-10  3.29e-06   131s
  95   6.56098815e+04  6.56094726e+04  4.28e-06 3.81e-10  2.03e-06   133s
  96   6.56097789e+04  6.56095262e+04  2.51e-06 2.39e-10  1.26e-06   134s
  97   6.56097299e+04  6.56095824e+04  1.63e-06 9.84e-11  6.79e-07   135s
  98   6.56096722e+04  6.56096114e+04  6.78e-07 3.09e-11  2.65e-07   137s
  99   6.56096412e+04  6.56096173e+04  2.01e-07 1.93e-11  1.14e-07   138s
 100   6.56096347e+04  6.56096239e+04  1.04e-07 6.63e-12  4.82e-08   139s
 101   6.56096282e+04  6.56096269e+04  1.05e-08 8.44e-13  5.64e-09   140s
 102   6.56096274e+04  6.56096273e+04  1.01e-09 6.25e-13  5.81e-10   141s

Barrier solved model in 102 iterations and 141.06 seconds (84.15 work units)
Optimal objective 6.56096274e+04

Crossover log...

   41620 DPushes remaining with DInf 5.1780300e-05               142s
   15480 DPushes remaining with DInf 5.1780300e-05               145s
    2980 DPushes remaining with DInf 0.0000000e+00               150s
    1299 DPushes remaining with DInf 0.0000000e+00               155s
     491 DPushes remaining with DInf 0.0000000e+00               161s
       0 DPushes remaining with DInf 0.0000000e+00               165s

    3665 PPushes remaining with PInf 2.6732668e-04               165s
    3571 PPushes remaining with PInf 3.9756959e-04               165s
       0 PPushes remaining with PInf 0.0000000e+00               169s

  Push phase complete: Pinf 0.0000000e+00, Dinf 1.2544330e-01    170s

Iteration    Objective       Primal Inf.    Dual Inf.      Time
   26531    6.5609627e+04   0.000000e+00   1.254433e-01    170s
Crossover time: 28.89 seconds (16.88 work units)
   26545    6.5609627e+04   0.000000e+00   0.000000e+00    171s

Solved in 26545 iterations and 170.62 seconds (101.29 work units)
Optimal objective  6.560962735e+04

AMPL MP initial flat model has 268733 variables (0 integer, 0 binary);
Objectives: 1 linear; 
Constraints:  491079 linear;

AMPL MP final model has 268733 variables (0 integer, 0 binary);
Objectives: 1 linear; 
Constraints:  491079 linear;


Gurobi 13.0.0: optimal solution; objective 65609.62735
26545 simplex iterations
102 barrier iterations

In [21]:

results_lca.variables['TotalLCIA']

results_lca.variables['TotalLCIA']

Out[21]:

	TotalLCIA	Run
TTEQ	841.206599	0
TTHH	357.779093	0

Formatting the results¶

In [25]:

results = results_lca  # choose among results_tc and results_lca

results = results_lca # choose among results_tc and results_lca

In [26]:

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 [27]:

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'})

In [28]:

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'})

In [29]:

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[29]:

	Name	Run	Production	Capacity
2	ATM_CCS	0	336.804807	2.167426
4	BEV_BATT	0	0.000000	91.160423
9	BOAT_FREIGHT_METHANOL	0	966.908558	30.521103
14	BUS_COACH_HYDIESEL	0	551.338240	6.381230
15	CAR_BEV	0	2756.691201	191.436889

In [30]:

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[30]:

	Name	Run	Import
7	BIODIESEL	0	849.952695
29	CO2_ATM	0	336.804807
31	CO2_EMISSIONS	0	176.167331
64	ELEC_EXPORT	0	587.642514
76	H2_RE	0	7132.203267

In [31]:

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¶

In [40]:

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, )

In [41]:

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 [42]:

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.