Basic run¶

First, we import the essential libraries and modules that will be used throughout this tutorial:

• pickle: Used for saving and loading Python objects to and from files.
• Energyscope from energyscope.energyscope: The main class for initializing and running the EnergyScope model.
• infrastructure_ch_2050, Model from energyscope.models: The specific model configuration we will use, which focuses on energy infrastructure. And the object Model to create your own model from external files.
• postprocessing from energyscope.result: Functions for processing and analyzing the results after optimization.
• plot_sankey from energyscope.plots: A function to generate Sankey diagrams for visualizing energy flows.

In [67]:

import pickle
from energyscope.energyscope import Energyscope
from energyscope.models import infrastructure_ch_2050, Model
from energyscope.result import postprocessing
from energyscope.plots import plot_sankey

import pickle from energyscope.energyscope import Energyscope from energyscope.models import infrastructure_ch_2050, Model from energyscope.result import postprocessing from energyscope.plots import plot_sankey

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Initialize and Run the Model¶

In this section, we initialize the EnergyScope model using the infrastructure dataset and perform a single optimization run.

Initialize the Model¶

Create an instance of the Energyscope class with the infrastructure model. This sets up the model with predefined parameters and datasets.

In [68]:

es_infra_ch = Energyscope(model=infrastructure_ch_2050)

es_infra_ch = Energyscope(model=infrastructure_ch_2050)

Export Model to AMPL and GLPK¶

For compatibility and further analysis, we export the model files in both AMPL and GLPK formats.

In [69]:

# Export model to AMPL
es_infra_ch.export_ampl(mod_filename='tutorial_output/AMPL_infrastructure_ch_2050.mod',dat_filename='tutorial_output/AMPL_infrastructure_ch_2050.dat')

# Export model to GLPK
es_infra_ch.export_glpk(mod_filename='tutorial_output/GLPK_infrastructure_ch_2050.mod',dat_filename='tutorial_output/GLPK_infrastructure_ch_2050.dat')

# Export model to AMPL es_infra_ch.export_ampl(mod_filename='tutorial_output/AMPL_infrastructure_ch_2050.mod',dat_filename='tutorial_output/AMPL_infrastructure_ch_2050.dat') # Export model to GLPK es_infra_ch.export_glpk(mod_filename='tutorial_output/GLPK_infrastructure_ch_2050.mod',dat_filename='tutorial_output/GLPK_infrastructure_ch_2050.dat')

Load External AMPL files¶

To load external files from AMPL you need to create a new Model as follow.

In [70]:

# Create you own Model object from imported AMPL files
Model_es_infra_ch = Model([
    ('mod', "tutorial_output/AMPL_infrastructure_ch_2050.mod"),
    ('dat', "tutorial_output/AMPL_infrastructure_ch_2050.dat")
])

# Create an instance of the Energyscope class with your own model.
es_infra_ch = Energyscope(model=Model_es_infra_ch)

# Create you own Model object from imported AMPL files Model_es_infra_ch = Model([ ('mod', "tutorial_output/AMPL_infrastructure_ch_2050.mod"), ('dat', "tutorial_output/AMPL_infrastructure_ch_2050.dat") ]) # Create an instance of the Energyscope class with your own model. es_infra_ch = Energyscope(model=Model_es_infra_ch)

Solve the Model¶

Perform the optimization calculation. This step runs the solver and computes the optimal configuration based on the model.

In [71]:

results_ch = es_infra_ch.calc()

results_ch = es_infra_ch.calc()

Gurobi 12.0.2:Gurobi 12.0.2: optimal solution; objective 9229.741872
6367 simplex iterations
1 branching node

Post-Process Results¶

After obtaining the raw results, we apply post-processing to compute Key Performance Indicators (KPIs) and prepare the data for visualization.

In [72]:

results_ch = postprocessing(results_ch)

# Example of how to extract post-processed data
results_ch.postprocessing['df_annual'].loc['WIND',:]

results_ch = postprocessing(results_ch) # Example of how to extract post-processed data results_ch.postprocessing['df_annual'].loc['WIND',:]

Out[72]:

	C_inv	C_maint	Annual_Prod	F_Mult	tau	C_op	C_inv_an	Annual_Use	Category	Category_2	Sector
Run
0	29312.4	458.0	40299.36	20.0	0.062433	0.0	1830.059062	40299.36	ELECTRICITY_MV	Wind	Electricity

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Visualize Results with a Sankey Diagram¶

A Sankey diagram is an effective way to visualize energy flows within the system.

Generate the Sankey Diagram¶

Use the plot_sankey function with the processed results to create the diagram.

In [73]:

fig = plot_sankey(results_ch)

fig = plot_sankey(results_ch)

Display the Diagram¶

Render the Sankey diagram within the notebook for immediate visualization.

Optional: You can save the diagram as an HTML file or an image for external use by uncommenting the following lines:

In [74]:

# fig.write_html("tutorial_output/sankey_infrastructure_ch_2050.html")
# fig.write_image('tutorial_output/sankey_infrastructure_ch_2050.png')

# fig.write_html("tutorial_output/sankey_infrastructure_ch_2050.html") # fig.write_image('tutorial_output/sankey_infrastructure_ch_2050.png')

In [75]:

fig.show(renderer="notebook")

fig.show(renderer="notebook")

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Save and Load Results¶

To preserve the results and avoid re-running the optimization every time, we can save the results to a file and load them later.

Define Save Function¶

Create a function to save the Result object using pickle.

In [76]: