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 from energyscope.models: The specific model configuration we will use, which focuses on energy infrastructure.
• 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 [1]:

import pickle
from energyscope.energyscope import Energyscope
from energyscope.models import infrastructure_ch_2050
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 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 [2]:

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 [3]:

# Export model to AMPL
es_infra_ch.export_ampl(mod_filename='tutorial_output/infrastructure_ch_2050.mod',dat_filename='tutorial_output/infrastructure_ch_2050.dat')

# Export model to GLPK
es_infra_ch.export_glpk(mod_filename='tutorial_output/infrastructure_ch_2050.mod',dat_filename='tutorial_output/infrastructure_ch_2050.dat')

# Export model to AMPL es_infra_ch.export_ampl(mod_filename='tutorial_output/infrastructure_ch_2050.mod',dat_filename='tutorial_output/infrastructure_ch_2050.dat') # Export model to GLPK es_infra_ch.export_glpk(mod_filename='tutorial_output/infrastructure_ch_2050.mod',dat_filename='tutorial_output/infrastructure_ch_2050.dat')

Solve the Model¶

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

In [4]:

results_ch = es_infra_ch.calc()

results_ch = es_infra_ch.calc()

Gurobi 11.0.0:Gurobi 11.0.0: optimal solution; objective 8151.373244
6079 simplex iteration(s)
1 branching node(s)
absmipgap=0.102405, relmipgap=1.25629e-05

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 [5]:

results_ch = postprocessing(results_ch)

results_ch = postprocessing(results_ch)

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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 [6]:

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 [7]:

# 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 [8]:

fig.show()

fig.show()

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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 [9]:

def save_result_to_pickle(data, filename):
    """
    Save the Result object to a pickle file.

    Parameters:
        data: The Result object to save.
        filename (str): The file path to save the object to.
    """
    with open(filename, 'wb') as fp:
        pickle.dump(data, fp, protocol=pickle.HIGHEST_PROTOCOL)

def save_result_to_pickle(data, filename): """ Save the Result object to a pickle file. Parameters: data: The Result object to save. filename (str): The file path to save the object to. """ with open(filename, 'wb') as fp: pickle.dump(data, fp, protocol=pickle.HIGHEST_PROTOCOL)

Define Load Function¶

Create a function to load the Result object from a pickle file.

Note: These utility functions could be integrated into the EnergyScope library for convenience.

In [10]:

def load_result_from_pickle(filename):
    """
    Load the Result object from a pickle file.

    Parameters:
        filename (str): The file path to load the object from.

    Returns:
        The loaded Result object.
    """
    with open(filename, 'rb') as handle:
        result = pickle.load(handle)
    return result

def load_result_from_pickle(filename): """ Load the Result object from a pickle file. Parameters: filename (str): The file path to load the object from. Returns: The loaded Result object. """ with open(filename, 'rb') as handle: result = pickle.load(handle) return result

Save the Results¶

Use the save_result_to_pickle function to save the results to a file.

In [11]:

save_result_to_pickle(results_ch, "tutorial_input/infrastructure_ch_2050.pickle")

save_result_to_pickle(results_ch, "tutorial_input/infrastructure_ch_2050.pickle")

Clear the Results Variable¶

Empty the results_ch variable to simulate a fresh environment.

In [12]:

results_ch = None

results_ch = None

Load the Results¶

Load the previously saved results using the load_result_from_pickle function.

In [13]:

results_ch = load_result_from_pickle("tutorial_input/infrastructure_ch_2050.pickle")

results_ch = load_result_from_pickle("tutorial_input/infrastructure_ch_2050.pickle")

Display Total Cost¶

Access and display the total cost from the loaded results to verify that the data was correctly saved and loaded.

In [14]:

results_ch.variables['TotalCost']

results_ch.variables['TotalCost']

Out[14]:

	TotalCost	Run
0	8151.373244	0

This should output a table showing the total cost of the optimized energy system configuration.

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By following these steps, you can perform a basic run of the EnergyScope model, visualize the results, and save/load the data for future use. This tutorial serves as a foundation for more complex analyses and customizations.