Choose datasets¶

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Import Necessary Libraries and Modules¶

First, we import the essential libraries and modules required for this tutorial:

• pandas as pd: A powerful data manipulation library.
• Energyscope from energyscope.energyscope: The main class to initialize and run the EnergyScope model.
• infrastructure_qc_2020 from energyscope.models: Predefined models for different regions or configurations.
• postprocessing from energyscope.result: Functions and classes for handling and processing results.
• plot_sankey from energyscope.plots: Functions to create Sankey diagrams for visualizing energy flows.

In [1]:

import pandas as pd
from energyscope.energyscope import Energyscope
from energyscope.models import infrastructure_qc_2020
from energyscope.result import postprocessing
from energyscope.plots import  plot_sankey

import pandas as pd from energyscope.energyscope import Energyscope from energyscope.models import infrastructure_qc_2020 from energyscope.result import postprocessing from energyscope.plots import plot_sankey

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Define Solver Options¶

The solver options determine how the optimization solver behaves during the calculation. In this section, we demonstrate two methods to specify these options:

Directly in the Code¶

You can define the solver options directly within your script by creating a dictionary.

In [2]:

solver_options = {
    'solver': 'cplex',
    'presolve_eps': 5e-9,
    'presolve_fixeps': 7e-10,
    'mipgap': 1e-10,
    'display_eps': 1e-10,
    'omit_zero_rows': 1,
    'omit_zero_cols': 1,
    'show_stats': 1,
    'solver_msg': 0,
    'cplex_options': 'integrality=5e-7'
}

solver_options = { 'solver': 'cplex', 'presolve_eps': 5e-9, 'presolve_fixeps': 7e-10, 'mipgap': 1e-10, 'display_eps': 1e-10, 'omit_zero_rows': 1, 'omit_zero_cols': 1, 'show_stats': 1, 'solver_msg': 0, 'cplex_options': 'integrality=5e-7' }

• Note: These options are specific to the CPLEX solver and control aspects like presolve tolerances, MIP gap, and output display.

Loading from a CSV File¶

Alternatively, you can load the solver options from a CSV file, which can be convenient for managing configurations externally.

In [3]:

solver_options = pd.read_csv('tutorial_input/solver_options_infrastructure_qc_2020.csv')
solver_options = dict(zip(solver_options['Option'], solver_options['Value']))   # Reformat dict

solver_options = pd.read_csv('tutorial_input/solver_options_infrastructure_qc_2020.csv') solver_options = dict(zip(solver_options['Option'], solver_options['Value'])) # Reformat dict

• Here, we read the CSV file into a Pandas DataFrame and then convert it into a dictionary that can be used by the model.

After loading the solver options, you can inspect them to ensure they have been set correctly:

In [4]:

solver_options

solver_options

Out[4]:

{'solver': 'cplex',
 'presolve_eps': '5.00E-09',
 'presolve_fixeps': '7.00E-10',
 'mipgap': '1.00E-10',
 'display_eps': '1.00E-10',
 'omit_zero_rows': '1',
 'omit_zero_cols': '1',
 'show_stats': '1',
 'solver_msg': '0',
 'cplex_options': 'integrality=5e-7'}

---

Initialize and Run the Model with Specific Dataset and Solver Options¶

In this section, we initialize the EnergyScope model using the infrastructure_qc_2020 dataset and apply the custom solver options defined earlier.

Initialize the Model¶

Create an instance of the Energyscope class with the infrastructure_qc_2020 model and the specified solver options.

In [5]:

es_infra_qc = Energyscope(model=infrastructure_qc_2020, solver_options=solver_options)

es_infra_qc = Energyscope(model=infrastructure_qc_2020, solver_options=solver_options)

• Model: The infrastructure_qc_2020 model represents the energy system of Quebec with monthly resolution.
• Solver Options: The solver_options dictionary configures the solver's behavior during optimization.

Run the Optimization¶

Execute the calculation to solve the optimization problem.

In [6]:

results_qc = es_infra_qc.calc()

results_qc = es_infra_qc.calc()

Presolve eliminates 18607 constraints and 13787 variables.
Adjusted problem:
12678 variables:
	814 binary variables
	11864 linear variables
17405 constraints, all linear; 107580 nonzeros
	7892 equality constraints
	9513 inequality constraints
1 linear objective; 1 nonzero.

Setting $presolve_fixeps < 4.26e-10 or >= 5.26e-09
could change presolve results.

CPLEX 22.1.1.0: integrality=5e-7

• This step runs the solver (e.g., CPLEX) with the specified options and computes the optimal energy system configuration.
• Output: You may see console outputs indicating the solver's progress and any warnings or errors.

Post-Process the Results¶

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

In [7]:

results_qc = postprocessing(results_qc)

results_qc = postprocessing(results_qc)

• The postprocessing function enriches the results with additional analysis, making them ready for further interpretation and plotting.

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Visualize Results with a Sankey Diagram¶

A Sankey diagram is an effective tool for visualizing energy flows within the system, showing how energy sources are converted and consumed.

Generate the Sankey Diagram¶

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

In [8]:

fig = plot_sankey(results_qc)

fig = plot_sankey(results_qc)

• This function processes the results and generates a Sankey diagram object.

Display the Diagram¶

Render the Sankey diagram within the notebook for immediate visualization.

In [9]:

fig.show()

fig.show()

• The diagram will display in the output cell, providing a visual representation of the energy flows in the optimized system.

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

In [10]:

# fig.write_html("tutorial_output/sankey_infrastructure_qc_2020.html")
# fig.write_image('tutorial_output/sankey_infrastructure_qc_2020.png')

# fig.write_html("tutorial_output/sankey_infrastructure_qc_2020.html") # fig.write_image('tutorial_output/sankey_infrastructure_qc_2020.png')

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By following these steps, you have:

• Imported the necessary libraries and modules.
• Defined solver options either directly or by loading from a CSV file.
• Initialized and ran the EnergyScope model using the infrastructure_qc_2020 dataset with custom solver configurations.
• Post-processed the results to compute KPIs and prepare for visualization.
• Visualized the energy flows using a Sankey diagram.

This tutorial demonstrates how to customize solver settings and work with different datasets, enabling you to perform more advanced analyses tailored to specific regions or scenarios.