Choose datasets¶

---

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

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 5
      3 from energyscope.models import infrastructure_qc_2020
      4 from energyscope.result import postprocessing
----> 5 from energyscope.plots import  plot_sankey

File /builds/energyscope/energyscope/src/energyscope/plots.py:19
     13 import numpy as np
     14 from pandas.api.types import (
     15     is_integer_dtype,
     16     is_datetime64_any_dtype,
     17     is_period_dtype,
     18 )
---> 19 from ipywidgets import Dropdown, HBox, VBox, Button, Output
     20 from ipywidgets import Dropdown, HBox, VBox, Button, Output
     21 import matplotlib.pyplot as plt

ModuleNotFoundError: No module named 'ipywidgets'

---

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()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[6], line 1
----> 1 results_qc = es_infra_qc.calc()

File /builds/energyscope/energyscope/src/energyscope/energyscope.py:72, in Energyscope.calc(self, ds, parser)
     68 def calc(self, ds: Dataset = None, parser: Callable[[AMPL], Result] = parse_result) -> Result:
     69     """
     70     Calls AMPL with `df` as .dat and returns the parsed result.
     71     """
---> 72     if self.es_model.getSets().__len__() == 0:  # Check if AMPL instance is empty
     73         self._initial_run(ds=ds)
     75     # Solve the model

File /builds/energyscope/energyscope/src/energyscope/energyscope.py:30, in Energyscope.es_model(self)
     28 else:
     29     try:
---> 30         self.__es_model = AMPL(Environment(os.environ["AMPL_PATH"]))
     31     except SystemError:
     32         # Try to create the object a second time to prevent errors when starting `ampl_lic`
     33         self.__es_model = AMPL()

File /usr/local/lib/python3.10/os.py:680, in _Environ.__getitem__(self, key)
    677     value = self._data[self.encodekey(key)]
    678 except KeyError:
    679     # raise KeyError with the original key value
--> 680     raise KeyError(key) from None
    681 return self.decodevalue(value)

KeyError: 'AMPL_PATH'

• 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)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[7], line 1
----> 1 results_qc = postprocessing(results_qc)

NameError: name 'results_qc' is not defined

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

---

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)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[8], line 1
----> 1 fig = plot_sankey(results_qc)

NameError: name 'plot_sankey' is not defined

• 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(renderer="notebook")

fig.show(renderer="notebook")

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[9], line 1
----> 1 fig.show(renderer="notebook")

NameError: name 'fig' is not defined

• 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')

---

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.