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 [1]:

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

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[1], line 5
      3 from energyscope.models import infrastructure_ch_2050, Model
      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'

---

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/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')

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[3], line 2
      1 # Export model to AMPL
----> 2 es_infra_ch.export_ampl(mod_filename='tutorial_output/AMPL_infrastructure_ch_2050.mod',dat_filename='tutorial_output/AMPL_infrastructure_ch_2050.dat')
      4 # Export model to GLPK
      5 es_infra_ch.export_glpk(mod_filename='tutorial_output/GLPK_infrastructure_ch_2050.mod',dat_filename='tutorial_output/GLPK_infrastructure_ch_2050.dat')

File /builds/energyscope/energyscope/src/energyscope/energyscope.py:92, in Energyscope.export_ampl(self, mod_filename, dat_filename)
     84 """
     85 Exports the model and data to .mod and .dat files for AMPL.
     86 
   (...)
     89     dat_filename (str): Path to the .dat file to export the data.
     90 """
     91 # Reset the AMPL model
---> 92 self.es_model.reset()
     94 # Load the model and data files
     95 for file_type, file_path in self.model.files:

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'

Load External AMPL files¶

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

In [4]:

# 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 [5]:

results_ch = es_infra_ch.calc()

results_ch = es_infra_ch.calc()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
Cell In[5], line 1
----> 1 results_ch = es_infra_ch.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'

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 [6]:

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',:]

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[6], line 1
----> 1 results_ch = postprocessing(results_ch)
      3 # Example of how to extract post-processed data
      4 results_ch.postprocessing['df_annual'].loc['WIND',:]

NameError: name 'results_ch' is not defined

---

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 [7]:

fig = plot_sankey(results_ch)

fig = plot_sankey(results_ch)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[7], line 1
----> 1 fig = plot_sankey(results_ch)

NameError: name 'plot_sankey' is not defined

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 [8]:

# 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 [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

---

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 [10]:

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 [11]:

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 [12]:

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

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

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[12], line 1
----> 1 save_result_to_pickle(results_ch, "tutorial_input/infrastructure_ch_2050.pickle")

NameError: name 'results_ch' is not defined

Clear the Results Variable¶

Empty the results_ch variable to simulate a fresh environment.

In [13]:

results_ch = None

results_ch = None

Load the Results¶

Load the previously saved results using the load_result_from_pickle function.

In [14]:

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 [15]:

results_ch.variables['TotalCost']

results_ch.variables['TotalCost']

Out[15]:

	TotalCost	Run
0	9229.741872	0

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

---

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.