To get started with MESSAGEix, the following tutorials are provided as Jupyter notebooks, which combine code, sample output, and explanatory text.

A static, non-interactive version of each notebook can be viewed online using the links below. In order to execute the tutorial code or make modifications, read the Preparation section, next.


Getting tutorial files

If you installed MESSAGEix from source, all notebooks are in the tutorial directory.

If you installed MESSAGEix using Anaconda, download the notebooks using the message-ix command-line program. In a command prompt:

$ message-ix dl /path/to/tutorials


If you installed message_ix into a specific conda environment, that environment must be active in order for your system to find the message-ix command-line program, and also to run the Jupyter notebooks. Activate the environment as described in the conda documentation; for instance, if you used the name message_env:

$ conda activate message_env


By default, the tutorials for your installed version of MESSAGEix are downloaded. To download a different version, add e.g. --tag v1.2.0 to the above command. To download the tutorials from the development version, add --branch master.

Running tutorials

Using Anaconda

The nb_conda package is required. It should be installed by default with Anaconda. If it was not, install it:

$ conda install nb_conda
  1. Open “Jupyter Notebooks” from Anaconda’s “Home” tab (or directly if you have the option).

  2. Choose and open a tutorial notebook.

  3. Each notebook requires a kernel that executes code interactively. Check that the kernel matches your conda environment, and if necessary change kernels with the menu, e.g. KernelChange KernelPython [conda root].

From the command line

  1. Navigate to the tutorial folder. For instance, if message-ix dl was used above:

    $ cd /path/to/tutorials
  2. Start the Jupyter notebook:

    $ jupyter notebook

Westeros Electrified

This tutorial demonstrates how to model a very simple energy system, and then uses it to illustrate a range of framework features.

  1. Build the baseline model (westeros_baseline.ipynb).

  2. Add extra detail and constraints to the model

    1. Emissions

      1. Introduce emissions and a bound on the emissions (westeros_emissions_bounds.ipynb).

      2. Introduce taxes on emissions (westeros_emissions_taxes.ipynb).

    2. Represent both coal and wind electricity using a “firm capacity” formulation (westeros_firm_capacity.ipynb): each generation technology can supply some firm capacity, but the variable, renewable technology (wind) supplies less than coal.

    3. Represent coal and wind electricity using a different, “flexibility requirement” formulation (westeros_flexible_generation.ipynb), wherein wind requires and coal supplies flexibility.

    4. Variablity in energy supply and demand by adding sub-annual time steps, e.g. winter and summer (westeros_seasonality.ipynb).

    5. Using share constraints to depict policies, e.g. requiring renewables to make a a certain share of total electricity generation (westeros_share_constraint.ipynb).

    6. Add a fossil-resource supply curve for the coal power plant, (westeros_fossil_resource.ipynb).

    7. After the MESSAGE model has solved, use the message_ix.reporting module to ‘report’ results, e.g. do post-processing, plotting, and other calculations (westeros_report.ipynb).

Austrian energy system

This tutorial demonstrates a stylized representation of a national electricity sector model, with several fossil and renewable power plant types.

  1. Prepare the base model version, in Python (austria.ipynb) or in R (R_austria.ipynb).

  2. Plot results, in Python (austria_load_scenario.ipynb) or in R (R_austria_load_scenario.ipynb).

  3. Run a single policy scenario (austria_single_policy.ipynb).

  4. Run multiple policy scenarios. This tutorial has two notebooks: an introduction with some exercises (austria_multiple_policies.ipynb) and completed code for the exercises (austria_multiple_policies-answers.ipynb).

Code reference

The module message_ix.util.tutorial contains some helper code used to simplify the tutorials; see also reporting.computations.stacked_bar().

message_ix.util.tutorial.prepare_plots(rep: message_ix.reporting.Reporter, input_costs='$/GWa')None

Prepare rep to generate plots for tutorial energy models.

Makes available several keys:

  • plot activity

  • plot demand

  • plot extraction

  • plot fossil supply curve

  • plot capacity

  • plot new capacity

  • plot prices

To control the contents of each plot, use set_filters() on rep.

message_ix.util.tutorial.solve_modified(base: message_ix.core.Scenario, new_name: str)

Context manager for a cloned scenario.

At the end of the block, the modified Scenario yielded by solve_modified() is committed, set as default, and solved. Use in a with: statement to make small modifications and leave a variable in the current scope with the solved scenario.


>>> with solve_modified(base_scen, "new name") as s:
...     s.add_par( ... )  # Modify the scenario
...     # `s` is solved at the end of the block

.Scenario – Cloned from base, with the scenario name new_name and no solution.