# Run configuration¶

Note

See Run settings in the configuration reference for a complete listing of all available configuration options.

At a minimum, the run configuration must provide three settings, as shown in this example:

model: 'model_config/model.yaml'
mode: 'plan'
solver: 'glpk'


model specifies the path to the model configuration file for the model to be run. mode specifies whether the model should be run in planning (plan) or operational (operate) mode (see Running the model). Finally, solver specifies the solver to be used. Calliope has been tested with GLPK, Gurobi and CPLEX. Any of the solvers that Pyomo is compatible with should work.

Additional (optional) settings, including debug settings, can be specified in the run configuration. In particular, the run settings can override any model settings by specifying override, e.g.:

override:
techs:
nuclear:
costs:
monetary:
e_cap: 1000


Note

If run settings override the data_path setting and specify a relative path, that path will be interpreted as relative to the run settings file and not the model settings file being overridden.

Instead of directly overriding settings within the run configuration file using an override block, it is also possible to specify an additional model configuration file with overriding settings by using the model_override: path/to/model_override.yaml setting (the path given here is relative to the run configuration file).

The optional settings to adjust the timestep resolution and those for parallel runs are discussed below. For a complete list of the other available settings, see Run settings in the configuration reference.

Models must have a default timestep length (defined implicitly by the timesteps defined in set_t.csv), and all time series files used in a given model must conform to that timestep length requirement.

However, this default resolution can be adjusted over parts of the dataset via time in the run settings (only support for downsampling is available).

There are two available ways to adjust resolution:

1. A CSV file that contains a time resolution series, via time.file.
2. A uniform resolution reduction, via time.resolution.
3. Application of one or more of the masks defined in calliope.time_masks, via a list of masks given in time.masks. See Time masks in the API documentation for the available masking functions. Options can be passed to this the masking functions by specifying options. time.resolution can still be specified and will define the uniform resolution reduction applied to all masked areas.

The following example demonstrates this third way:

time:
resolution: 24
options:
tech: 'wind_offshore'
what: 'min'


This passes the options, tech='wind_offshore', what='min' to the specified masking function. In this case, the result is that the function looks for the week where the resource data for the wind_offshore technology is minimal, and returns a mask for the rest of the time series. That (unmasked) week is retained at the original resolution, the rest of the (masked) data is resampled to 24-hourly timesteps.

If specifying a file (the path is relative to the run configuration file), it must contain two columns. The first is integer indices for the timesteps. The second contains either:

• a positive integer (signifying that this and following timesteps should be summarized with the new, given resolution)
• $$-1$$ (following a positive integer and marking the timesteps that are summarized)
• or $$0$$ (no adjustment made to this timestep).

The following example file illustrates this:

0,3
1,-1
2,-1
3,3
4,-1
5,-1
6,0
7,0
8,0


Here, the first three timesteps will be summarized into one (0,1,2), as will the next three timesteps (3,4,5), and the final three timesteps are not touched (6,7,8).

## Settings for parallel runs¶

The run settings can also include a parallel section.

This section is parsed when using the calliope generate command-line tool to generate a set of runs to be executed in parallel (see Parallel runs). A run settings file defining parallel can still be used to execute a single model run, in which case the parallel section is simply ignored.

The concept behind parallel runs is to specify a base model (via the run configuration’s model setting), then define a set of model runs using this base model, but overriding one or a small number of settings in each run. For example, one could explore a range of costs of a specific technology and how this affects the result.

Specifying these iterations is not (yet) automated, they must be manually entered under parallel.iterations: section. However, Calliope provides functionality to gather and process the results from a set of parallel runs (see Analyzing results).

At a minimum, the parallel block must define:

• a name for the run
• the environment of the cluster (if it is to be run on a cluster), currently supported is bsub and qsub. In either case, the generated scripts can also be run manually
• iterations: a list of model runs, with each entry giving the settings that should be overridden for that run. The settings are run settings, so, for example, time.function can be overridden. Because the run settings can themselves override model settings, via override, model settings can be specified here, e.g. override.techs.nuclear.costs.monetary.e_cap.

The following example parallel settings show the available options. In this case, two iterations are defined, and each of them overrides the nuclear e_cap costs (override.techs.nuclear.costs.monetary.e_cap):

parallel:
name: 'example-model'  # Name of this run
environment: 'bsub'  # Cluster environment, choices: bsub, qsub
# Execute additional commands in the run script before starting the model
pre_run: ['source activate pyomo']
# Execute additional commands after running the model
post_run: []
iterations:
- override.techs.nuclear.costs.monetary.e_cap: 1000
- override.techs.nuclear.costs.monetary.e_cap: 2000
resources:
wall_time: 30  # Set to request a non-default run time in minutes
memory: 1000  # Set to request a non-default amount of memory in MB


This also shows the optional settings available:

• data_path_adjustment: replaces the data_path setting in the model configuration during parallel runs only
• pre_run and post_run: one or multiple lines (given as a list) that will be executed in the run script before / after running the model. If running on a computing cluster, pre_run is likely to include a line or two setting up any environment variables and activating the necessary Python environment.
• resources: specifying these will include resource requests to the cluster controller into the generated run scripts. threads, wall_time, and memory are available. Whether and how these actually get processed or honored depends on the setup of the cluster environment.

For an iteration to override more than one setting at a time, the notation is as follows:

iterations:
- first_option: 500
second_option: 10
- first_option: 600
second_option: 20


See Parallel runs in the section on running models for details on how to use the parallel settings to generate and execute parallel runs.

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