model¶

This module defines the PREP-SHOT model. The model is created using the pyoptinterface library.

prepshot.model.check_line_symmetry(existing)[source]¶

Check that each existing line has the same capacity in both directions.

A line has one entry for each direction, and the cost rules charge half of the line cost on each entry. An entry with no equal entry in the opposite direction would thus be charged incorrectly.

Parameters: existing (dict) -- transmission_existing, keyed (zone1, zone2) or (zone1, zone2, commission_year).
Raises: ValueError -- If the capacity of a line differs between the two directions.
Return type: None

prepshot.model.create_model(params)[source]¶

Create the PREP-SHOT model.

Parameters: params (dict) -- Dictionary of parameters for the model.
Returns: Model object.
Return type: object

prepshot.model.define_active_zone_tech(model)[source]¶

Pull the sparse (zone, tech) set from model.params (built once at file-read time by load_data.compute_active_zone_tech) and derive the time-indexed key lists used by the constraint builders.

The (zone, tech) set itself is data-property (depends only on existing_fleet + expansion_candidates), so we compute it in load_data and reuse it unchanged across every PCM window. The (h, m, y, z, te) lists DO depend on the per-window time index sets, so they're materialised here.

Parameters: model (object) --
Return type: None

prepshot.model.define_basic_sets(model)[source]¶

Define sets for the model.

Parameters: model (object) -- Model object to be solved.
Return type: None

prepshot.model.define_complex_sets(model)[source]¶

Validate that every line param needed by the constraints is actually present in the loaded CSVs.

Refuses to silently default a missing entry. Each (active line, year) combination MUST have a row in the CSV.

Parameters: model (object) --
Return type: None

prepshot.model.define_constraints(model)[source]¶

Define constraints for the model.

Parameters: model (object) -- Model to be solved.
Return type: None

prepshot.model.define_model(params)[source]¶

This function creates the model class depending on predefined solver.

Parameters: params (dict) -- parameters for the model
Returns: A pyoptinterface Model object depending on the solver
Return type: object
Raises: ValueError -- Unsupported or undefined solver

prepshot.model.define_variables(model)[source]¶

Define variables for the model.

Parameters: model (object) -- Model to be solved.
Return type: None