Interpolate a model into a solver-ready scenario

Builds an interpolated scenario from a model via the mapping pipeline: collects sets from the model objects, builds the membership / calendar / lifespan / value / constraint / cost mappings, extracts and interpolates the numeric parameters over the milestone years, and (optionally) folds, prunes and validates the result. The returned scenario is ready for solve_model() / solve_scenario().

Usage

interpolate_model(
  mod,
  name = NULL,
  ...,
  desc = NULL,
  ondisk = FALSE,
  overwrite = FALSE,
  fold = FALSE,
  sparse = TRUE,
  prune = TRUE,
  validate = TRUE,
  code = NULL,
  kvl = FALSE,
  boundary_prices = NULL,
  .prefilter = FALSE,
  verbose = isVerbose()
)

# S4 method for class 'model'
interpolate(object, ...)

Arguments

mod

a model object, or a scenario (its @model is re-interpolated).

name

character scenario name. If NULL, a default scen_<model> is used (with a warning).

...

additional energyRt objects folded into the model BEFORE the pipeline runs: settings, config, calendar, horizon, a whole repository, or individual model "bricks" (technology, commodity, storage, ...). This is how a scenario overrides or extends the model (e.g. pass a sampled calendar to interpolate on a reduced time resolution).

desc

character scenario description.

ondisk

logical; store each parameter's data in the on-disk parameter store rather than the in-memory @data slot. FALSE (default) keeps data in memory, which the solver writers read directly; TRUE suits very large models (data is materialised back to memory at solve time).

overwrite

logical; overwrite an existing on-disk scenario of the same name.

fold

logical or character; whole-column "fold" of trimmable dimensions to NA wildcards to shrink the data. TRUE folds region + timeslice + year; FALSE (default) folds nothing; a character vector selects dims among region, timeslice, year, comm, tech, stg, trade. The wildcards are substituted by an artificial set member in the written model files only; the scenario object keeps them and getData() expands them.

A dimension folds only where the value is uniform across its WHOLE set, so folding never changes the solution — it changes how the same numbers are stored. year matters most in weather-heavy models: one weather series repeated across milestone years is usually the largest parameter, and region/timeslice alone do not touch it. Measured on a 4-region d365_h24 model over 7 milestones, adding year took pWeather from 245,259 to 35,037 rows (-85.7%) with a bit-identical objective on GLPK and Pyomo/HiGHS. A model whose weather genuinely differs by year simply does not fold.

sparse

logical; the storage knob, and the user-facing switch between drop_default_values() and expand_default_values(). TRUE drops value == defVal rows; FALSE materialises the default over each parameter's full domain (the form GAMS needs) and then trims rows no map indexes. Folding applies to either.

prune

logical; apply the per-parameter misc$prune$value filter via drop_default_values() - rows whose interpolated value equals that parameter's flagged value are dropped (lossless: an absent tuple reads as the default, which equals the dropped value). One parameter is flagged today (pWeather). Superseded by sparse's value = form; retained for compatibility.

validate

logical; run validate_scenario_parameters() after interpolation (schema, duplicate keys, map/parameter coverage, missing governing constraints, calendar chronology).

code

optional named list overriding solver source-code blocks (GLPK, GAMS, JuMP, PYOMOConcrete, ...), each either a script-file path or a character vector of lines. Lets a model-script version be supplied at interpolation time without rebuilding sysdata (handy to A/B templates).

kvl

logical or NULL; build the Kirchhoff voltage-law cycle constraints for meshed trade networks. NULL (default) engages them when the network has cycles and reactances to use.

boundary_prices

optional data.frame pricing import/export stubs for trade routes dropped by a SPATIAL SAMPLE (a geoscales::filter_geoscale() subset passed via ...); see subset_model_regions() for the columns. Ignored (with a warning) when no sampled geoscale is supplied.

.prefilter

EXPERIMENTAL, default FALSE. Restrict each model object's data to the timeslices and regions the SCENARIO declares before interpolating it, rather than interpolating everything and discarding the excess afterwards.

On the sampled-calendar recipe – a full-year model with a subset calendar handed to this function – the default order expands all 8,760 timeslices in ob2mi and then keeps 96. The parameter filters that follow are cheap (0.05s on a 5-node model); the cost is the interpolation they cannot undo.

Off by default because it is not obviously safe: anything deriving a relation from the full declared grid rather than from the calendar would see a narrower input. Compare objectives before relying on it.

verbose

logical; print per-step progress. This also governs the variant-expansion report – how many process objects were expanded and how many constraints were generated for them. The generated constraints themselves are retrievable with getObject(scen, class = "constraint"); each carries a readable desc and, in misc$.variant_source, the object it was derived from.

object

as mod, for the method dispatched on a scenario.

Value

an interpolated scenario object.

Usage

Arguments

Value

See also