Validate interpolated scenario parameters

Runs a set of post-interpolation consistency checks over the numeric / bounds / map parameters of a scenario and reports any issues. Checks:

• NA index columns: no NA in a parameter's dimSets id columns. When fold = TRUE, NA is permitted only in the trimmable dimensions (region / timeslice / vintage, which fold encodes as wildcards); when fold = FALSE, no NA is permitted in any id column.

• Schema: data columns match the declared dimSets (plus value, and type for bounds).

• Duplicate keys: no duplicate id tuples.

• Map vs parameter (correctness): every tuple of a value map is covered by its source parameter; a missing value would otherwise be silently replaced by the solver default.

• Parameter vs map (efficiency): value parameter rows lie within the union of their maps (no orphan / out-of-domain rows). After trimming this must hold exactly.

• Free meals: every object with flow variables has its governing constraints. A storage without its balance chain, or a trade corridor without its flow-capacity link, would supply the balance unbounded and solve to OPTIMAL with a meaningless objective – structural. Missing charge/discharge or availability bounds are advisory, since an open (Inf) side deliberately produces no equation.

• Calendar chronology: the timeslice successor chain (the storage balance and ramping order) is derived from the calendar's timetable row order; a row order that contradicts the timetable's own timeframe columns is structural.

Usage

validate_scenario_parameters(
  object,
  fold = TRUE,
  action = c("warn", "stop", "silent")
)

Arguments

object

an interpolated scenario.

fold

logical or character; whether (or in which dimensions) the scenario is folded, so NA wildcards are allowed there.

action

one of "warn" (default), "stop", "silent": how to report advisory issues.

Value

(invisibly) a data frame of findings with columns parameter, check, detail, severity.

Details

Structural findings always stop; action governs the advisory ones. interpolate_model() runs this automatically (validate = TRUE, action "warn"); call it directly to re-check a scenario or to collect the findings table.

Examples

# \donttest{
ek   <- topia$modules$electricity$R3
mod  <- newModel("TOPIA", data = ek$repo,
                 calendar = topia$modules$calendars$s4_h24,
                 region = ek$regions,
                 horizon = topia$modules$horizons$base,
                 discount = 0.05)
mod <- setGeoscale(mod, ek$geoscale)
scen <- interpolate_model(mod, name = "EX")
#> Cost declared at a coarse geoscale level:
#>   TBD_COA_WEST_CENTRAL@invcost: CENTRAL, WEST
#>   TBD_GAS_WEST_CENTRAL@invcost: CENTRAL, WEST
#> The cost is charged ONCE at that cell rather than at each child region, and is discounted at the children's rate. That is usually what a national cost datum means -- said here because it is a different number from naming the children individually.

# no rows means every parameter's domain, bounds and defaults agree
validate_scenario_parameters(scen, action = "silent")
# }