Aggregate a model to a coarser set of regions

Build a smaller model from a finer one by collapsing its regions onto a coarser geoframe of a geoscales::GeoScale. The result is a model in its own right: it can be interpolated, solved and reported like any other.

Usage

aggregate_model_regions(
  object,
  geoscale = NULL,
  level,
  clusters = NULL,
  as = c("clusters", "objects"),
  name = NULL,
  verbose = isVerbose(),
  .trade_parts = NULL
)

Arguments

object

A model.

geoscale

A geoscales::GeoScale whose finest geoframe contains the model's regions. NULL (default) uses the model's own.

level

Name of the target geoframe, coarser than the model's regions.

clusters

NULL (default) collapses each coarse region to one value. Otherwise a list keyed by process group (see get_process_groups()) that keeps some of the spread instead – see the section below. A group that is not named is aggregated plainly, so clustering is opted into one family at a time.

as

With clusters, the shape of the result: "clusters" (default) gives one object per family, region variants becoming technology@cluster entries; "objects" gives one object per (family x cluster), named with the cluster's label (ECOA_W1), each keeping its own clusters. Same LP either way – what differs is how results are keyed.

name

Name for the aggregated model. NULL (default) appends level to the source model's name.

verbose

Report what was aggregated, merged and dropped.

.trade_parts

internal; a named vector mapping trade names to a part label, appended to the merge key so corridors in different parts stay separate instead of collapsing into one merged object.

Value

A model declared over the coarser regions, carrying the pruned geoscale. With clusters, the grouping is attached and readable with model_clusters().

Details

Every region-keyed slot is recast. Extensive quantities – capacities, demand, supply availability, import and export limits – are summed over the regions of each parent. Intensive ones – efficiencies, availability factors, costs, weather values – take a mean weighted by the object's own size in each region, so an aggregated value stays within the range of the values it came from. When the weights of a group are all zero the mean is unweighted, which keeps a group of empty regions at their common value rather than undefined.

Trade is different: a trade object is one region pair, so corridors that land inside a single parent region become internal and are dropped, and the rest merge – their capacities add, and their loss factors take the capacity-weighted mean. Corridors merge only when they agree on commodity and on cluster structure, so tranched and untranched routes stay apart.

Keeping the spread as clusters

Collapsing a zone's regions to one average technology throws away the difference between its cheap sites and its dear ones, and a model that can no longer see that difference builds the average everywhere. clusters keeps some of it: the fine regions are grouped into k connected clusters and carried into technology@cluster, one parallel sub-process each, with its own capacity, availability and costs.

Settings are per technology family, because one k for the model is meaningless – a coal fleet groups on cost and efficiency, a wind fleet on resource quality and profile shape:

get_process_groups(mod)                                   # what can be merged
process_cluster_sweep(mod, "ECOA", level = "zone")     # where its structure is
aggregate_model_regions(
  mod, level = "zone",
  clusters = list(ECOA = list(k = 3, features = c("invcost", "eff")),
                  EWIN = list(k = 8, features = wind_shape)))

Each element is a list with k and optionally features; a bare number is shorthand for list(k = n). A bare number for the whole argument is accepted only when the model has ONE group. k = "auto" picks the best average silhouette, which is a rule rather than an answer – sweep and look instead.

k runs from the number of coarse regions (one cluster each – exactly what clusters = NULL gives) to the number of fine regions (1:1, nothing averaged, which on a model whose fine regions already pool inside each coarse region reproduces the fine objective exactly). Clusters cannot straddle a coarse region.

A family that ALREADY has clusters – wind by site grade – keeps them: the two groupings compose, GOOD in W1 becoming GOOD_W1.

See also

subset_model_regions(), which takes a sub-territory at the model's own resolution instead of coarsening the whole of it; get_process_groups(), process_cluster_sweep(), model_clusters().

Examples

repo <- newRepository(
  "mini",
  newCommodity("COA", unit = "MMBtu"),
  newCommodity("ELC", unit = "MWh", timeframe = "ANNUAL"),
  newSupply("SUP_COA", commodity = "COA", supply = data.frame(cost = 1)),
  newTechnology("ECOA", input = list(comm = "COA"), output = list(comm = "ELC"),
                cap2act = 1, invcost = list(invcost = 100),
                olife = list(olife = 30)),
  newDemand("DEM", commodity = "ELC",
            demand = data.frame(region = c("W1", "W2"), year = 2020, demand = 10))
)
mod <- newModel("MINI", data = repo, calendar = calendars$annual,
                region = c("W1", "W2"), horizon = horizons$Y2020_2060_by_5)

# roll the two regions up into the zone they share
agg <- aggregate_model_regions(mod, geoscale = topia$geoscales$squares,
                               level = "zone")
get_region(agg)
#> [1] "WEST"