An S4 class to represent default model configuration.

Config class is used to represent the default model configuration. It is stored in the model object and is used to initialize the scenario settings.

Slots

name

character. Name of the configuration object for own references, also can be used in functions to distinguish between different model or scenario instances.

desc

character. Description of the configuration object for own references.

region

character. Coma separated string of all region names in the model. All regions used in the model-objects should be listed here.

calendar

calendar. Calendar object with the model time parameters.

geoscale

ANY. Optional geoscales::Geoscale object describing the model's regions – their nesting into coarser levels, weights, and (optionally) geometry. Purely additional information about the labels in region, which remains authoritative; the geoscale is used for plotting, reporting and subsetting and never changes the optimisation model. NULL when unset. Typed ANY because geoscales is an optional (Suggests) dependency.

horizon

horizon. Horizon object with the model time parameters. The horizon defines the planning period and intervals of the model.

discount

data.frame. The model's two rates, which can be assigned by region and year. wacc annuitises investment into the equivalent annual cost; sdr discounts the stream of system costs in the objective. They are independent – there is no fallback from one to the other. As an ARGUMENT (newModel(discount = ), update(cfg, discount = )) this slot also accepts the shorthand discount, a single rate that plays both roles: discount = 0.05 is equivalent to data.frame(wacc = 0.05, sdr = 0.05). Per row you supply either discount, or both wacc and sdr – a partial pair or a mix of the two forms is an error. There is no discount column: the shorthand is expanded on entry, so the stored table always has both rates.

region: character. Region name to apply the parameter, NA for every region.
year: integer. Year to apply the parameter, NA for every year.
wacc: numeric. Weighted average cost of capital, used to annuitise investment costs into pTechEac / pStorageEac / pTradeEac. A process may override it with its own @invcost$wacc. Default is 0.
sdr: numeric. Social discount rate, used to build the cumulative discount factor applied to total system costs in the objective. A property of the model, never of an individual process. Default is 0.

discountFirstYear

logical. If TRUE, the discounting starts from the beginning of the year. If FALSE, the discounting starts from the end of the first year, i.e., the first year is not discounted.

optimizeRetirement

logical. Incidates if the retirement of capacities of the model objects should be optimized. Also requires the same parameter in the classes with capacitu, such as technology, storage, trade to be set to TRUE to be effective for a specific object.

defVal

data.frame. Default values of model parameters. The data frame with the default values for every parameter in the model, used to fill the missing values in the model objects. The values are used in the interpolation step. The data is stored in energyRt::.defVal object and can be overwritten by the user and supplied to the model as a parameter.

interpolation

data.frame. Default interpolation rules for every parameter in the model. The data frame with the default interpolation rules is stored in energyRt::.defInt object and can be overwritten by the user and supplied to the model as a parameter.

debug

data.frame. Artificial (dummy or sluck) variables to debug model infeasibility. Can be specified by commodities, regions, years, and slices.

variant_prefix

character. Named character vector giving the prefixes inserted into the names of expanded technology variants, keyed by variant dimension: c(vintage = "_VIN", cluster = "_CL"). A technology WIND with vintage 2030 and cluster "01" then becomes the set member WIND_VIN2030_CL01. Number the clusters rather than labelling them ("01", not "best"): the prefix already says what the axis is, so a label that repeats it reads as WIND_VIN2030_CLCL01. Model-wide, because these become solver set-member names; the scenario settings inherit the value and must agree with it. Each prefix must be non-empty, match ^[[:alnum:]_]+$, and the two must differ. A partial value such as c(cluster = "_RC") is merged over the defaults.

misc

list. Any additional data or information to store in the object.

An S4 class to represent default model configuration.

Slots

See also