Parameter overview

Parameter data is provided in .csv csv files starting with par_ and transferred into ParElement objects. The read-in data is used by the inheritance algorithm to compile the parameter data being used in the model's underlying optimization problem. This avoids huge but largely redundant input files and allows for lean and flexible models.

Read-in

In contrast to sets, the naming of parameter files and what data is provided in which file is entirely up to the user. For instance, data for the same parameter can be spread across different files and within one file multiple parameters can be defined.

Pivot format

Parameter data is provided in a pivot format where the column parameter determines the respective parameter and the column value its respective value. Additional columns specify the nodes within the hierarchical trees of sets a value applies to. Therefore, names of these columns are comprised of the set's name and, if the set is organized in a tree with multiple levels, the number of the respective level.

The all keyword can be used analogously to sets (see Time-steps). Columns that do not contain a set name (i.e. region, timestep, carrier, technology, mode, or id) or keyword (i.e. parameter and value) are not read in and can be used for documentation. To define multiple parameters within the same row, several numbered parameter/value columns can be provided (see par_techCost.csv).

As an example, the table below shows the definition of the discount rate parameter in the demo model found in the par_techInvest.csv file.

In case, a set is included in the dimensions of a parameter, but no node of that set is assigned for a respective value, the very top node of the tree is assigned instead.

Multiple dependencies on same set

Parameter can depend on multiple instances of the same set. In case of an emerging technology (see section on optional mappings for technologies) efficiency for instance can depend on two different kinds of time-steps: $Ts_{dis}$, the time-step a technology is being used, and $Ts_{exp}$, the time-step a technology was built. Following the order of sets in the definition, the first time-step specified in the input data will always relate to $Ts_{dis}$ and the second to expansion $Ts_{exp}$.

Accordingly, in table below the first column relates to $Ts_{dis}$ and the second to $Ts_{exp}$. Consequently, the specified efficiency applies to the first hour of every year for all heatpumps constructed 2020.

This concept equally applies if one of the sets is defined by more then one column. In the table below, the first and the second column are attributed to specify $Ts_{dis}$. Since the third column goes back up to the first level, AnyMOD realizes it refers to a different dimension and attributes it to $Ts_{exp}$. As a result, both efficiencies apply to heatpumps constructed 2020, but one row relates to the first hour of 2020 and the other to the first hour of 2030. So, this example also shows how the aging process of technologies can be modelled.

Inheritance

The combinations of sets or instances parameters are provided for within the input data do not need to match the instances required within the model. The required values are automatically compiled from the data provided. This facilitates the provision of input data and allows for a high level of flexibility in modelling.

If, for example, for one technology efficiency should be dependent on the time-step and for another irrespective of the time-step, this can simply be achieved in the model by providing efficiencies with an hourly granulation in the first and without specifying the temporal domain in the other case. In the demo problem for instance, heat-pump efficiencies are provided hourly in par_heatpumps.csv while for all other technologies par_techDispatch.csv defines efficiencies without specifying a dispatch time-step.

Modes of inheritance

When data is required but not defined for a specific combination of nodes, the algorithm moves along the vertices of the set trees to derive it. There are four different modes to do this, that will be explained based on the graph below. It shows the hierarchical tree of time-steps in the demo problem with data specified for some nodes (green), but not for the one required (red circle).

• upwards
  Moving upwards the tree until a node with data is reached. In the example this means to assign d001 the value 8.3. If data would already be provided for 2020, the direct ancestor of d001, this value would be used instead.
• average
  Moving downward until nodes with some data are reached and take the average of those value. In the example this means to assign d001 the value 2.9 ($=\frac{2.7 + 3.1}{2}$).
• sum
  Moving downward until nodes with some data are reached and take the sum of those value. In the example this means to assign d001 the value 5.8 ($=2.7 + 3.1$).
• sum*
  Moving downward until nodes who all have data assigned are reached and take the sum of those value. In the example this means to assign d001 the value 16.6 ($=2.1+6.8+4.5+3.2$).

Inheritance algorithm

The actual algorithm is outlined for the example of the discount rate parameter from the demo problem. The instances of the parameter required with in the model are given below, the provided input data was shown above.

Three steps are taken to populate the table with the required data:

1.    Direct matches
   

   First, values are assigned where instances of the required data exactly match the provided input data. For example, data for 2020 and WestSouth is specifically provided and can be used directly.

   timestep_superordinate_dispatch	region_expansion	value
   2020	West < WestNorth
   2030	West < WestNorth
   2020	West < WestSouth
   2030	West < WestSouth	0.015
   2020	East < EastNorth
   2030	East < EastNorth
   2020	East < EastSouth
   2030	East < EastSouth
   2020	East
   2030	East	0.03
   2020	West
   2030	West

2.    Go along inheritance rules
   

   Next, the algorithm consecutively applies the inheritance rules of the respective parameter. For each parameter these rules are documented in the parameter list. These rules assign sets to the modes of inheritance introduced above.

   1.    $Ts_{sup}$ → upwards
      

      For discount rates, the first rule is to try to obtain additional values by moving upwards in the hierarchical trees of time-steps. In the first row of input data, a value was provided irrespective of time-step, but only for the region West. This value is now assigned to the missing entries of that region.

      timestep_superordinate_dispatch	region_expansion	value
      2020	West < WestNorth
      2030	West < WestNorth
      2020	West < WestSouth
      2030	West < WestSouth	0.015
      2020	East < EastNorth
      2030	East < EastNorth
      2020	East < EastSouth
      2030	East < EastSouth
      2020	East
      2030	East	0.03
      2020	West	0.00
      2030	West	0.00

   2.    $R_{exp}$ → upwards
      

      Next, the concept is analogously applied to regions. By moving up the tree the value provided for East is now assigned for the descendant regions EastNorth and EastSouth as well.

      timestep_superordinate_dispatch	region_expansion	value
      2020	West < WestNorth	0.00
      2030	West < WestNorth	0.00
      2020	West < WestSouth	0.00
      2030	West < WestSouth	0.015
      2020	East < EastNorth
      2030	East < EastNorth	0.03
      2020	East < EastSouth
      2030	East < EastSouth	0.03
      2020	East
      2030	East	0.03
      2020	West	0.00
      2030	West	0.00

   3.    $Ts_{sup}$ → average and $R_{exp}$ → average
      

      The average mode tries to inherit values from descendant carriers. In this case, for none of these any data is defined, and consequently no additional data can be compiled.

3.    Use default value
   

   Finally, for all cases where no data was assigned, the default value is used instead. In case a parameter does not have a default, cases are dropped.

   timestep_superordinate_dispatch	region_expansion	value
   2020	West < WestNorth	0.00
   2030	West < WestNorth	0.00
   2020	West < WestSouth	0.00
   2030	West < WestSouth	0.015
   2020	East < EastNorth	0.02
   2030	East < EastNorth	0.03
   2020	East < EastSouth	0.02
   2030	East < EastSouth	0.03
   2020	East	0.02
   2030	East	0.03
   2020	West	0.00
   2030	West	0.00