--- jupytext: text_representation: extension: .md format_name: myst format_version: 0.13 jupytext_version: 1.10.3 kernelspec: display_name: Python 3 language: python name: python3 --- Variations in installed capacities (MW/period) ====== *indexed by: asset, data_type, energy, period, technology, test_case*
### Calculation All the equations below are valid for any realization and are therefore implicitly indexed by test case. Index *Technology* is directly deduced from the asset. Let $x_{a, p, data\_type}$ be the value returned by this KPI for a given asset $a$ for pathway's period $p$ optimized during at least one period of the considered pathway. Each data type of this KPI is computed as follows : #### Additional capacity $$ x_{a, p, add} = add_{a, p} - repow_{a, p} $$ With: - $add_{a, p}$ : The optimization result for variable representing the added capacity of asset $a$ during the period $p$. - $repow_{a, p}$ : The optimization result for variable representing the repowered capacity of asset $a$ during the period $p$. #### Decommissioned capacity $$ x_{a, p, less} = less_{a, p} - repow_{a, p} $$ With: - $less_{a, p}$ : The optimization result for variable representing the decommissioned capacity of asset $a$ during the period $p$.Modelling hint
For the first pathway step, values returned by this KPI can be very high for assets that are scenarized then optimized throughout the pathway as the first step can represent the historical installed capacities in this case.