--- 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 ---
.. warning:: This KPI is designed to look solely at asset-by-asset results. Aggregations need to be handled with a careful attention as the sum and the average of the consumption peak are not dynamically computed. Aggregation by technology, for example, sums up the peaks in demand for each of the assets, but this does not necessarily correspond to the peaks in aggregate demand for all of these assets. In fact, these peaks in demand are not necessarily simultaneous. ### Calculation The equation below is valid for any realization and is therefore implicitly indexed by test case. Let be $x_{a, e, n}$ the consumption peak of a given asset $a$, energy $e$ and node $n$. Technology is directly deduced from the asset. We can then express $x_{a, e, n}$ as: $$ x_{a, e, n} = max_t[c_{a, e, n}(t)] $$ *Global variables and parameters notations definitions can be consulted [here](../notations.md).* ### Indexing - The asset index refers to the name of the asset - The energy index is the energy consumes by the asset - The node index of this KPI refers to the node in which the asset consumes - The technology index refers to the technology type of the asset - The test case index corresponds the test case of the realization variables and parameters are taken fromModelling hint
This helps us to understand which assets are responsible for peak demand. It can also be used to compare demand peaks for assets whose consumption is optimised and those for which this is not the case (electric vehicles, for example).