  Energy system models are challenged by the need for high temporal and spatial
resolutions in or-der to appropriately depict the increasing share of
intermittent renewable energy sources, storage technologies, and the growing
interconnectivity across energy sectors. This study evaluates methods for
maintaining the computational viability of these models by ana-lyzing different
temporal aggregation techniques that reduce the number of time steps in their
in-put time series. Two commonly-employed approaches are the representation of
time series by a subset of single (typical) time steps, or by groups of
consecutive time steps (typical periods). We test these techniques for two
different energy system models that are implemented using the Frame-work for
Integrated Energy System Assessment (FINE) by benchmarking the optimization
results based on aggregation to those of the fully resolved models and
investigating whether the optimal aggregation method can, a priori, be
determined based on the clustering indicators. The results reveal that typical
time steps consistenly outperform typical days with respect to cluster-ing
indicators, but do not lead to more accurate optimization results when applied
to a model that takes numerous storage technologies into account. Although both
aggregation techniques are ca-pable of coupling the aggregated time steps,
typical days offer more options to depict storage oper-ations, whereas typical
time steps are more effective for models that neglect time-linking
con-straints. In summary, the adequate choice of aggregation methods strongly
depends on the mathematical structure of the considered energy system
optimization model, and a priori decisions of a sufficient temporal aggregation
are only possible with good knowledge of this mathematical structure.
