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Energy system models are challenged by the need for high temporal and spatialresolutions in or-der to appropriately depict the increasing share ofintermittent renewable energy sources, storage technologies, and the growinginterconnectivity across energy sectors. This study evaluates methods formaintaining the computational viability of these models by ana-lyzing differenttemporal aggregation techniques that reduce the number of time steps in theirin-put time series. Two commonly-employed approaches are the representation oftime series by a subset of single (typical) time steps, or by groups ofconsecutive time steps (typical periods). We test these techniques for twodifferent energy system models that are implemented using the Frame-work forIntegrated Energy System Assessment (FINE) by benchmarking the optimizationresults based on aggregation to those of the fully resolved models andinvestigating whether the optimal aggregation method can, a priori, bedetermined based on the clustering indicators. The results reveal that typicaltime steps consistenly outperform typical days with respect to cluster-ingindicators, but do not lead to more accurate optimization results when appliedto a model that takes numerous storage technologies into account. Although bothaggregation techniques are ca-pable of coupling the aggregated time steps,typical days offer more options to depict storage oper-ations, whereas typicaltime steps are more effective for models that neglect time-linkingcon-straints. In summary, the adequate choice of aggregation methods stronglydepends on the mathematical structure of the considered energy systemoptimization model, and a priori decisions of a sufficient temporal aggregationare only possible with good knowledge of this mathematical structure.

Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models