  The growing share of intermittent renewable energy sources, storage
technologies, and the increasing degree of so-called sector coupling
necessitates optimization-based energy system models with high temporal and
spatial resolutions, which significantly increases their runtimes and limits
their maximum sizes. In order to maintain the computational viability of these
models for large-scale application cases, temporal aggregation has emerged as a
technique for reducing the number of considered time steps by reducing the
original time horizon down to fewer, more representative ones. This study
presents advanced but generally applicable clustering techniques that allow for
ad-hoc improvements of state-of-the-art approaches without requiring profound
knowledge of the individual energy system model. These improvements comprise
the optimal tradeoff between the number of typical days and inner-daily
temporal resolutions, as well as constituting a representation method that can
reproduce the value distribution of the original time series. We prove the
superiority of these approaches by applying them to two fundamentally different
model types, namely a single-node building energy system and a European
carbon-neutral energy scenario, and benchmark these against state-of-the-art
approaches. This is performed for a variety of temporal resolutions, which
leads to many hundreds of model runs. The results show that the proposed
improvements on current methods strictly dominate the status quo with respect
to Pareto-optimality in terms of runtime and accuracy. Although a speeding up
factor of one magnitude could be achieved using traditional aggregation methods
within a cost deviation range of two percent, the algorithms proposed herein
achieve this accuracy with a runtime speedup by a factor of two orders of
magnitude.

Optimization and Control