  Simulating energy systems is vital for energy planning to understand the
effects of fluctuating renewable energy sources and integration of multiple
energy sectors. Capacity expansion is a powerful tool for energy analysts and
consists of simulating energy systems with the option of investing in new
energy sources. In this paper, we apply clustering based aggregation techniques
from the literature to very different real-life sector coupled energy systems.
We systematically compare the aggregation techniques with respect to solution
quality and simulation time. Furthermore, we propose two new clustering
approaches with promising results. We show that the aggregation techniques
result in consistent solution time savings between 75% and 90%. Also, the
quality of the aggregated solutions is generally very good. To the best of our
knowledge, we are the first to analyze and conclude that a weighted
representation of clusters is beneficial. Furthermore, to the best of our
knowledge, we are the first to recommend a clustering technique with good
performance across very different energy systems: the k-means with Euclidean
distance measure, clustering days and with weighted selection, where the
median, maximum and minimum elements from clusters are selected. A deeper
analysis of the results reveal that the aggregation techniques excel when the
investment decisions correlate well with the overall behavior of the energy
system. We propose future research directions to remedy when this is not the
case.
