  The determination of environmentally- and economically-optimal energy system
designs and operations is complex. In particular, the integration of
weather-dependent renewable energy technologies into energy system optimization
models presents new challenges to computational tractability that cannot only
be solved by advancements in computational resources. In consequence, energy
system modelers must tackle the complexity of their models daily and introduce
various methods to manipulate the underlying data and model structure, with the
ultimate goal of finding optimal solutions. As which complexity reduction
method is suitable for which research question is often unclear, herein we
review some approaches to handling complexity. Thus, we first analyze the
determinants of complexity and note that many drivers of complexity could be
avoided a priori with a tailored model design. Second, we conduct a review of
systematic complexity reduction methods for energy system optimization models,
which can range from simple linearization performed by modelers to
sophisticated multi-level approaches combining aggregation and decomposition
methods. Based on this overview, we develop a guide for modelers who encounter
computational limitations.
