A modeler's guide to handle complexity in energy systems optimization

Abstract

• Holistic review of complexity management methods in energy systems optimization. • Data availability drives model sizes and therefore the model complexity. • Systematic temporal down-sampling or aggregation are widespread and easily accessible while spatial aggregation has only a small community. • Decomposition methods still need to be customized to the model wherefore exploitation of parallel compute infrastructure requires high efforts. • Level of details in system modeling drives non-convexity and is often avoided. Determining environmentally- and economically-optimal energy systems 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 by applying 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 different approaches for handling complexity. 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 energy system modelers who encounter computational limitations.