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

TL;DR

This paper reviews methods for managing complexity in energy system optimization models, providing a guide for modelers to choose suitable approaches for computational challenges in renewable energy integration.

Contribution

It offers a systematic review of complexity reduction techniques and develops a practical guide tailored for energy system modelers facing computational limitations.

Findings

Many complexity drivers can be avoided with tailored model design.

Systematic reduction methods include linearization, aggregation, and decomposition.

The guide helps select appropriate methods based on research questions.

Abstract

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.