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Energy systems are becoming increasingly complex as developments such as sector coupling and decentral electricity generation increase their interconnectedness. At the same time, energy system models that are implemented to depict and predict energy systems are limited in their complexity due to computational constraints. Thus, a trade-off has to be made between high degrees of detail and model runtimes. As a first step towards efficiently managing the complexity of energy system models, we examine the relationship between the purpose of models and their complexity. Using fact sheets on 145 models, we manually cluster these models based on their purpose and underlying research questions. Further, we conduct mathematical clustering using several clustering methods to investigate the reproducibility of our results. For our study, we define the complexity of a model as the level of detail in which it represents reality. We distinguish the level of detail into the four dimensions of temporal, spatial, mathematical and modeling content complexity. The differences between the clusters found in these dimensions are verified statistically using confidence intervals. 112 out of 145 models can be allocated to one out of four major clusters possessing clearly distinguishable complexity profiles: unit commitment, electrical grids, policy assessment, and future energy systems. In each of these profiles, high complexity in one dimension or subdimension is compensated by low complexities in other dimensions. We therefore conclude that when creating a model, modelers allocate complexity in order of priority on those features and properties that are particularly important for fulfilling the model's purpose. Our results provide a necessary basis for the emerging field of complexity management in energy system modeling and are therefore of high interest for the scientific community and the interpreters of model results such as decision makers from policy and industry. |
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