Enabling energy systems research on HPC

Abstract

Energy systems research strongly relies on large modeling frameworks. Many of them use linear optimization approaches to calculate blueprints for ideal future energy systems, which become increasingly complex and so do the models. The state-of-the-art to compute them is the application of shared-memory computers combined with approaches to reduce their size. We overcome this and implement a full automatized workflow on HPC using a newly developed solver for distributed memory architectures. Moreover, we address the challenge of uncertainty in scenario analysis by performing sophisticated parameter variations for large-scale power system models, which cannot be solved in the conventional way. Preliminary results show that we are able to identify clusters of future energy system designs, which perform well from different perspectives of energy system research and also consider disruptive events. However, we also see that our approach provides most insights when being applied to rather complex than simple models.