Evaluation of influential factors on energy system optimisation

Energy system analysis is gaining more importance in political decisions, science, and society, thus, the basis of interpreting the results is increasingly being questioned. Different approaches have been used in energy system models: Scenario variations, changing base assumptions or sensitivity analyses. A direct comparison of these approaches aims to show that the influence on the results is comparable for all and thus needs to be considered alongside each other. Therefore, this paper uses energy system optimisation within a case study on Germany. It confirms that all approaches greatly influence the results and are thus suitable for discussing uncertainties. Among other things, the sole change of the reference weather year can have a comparable influence on the emissions just like the specification of an entirely different mobility scenario (+44 % and +56 % respectively). However, the target function (total costs) is significantly more influenced by scenarios. Among the evaluated parameters, the investment costs of renewable technologies and the potential of renewable feedstock affect the system the most. In summary, all three methods provide important insights into the optimisation model and should therefore be used together for validation, discussion and design of energy system models. • Discussion of uncertainties in energy system optimisation using three methods. • All methods show equally high influence and thus should be used combined. • Base year and scenario variation lead to ± 56 % emissions and ± 27 % total costs. • The factor with the most significant influence is the investment costs of renewables. • The potential of sustainable feedstock plays a significant role in sector coupling.