The benefits and challenges of soft-linking investment and operational energy system models

Large-scale energy system modelling is often applied to inform decision-making in the green transition. Energy system models tend to increase in complexity at the expense of increased computation time. Soft-linking of energy systems models is frequently applied to increase the modelling scope and thereby answer complex research questions while maintaining tractability. However, evaluations of the soft-linking strategy itself are rarely investigated or documented. We, therefore, explore the benefits and challenges of soft-linking through assessment of a bi-directional soft-linking framework for an integrated, pan-European electricity and hydrogen system. The frameworks Balmorel and Antares are chosen as components in the bi-directional soft-linking framework, with harmonised input data and spatial resolutions. Conversion, storage and transmission investments are computed in Balmorel based on one representative weather year and aggregated timeslices. These investments are subsequently evaluated over 31 historical weather years at full hourly resolution in Antares. Different strategies for increasing the potentially inadequate investments by Balmorel are analysed. This includes previously applied methods based on so-called capacity credits and profit signals. We also introduce a novel ’fictive demand’ approach, and the results and computation times are discussed using a low loss of load expectancy (LOLE) in electricity and hydrogen as a key performance indicator. The fictive demand method proved stable across multiple iterations and successfully reduced LOLE, while the capacity credit showed some promise. Finally, recommendations for soft-linking studies are formulated, and we argue that soft-linking enables large-scale problems to be investigated, but the necessary model harmonisations, choice of specific soft-linking strategy, and tuning of it present significant challenges.