Enhancing Energy System Modelling with Advanced Mathematical Decomposition Techniques: Feasibility of Coupling Sms++ and Pypsa

Large-scale high-resolution energy system modelling is essential to support long-term decarbonization strategies. Open-source frameworks such as PyPSA-Eur and PyPSAEarth enable high-resolution techno-economic analyses but face computational bottlenecks when applied to large-scale or stochastic systems. Advanced mathematical decomposition techniques can alleviate these challenges, yet they are rigid to specific problems and difficult to scale to different applications. SMS++ is a high-performance, C++-based modelling framework designed to support flexible, nested decomposition strategies through a structured block-based approach. However, its use has so far remained isolated from mainstream energy system modelling. This paper presents a novel methodology to couple energy modelling tools with mathematical decomposition frameworks, focusing on integrating PyPSA with SMS++, to facilitate the wide adoption of advanced decompositions into energy applications. A formal interface is developed by mapping PyPSA components to SMS++ blocks. A fully reproducible prototype workflow is then implemented and validated on six case studies, demonstrating numerical equivalence within tight tolerances. The successful integration confirms the feasibility of applying advanced decomposition techniques within a userfriendly modelling environment. This approach opens new pathways for scaling energy system models to sector-coupled and uncertainty-aware applications with enhanced computational efficiency.