Stochastic Planning of Energy System Transformation Pathways Under Uncertain Industry Demands

The transition of industrial sectors to achieve climate neutrality is imperative for effectively meeting the energy and climate policy objectives. Transforming these diverse sectors faces uncertainties, resulting in diverging end-use demands and effects on optimal transformation pathways. We use a scalable stochastic planning framework for optimising robust transformation pathways in a two- and multi-stage setup. By analysing changes in recourse decisions, including shifts in technology investments and modifications in system operation, such as imports of renewable hydrogen, we enhance decision support when facing the considered industry sector uncertainties. Our findings indicate that robust investments lead to higher lifetime system costs. Industrial demand variations affect electrolyser and storage capacities more than other capacities in the system. Moreover, weather uncertainty has a larger impact on system costs than industry uncertainties.