Optimization of hydrogen consumption and production by electrolysers in energy systems is of great interest to the community. In this work, hydrogen-based systems have been modeled with the Modelica open-source language and simulated with Dymola, a dedicated software that also allows users to export complex models using the FMI (Functional Mock-up Interface) standard towards Matlab/Python. This allows to capture with various degrees of fidelity the plant’s dynamics and to expose its behavior at different time scales and for different flows (of hydrogen, load and energy demand). We use this framework to provide advances in the analysis, control and management of the hydrogen-based distribution network. The technical novelties reside in the use of economic MPC (Model Predictive Control) to schedule hydrogen flows towards the storage units and the vehicles intermittently-attached to it, while guaranteeing robustness against uncertainties (in demand). This novel implementation allows maximizing economic output and simultaneously fulfilling the demand (here, vehicle refueling). The results are validated over a hydrogen-based microgrid benchmark provided (together with data profiles) by EDF.