Technologies for the exploitation of renewable energies have been dramatically increasing in number, complexity and type of source adopted. Among the others, the use of saline gradient power is one of the latest emerging possibilities, related to the use of the osmotic/chemical potential energy of concentrated saline solutions. Nowadays, the fate of this renewable energy source is intrinsically linked to the development of the pressure retarded osmosis and reverse electrodialysis technologies. In the latter, the different concentrations of two saline solutions is used as a driving force for the direct production of electricity within a stack very similar to the conventional electrodialysis ones. In the present work, carried out in the EU-FP7 funded REAPower project, a multi-scale mathematical model for the Salinity Gradient Power Reverse Electrodialysis (SGP-RE) process with seawater and concentrated brines has been developed. The model is based on mass balance and constitutive equations collected from relevant scientific literature for the simulation of the process under extreme conditions of solutions concentration. A multi-scale structure allows the simulation of the single cell pair and the entire SGP-RE stack. The first can be seen as the elementary repeating unit constituted by cationic and anionic membrane and the relevant two channels where dilute and concentrate streams flow. The reverse electro-dialysis stack is constituted by a number of cell pairs, the electrode compartments and the feed streams distribution system. The model has been implemented using gPROMS®, a powerful dynamic modelling process simulator. Experimental information, collected from the FUJIFILM laboratories in Tilburg (the Netherlands), has been used to perform the tuning of model formulation and eventually to validate model predictions under different operating conditions. Finally, the model has been used to simulate different possible scenarios and perform a preliminary analysis of the influence of some process operating conditions on the final stack performance.