In the face of escalating global energy demands and the imperative to transition towards sustainable energy sources, the widespread diffusion of Renewable Energy Communities aims at sharing the energy, environmental and social benefits deriving from renewable energies. In this context, understanding and quantifying the uncertainties associated with renewable energy generation as well as the final demands becomes paramount. This paper delves into the main aspects of probabilistic modeling and uncertainty assessment in the context of multi-source Renewable Energy Communities, employing the Monte Carlo simulation method as a robust tool for comprehensive analysis. The results of the demonstrative case study show that the optimal size of the equipment among the several uncertain scenarios can be effectively identified using the mode of the results.