Energy communities are exploring multi-vector solutions to integrate variable renewable generation while preserving security of supply and affordability. This thesis develops an optimization-based energy management model for energy communities that enables the use of a reversible solid oxide cell (rSOC), where the rSOC can operate in electrolysis mode (SOEC) to convert electricity into hydrogen and in fuel-cell mode (SOFC) to reconvert hydrogen into electricity. A mixed-integer linear programming (MILP) formulation is implemented in Python using Pyomo and solved with Gurobi to determine the optimal configuration over a time-resolved horizon. The multi-vector energy model optimizes electricity, heat and hydrogen flows, accounting for hydrogen storage dynamics, rSOC operating limits and mode-dependent behavior.