Optimal aggregation of users and sizing of renewable plants in energy communities

The increasing diffusion of energy communities requires advanced optimization tools capable of addressing economic, environmental, and operational trade-offs. This thesis focuses on the multi-objective optimization of renewable energy system sizing within an energy community, with particular attention to the effects of user aggregation and energy mismatch. The main objective is to identify optimal system configurations that balance total cost, CO₂ emissions, and demand–supply mismatch. The optimization problem is formulated as a mixed-integer linear program and solved using the epsilon-constraint method, which enables systematic exploration of Pareto-optimal solutions without relying on subjective weighting factors. The methodology is applied to a case study at substation level, ensuring physical consistency between demand data, grid interaction, and system constraints. The results highlight the strong influence of aggregation assumptions and mismatch constraints on system feasibility, optimal capacity sizing, and trade-off structures. The proposed framework provides transparent decision support for the planning and design of local energy communities.