This paper presents a new approach to optimize the clustering of industrial users and to determine the appropriate size of photovoltaic (PV) systems in renewable energy communities (RECs). By combining data including each company’s energy consumption profiles based on its ATECO classification, existing and installable PV capacity, electricity purchase and sale costs, REC incentives, and PV installation costs, the proposed algorithm identifies the optimal clustering of industrial users to form an economically efficient REC. Additionally, the optimal PV capacity for each member is evaluated, taking into account potential constraints of the available area. As a whole, the proposed algorithm can determine which cluster of companies maximizes the REC net present value (NPV) without compromising the payback time (PBT), providing a strategic framework and aid for improving the economic performance of industrial RECs, correctly sizing the community and ensuring that PV installation and investment yields the greatest possible financial and social benefits. From the analysis of the considered case studies, it appears that the proposed clustering and sizing method allows, for the REC as a whole, for an increase in the NPV from a minimum of about 25% with no change in PBT, up to about 75% in the case of a change in PBT of up to 5 years.
