To respond to the global need for sustainable energy solutions and the imperative to combat climate change, Renewable Energy Communities (REC) have emerged as a promising solution to achieve energy transition goals. Of course, some optimization tools need to be developed to face the challenges related to their operational management and maximize their potential. In this context, this paper proposes a bilevel optimization approach for the optimal management of a REC, focusing on maximizing shared energy and economic benefits. The high-level models the problem of the Energy Community Manager (ECM), who aims at maximizing shared energy rewarded with proportional incentives; instead, the low-level problem focuses on each Energy Community Participant (ECP) aiming to minimize individual costs. To solve this problem Karush-Kuhn-Tucker (KKT) conditions are exploited to convert low-level problems into constraints for the high-level problem. The proposed approach is first applied to a case study involving six ECPs, then a scalability analysis is performed considering 20 ECPs to simulate a more realistic scenario. According to the results, each ECP would obtain an income per year for sharing energy which could be significant especially for those suffering from energy poverty.