Reward allocation in Energy Communities by size, composition and prosumers penetration

The energy transition calls for citizen-driven investments in distributed renewables, supported by policies that enable Energy Communities. However, the viability of such initiatives hinges on the fair and transparent allocation of collective revenues in agreement to the community size and composition. Classical game-theoretic allocations, while offering fairness and coalition stability, are often too complex and opaque for real-world implementation. This paper investigates the fair allocation of rewards in Energy Communities depending on community size, composition and prosumers penetration, to infer policy and technical insights. We adopt the game-theoretic reward allocation Variance Least Core that is unique and stable. To break down computational requirements, we adopt a row-generation technique in a Mixed-Integer Linear programming framework that breaks down the typical computational requirements of game-theoretic reward allocation. The methodology is tested across 64 case studies varying in size, prosumer share, and user diversity. The results confirm that the proposed algorithm delivers fair, reproducible, and regulator-ready reward allocations for large-scale energy communities - thus making advanced game-theoretic principles practically usable within open-source tools.