Demonstrates loyalty-based benefit allocation in renewable energy communities, suggesting enhanced member participation.
As Renewable Energy Communities (RECs) mature, they require benefit allocation mechanisms that not only preserve economic efficiency but also support continuous member participation. Current dynamic allocation methods optimize for short-term economic efficiency but often fail to reward broader forms of loyalty, which in this work include long-term participation, flexibility provision, social engagement within the community, and commercial engagement with the REC promoter. This paper proposes a modular multi-stage optimization framework that integrates loyalty scores into the settlement process and compares four distinct mechanisms. Two models use an energy lever, modifying allocation coefficients to prioritize energy access for high-loyalty members: a strict cascade model and a hybrid model that introduces a safety net to balance meritocracy with social fairness. Two additional models use a direct benefit lever, preserving the community's global economic optimum: a revenue-neutral model that mimics cascade outcomes through ex post transfers, and a fixed-policy model that redistributes surplus through administrative rules. Simulation results for a 50-member community show that while direct-benefit mechanisms preserve collective economic optimality, rule-based policies can create stronger disparities, whereas energy-lever models embed incentives directly into energy allocation. A no-harm guarantee relative to the independent baseline is always enforced, ensuring that no member is financially worse off than operating independently. Overall, the proposed framework offers a configurable range of loyalty-allocation strategies, from stronger meritocratic differentiation to more protected equity, while preserving a modular architecture in which different upstream loyalty definitions can be combined with the same downstream allocation and settlement engine.
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Rodrigues et al. (2026) studied this question.
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