A game theory-based edge device for renewable energy communities optimal management

In most running implementations, Renewable Energy Communities aggregate consumers who share the use of renewable energy with the goal of reducing environmental impact. Each member, connected to the power grid via a single Point of Delivery, should optimize their consumption in order to achieve the community goal. However a comprehensive knowledge of the production capacities and consumption profiles of all members is required. To address this challenge, this paper proposes a distributed load scheduling method based on Game-Theory that achieves an optimal balance between individual and collective goals while preserving privacy and enabling energy services provision. The method distributes the computational workload among all users, making it feasible to implement on low-cost hardware devices. In addition, the method allows users to choose their own preferences regarding overall community goals. The proposed approach was evaluated in two case studies, showing that in a few iterations of the game, users reach an optimal equilibrium that not only maximizes individual profits but also satisfies community goals, without the need to share sensitive data. • Game Theory-based method for optimal load management in Renewable Energy Communities. • Iterative multi-objective optimization balancing energy sharing and user comfort. • Fully distributed implementation on low-cost edge devices ensuring privacy and scalability. • Experimental validation including computation and communication performance analysis.