Optimal Formation of Energy Communities Under Technical-Spatial Constraints

The growing incorporation of renewable energy sources, electric vehicles, and energy storage systems within modern power systems has amplified the need for efficient energy management frameworks. This paper presents a novel optimization approach for clustering prosumers into energy community groups to enhance self-sufficiency, operational efficiency, and economic performance. The proposed framework incorporates an initial number of prosumers with energy assets, including photovoltaic systems, storage, and electric vehicles, aiming to form energy communities while addressing the distribution grid's technical and physical constraints. The optimization problem is formulated as a Mixed-Integer Nonlinear Programming model, capturing the nonlinear relationships and discrete decision variables involved in prosumer clustering, complying with grid operational limits, and minimizing power losses within the distribution network. The optimization outcomes demonstrate significant improvements in energy utilization, reducing grid dependency and cost savings for prosumers, enabling greater economic and technical benefits. The results indicate a 43.4 percent reduction in the energy bills of prosumers following their participation in the energy community. This study provides policymakers and grid operators a tool to cluster prosumers by considering technical and regulatory constraints.