Renewable energy communities struggle with a paradoxical problem: abundant local solar generation often exceeds local demand, forcing surplus power back into the grid rather than serving nearby households. While existing allocation methods fairly distribute shared energy after the fact through billing mechanisms, they fail to influence consumption behavior in real time. A new framework transforms this approach by converting sharing coefficients into predictive price signals that guide household demand to times and locations where solar surplus can be captured locally.
The methodology operates around a central principle: energy costs less when and where it is available. Each day, the energy manager forecasts community PV output and household demand, then computes allocation scenarios using convex optimization. The system decomposes purchased energy into three categories: power from same-feeder solar, inter-feeder transfers, and grid imports. Households receive individualized prices reflecting these distinctions, incentivizing them to shift flexible loads toward high-local-generation periods.
Two design variants were tested. The feeder-aware approach shares surplus first within each low-voltage feeder before opening a community-wide pool, capturing network topology benefits. The feeder-agnostic design pools all surplus globally. Across 15 real households and validated using AC power flow analysis, the feeder-aware approach reduced reverse energy flow by 45% on peak reverse days and 69% annually. The feeder-agnostic variant achieved 44.6% and 66.3%, respectively.
These results carry meaningful implications for grid operations. Reverse power flow degrades voltage quality, complicates protection systems, and wastes the economic value of renewable generation. By embedding locational and temporal signals into pricing, the framework aligns household incentives with network physics. The approach leverages data already required for community management, requiring no additional infrastructure beyond standard forecasting and optimization tools. As renewable energy communities scale globally, dynamic price signals tied to local generation patterns offer a proven pathway to maximize self-consumption while maintaining network stability.
