Optimal dynamic allocation of wind and solar power forecasts and generation to substation transformers in transmission grids

The accurate allocation of wind and solar power forecasts to transformers in the transmission grid is crucial for the stability of modern power systems. As renewable energy integration increases, it becomes vital for transmission system operators to understand power flows across different grid levels. Traditional methods require detailed grid information that is often inaccessible and dispersed among various grid operators. This study addresses the challenge of determining allocation keys for renewable energy sources to transformers in the transmission grid. Here, we propose a novel statistical approach that estimates these allocations using vertical power flow data and spatially distributed feed-ins. Our two-step method first allocates power plants to their nearest lower voltage level substations, then optimizes these allocations to match measured power flows in the transmission grid. This optimization is framed as a quadratic problem with linear constraints. Compared to previous nearest-neighbor approaches, our method significantly reduces allocation errors. These findings enhance the accuracy of power flow data for transmission system operators, contributing to improved grid stability and management. Our approach provides a scaleable solution adaptable to dynamical changes in grid topology.