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