Dual-Flow GRU and Residual MLP Fusion PROP Based Coordinated Automatic Generation Control with Renewable Energies

With the growing penetration of renewable energy, automatic generation control (AGC) faces challenges like frequent frequency fluctuations and tie-line power deviations. Traditional proportional (PROP) allocation algorithms, limited by fixed weights, struggle to adapt to dynamic system changes. To address this, this study proposes a coordinated AGC allocation framework fusing a dual-flow Gate Recurrent Unit (GRU) with residual Multilayer Perceptron (MLP) based on PROP, preserving physical prior knowledge while learning adaptive correction terms. Validated on a provincial power grid, the proposed method reduces the cumulative absolute ACE (Sum) by about 0.3–0.9% compared with PROP under 10–100 MW step disturbances. Under random disturbances, it achieves larger reductions of about 3.2% (vs. PROP) and 4.8% (vs. MLP), while maintaining interpretability and deployment feasibility, improving the relevant performance indicators of AGC unit allocation while maintaining interpretability and deployment feasibility, providing an effective solution for AGC under high renewable energy penetration.