The integration of large-scale regional water-wind-solar hybrid energy systems poses challenges to power grid stability due to persistent fluctuations that conventional automatic generation control (AGC) systems struggle to mitigate effectively. To address this issue and optimize frequency modulation resource utilization, this study presents a bidirectional communication-based AGC optimization strategy. The proposed approach enhances reinforcement learning algorithms through a dual-estimation framework, enabling dynamic power distribution among generation units. Simultaneously, the methodology incorporates coordinated grid power flow adjustments to achieve integrated uncertainty modeling and coordinated optimization for regional hydro-wind power systems. Experimental validation demonstrates that the enhanced control strategy achieves an improvement of 2.2%–5.8% in Control Performance Standard (CPS) metrics compared with conventional methods, confirming superior system regulation capability.
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