保障稳定性的自适应一、二次频率控制策略优化

Flexible resources represented by battery energy storage have been integrated into load frequency system to mitigate frequency security risks arising from the increasing penetration of renewable energy resources. These resources offer high flexibility, enabling customization of control logic to enhance frequency control performance. While extensive research has focused on optimizing control logic and parameters for either primary frequency regulation (PFR) or secondary frequency regulation (SFR) with these flexible resources, the joint optimization of PFR and SFR has not been thoroughly explored. Considering the coupled effects of PFR and SFR on system frequency control stability, performance and efficiency, this paper first proposes a novel framework for PFR and SFR with enhanced control flexibility and adaptability. Within this control framework, a model-based H_ stability criterion is then derived to ensure system frequency stability and robustness. Finally, this stability criterion is incorporated into a deep reinforcement learning (DRL) method to jointly optimize the control parameters for PFR and SFR. Time-domain simulations demonstrate that the optimized frequency control framework achieves superior performance under both normal and contingency operating conditions.