A Multi-Time-Scale Fast Iteration and Online Optimization Framework for Emergency Control Strategies in Modern Power Systems

With the increasing penetration of renewable energy and frequent variations in grid operating conditions, traditional offline emergency control strategy tables face challenges in adaptability, timeliness, and economic efficiency. To address these issues, this paper proposes a multi-time-scale fast iteration framework for emergency control strategies in modern power systems. The framework organizes strategy updates across yearly, monthly, weekly, and dispatch-cycle scales, enabling progressive refinement of control parameters according to evolving operating characteristics and risk levels. In addition, an automated strategy formulation workflow is established, integrating operating characteristic generation, control resource organization, fault set construction, coordinated strategy generation, and simulation-based verification. This reduces reliance on manual experience and improves the standardization and efficiency of strategy deployment. A case study based on an improved IEEE 39-bus system is conducted to emulate the strategy evolution process under different operating characteristics. The results show that the proposed framework enables dynamic adjustment of control intensity, achieving a transition from long-term conservative coverage to mid-term economic optimization and short-term risk-oriented reinforcement.