Multi-Modal Risk Profiling-Driven Power Grid Disaster Emergency Response Strategies and Dynamic Resource Synergy Optimization Model

To improve the efficiency of power grid emergency response after disasters, this study proposes a multi-modal risk profiling-driven power grid disaster emergency response strategy and dynamic resource synergy optimization model. A risk assessment model is constructed by integrating equipment health status, real-time failure rate, and power grid topology importance to generate equipment risk profiles for identifying key nodes. A two-stage optimization mechanism is then designed, the first stage achieves priority coverage of high-risk equipment and minimization of inspection costs through multi-objective path planning. The second stage adopts a mixed-integer programming model to coordinate personnel scheduling and material allocation under resource constraints. A rolling optimization framework is introduced to dynamically respond to sudden failures and resource changes, ensuring the adaptability of scheduling schemes. To verify the model’s effectiveness, three typical scenarios, ”no sudden failures”, “equipment risk escalation”, and “personnel working hour constraints”, are simulated. Compared with traditional strategies, the model significantly improves the rationality and dynamic adaptability of resource scheduling, providing new ideas and engineering practice support for enhancing the resilience of smart grid disaster emergency response.