## 1 Introduction  
Due to global climate warming, coastal areas within the subtropical monsoon climate zone are increasingly facing safety challenges to the grid operation from tropical cyclones such as typhoons. Unlike sudden natural disasters like earthquakes and fires, typhoons typically have complete spatiotemporal trajectories, causing damage to power grid equipment and its abnormal characteristics to change over time. Therefore, the distribution of system failures under typhoons exhibits complex spatiotemporal variations. During the spatiotemporal evolution of grid failures, it is imperative to study emergency dispatch strategies that meet the needs of system safety operations, ensuring the security and reliability of grid operation. The safety risk factors of the modern digital and intelligent power system include new flexible resources such as distributed energy resources and electric vehicles [1; 2], extreme weather disasters [3; 4], and cyber attacks [5; 6]. Among these, extreme weather disasters can cause power grid equipment outages, leading to load shedding and even system splitting issues, which have garnered widespread attention from researchers [7]. Current research primarily focuses on equipment failure analysis [8; 9], the identification of critical vulnerable equipment [10], and system risk assessment under extreme weather conditions such as typhoons, extreme heat, and cold waves [11; 12; 13]. By analyzing the abnormal mechanisms of extreme weather on equipment, and combining classical equipment aging failure models or data-driven models, the failure probability of the equipment can be determined. The failure probability of a local transmission tower-line system is calculated considering the joint probability distribution of wind speed and direction [14]. A fatigue probability calculation method for wind-turbine towers is developed using the probability density evolution [15]. Based on the solar panel structural performance data, fragility functions for rooftop- and ground-mounted panels are estimated using a Bayesian approach [16]. By generating multiple fault scenarios based on the fault probability characteristics of the equipment and calculating the system operation risk indicators under different fault scenarios, a risk assessment can be achieved. A risk assessment may include a large number of operational scenarios and their safety risk indicator information, making it difficult for dispatchers to quickly grasp key information. Therefore, by classifying the operational scenarios into risk levels through a severity grading strategy, a risk-level-based early warning method can be obtained [17]. In addition to risk assessment and early warning, researchers are also enhancing the resilience of the power grid to extreme natural disasters through preventive dispatch [18], emergency dispatch [19], and restorative dispatch strategies [20]. Considering the uncertainties of wind power variation, a coordinated method for preventive generation rescheduling and corrective load shedding is proposed to maintain power system transient stability [18]. In [19], a consensus algorithm is developed for energy storage and thermal units to achieve the unit rate of communication [19]. As for restoring the electricity service, a coordinated switch operation and crew dispatch strategy is developed to enhance restorative effectiveness. In addition to treating preventive, emergency, and restorative dispatch separately, some researchers study their coordination [21]. Coordinating different measures can reduce total losses throughout the duration of these events. A novel hierarchical defender-attacker model is employed to coordinate the above dispatch stages, and the robust resilience trapezoid and coordinated strategy is obtained through a nested column-and-constraint generation (C&CG) algorithm [21]. Besides conventional generation, researchers also investigate how to coordinate renewable energy in supporting coordinated multi-stage dispatch [22].