Experimental Technology and Management

[Objective] With the increasing frequency and intensity of flood disasters， distribution networks are exposed to complex cascading failures caused by the strong coupling between power and transportation systems. However， conventional experimental pedagogy in electrical engineering focuses primarily on deterministic operation and single-system analysis， which limits students’ understanding of disaster-induced risks， recovery processes， and resilience-oriented decision-making. This study aims to design an experimental teaching platform for enhancing the resilience of distribution networks in flood-disaster scenarios by explicitly considering power–transportation coordination. The goal is to support interdisciplinary learning and improve students’ ability to analyze and manage complex coupled systems under extreme conditions. [Methods] An integrated experimental platform is developed by coupling distribution and transportation networks with emergency resource systems within a unified modeling framework. The evolution of flood disasters is first described using a grid-based hydrological model， which captures the spatial and temporal accumulation of surface water. The simulated flooding depth is then mapped to the probability of failure of distribution nodes and degradation of road traffic， enabling the construction of a coupled power–transportation failure model. Emergency resources， including repair crews and mobile energy storage systems (MESS)， are incorporated to represent both structural repair and temporary power supply capabilities. To reflect uncertainty in the impact of disasters， Monte Carlo simulation is employed to generate multiple failure scenarios. By implementing scenario clustering， the computational burden is reduced while preserving representative and high-risk characteristics. Based on these scenarios， a two-stage emergency scheduling framework is established， in which pre-disaster resource allocation and post-disaster dynamic dispatch decisions are jointly optimized. A risk-aware strategy based on conditional value-at-risk (CVaR) is introduced to emphasize low-probability but high-impact scenarios. The platform enables comparative experimental analysis by varying key dimensions， including the consideration of road flooding effects and the number of MESS units and repair crews deployed. [Results] The simulation results under different flood disaster scenarios demonstrate that the proposed platform effectively captures the influence of cross-system coupling on the resilience of the distribution network. When road flooding and traffic degradation are considered， the arrival of emergency resources is delayed， and the power restoration process is significantly slowed， leading to larger resilience loss. Increasing the number of MESS units improves early-stage power supply by providing temporary support to critical loads， which helps mitigate initial service interruptions. In contrast， increasing the number of repair crews mainly accelerates mid- and late-stage structural recovery， enabling the system to reach full restoration earlier. The best overall performance is achieved through the coordinated deployment of repair crews and MESS， combining early power support with faster recovery. Moreover， the CVaR-based scheduling strategy provides enhanced robustness by prioritizing high-impact disaster scenarios， resulting in more stable recovery trajectories across different scenarios. These results clearly illustrate the complementary roles of transportation conditions， emergency resources， and risk-aware decision-making in resilience enhancement. [Conclusions] The proposed experimental teaching platform integrates flood-disaster modeling， coupled power–transportation failure analysis， and risk-aware emergency scheduling into a coherent framework. It transforms abstract concepts of system resilience into observable experimental phenomena， enabling students to intuitively understand how disaster evolution， transportation accessibility， and resource coordination jointly affect the recovery of distribution networks. The platform effectively supports comparative experiments and decision analysis in uncertainty， fostering interdisciplinary thinking and resilience-oriented engineering skills. This study provides a practical reference for advancing experimental teaching reform in electrical engineering and cultivating students’ ability to analyze and manage complex coupled energy systems in the event of extreme events.