Distribution networks denote important public infrastructure necessary for people’s livelihoods. However, extreme natural disasters, such as earthquakes, typhoons, and mudslides, severely threaten the safe and stable operation of distribution networks and power supplies needed for daily life. Therefore, considering the requirements for distribution network disaster prevention and mitigation, there is an urgent need for in-depth research on risk assessment methods of distribution networks under extreme natural disaster conditions. This paper accesses multi-source data, presents the data quality improvement methods of distribution networks, and conducts data-driven active fault diagnosis and disaster damage analysis and evaluation using data-driven theory. Furthermore, the paper realizes real-time, accurate access to distribution network disaster information. The proposed approach performs an accurate and rapid assessment of cross-sectional risk through case study. The minimal average annual outage time can be reduced to 3 h/a in the ring network through case study. The approach proposed in this paper can provide technical support to the further improvement of the ability of distribution networks to cope with extreme natural disasters.

Rural integrated energy systems (RIESs) suffer from volatile renewable generation, surging agricultural peak loads, and unevenly distributed forecast errors, which severely undermine scheduling robustness and economic efficiency. Conventional fixed-scenario robust scheduling cannot dynamically adapt to error variations, leading to either excessive conservatism or insufficient operational safety. This paper proposes a forecast-error-driven multi-time-scale hybrid robust-scenario scheduling framework with a three-layer closed-loop architecture: day-ahead multi-scenario robust baseline scheduling, intraday error-driven rolling correction, and real-time dynamic-simulation calibration. Case studies on a practical Yunnan rural system verify that within ±25% forecast error, the proposed method reduces operating costs by 12.8%, increases the renewable energy accommodation rate by 22.6%, lowers the dynamic constraint-violation rate below 0.05%, and maintains the average solution time within 9 s, achieving a superior balance of resilience and economy for complex rural operations.