To address the disaster uncertainty of extreme events, this chapter proposes a resilience planning method for integrated energy systems (IES) that balances economic efficiency and risk. First, a hierarchical fault scenario generation strategy is developed by integrating stratified sampling, importance sampling, stochastic fault simulation, and average loss filtering, forming a set of representative fault scenarios covering multiple crisis levels and their probabilistic characteristics. Second, a two-stage resilience planning framework is established, incorporating pre-disaster protection and post-disaster multi-energy coordinated emergency scheduling. Conditional value-at-risk (CVaR) is introduced to formulate a tail risk analytical expression for low-probability, high-impact events, ensuring controllability between economic efficiency and risk balance. To enhance computational efficiency for large-scale models, a tailored Benders decomposition (TBD) algorithm accelerated by primal-dual cuts is proposed. The effectiveness of the proposed model and algorithm is validated through case studies. This work provides a systematic solution for disaster resilience planning in integrated energy systems, ensuring a balanced trade-off between cost and risk control.