针对极端冰灾时空不确定性与输电网规划运营成本的矛盾，本文提出了一种面向韧性提升的输电网协同规划与运营策略研究。首先，在灾害建模层面，构建了融合广义极值分布（GEVD）与时空随机场的多阶段时空耦合覆冰模型，以捕捉灾害演化的非平稳特性；同时，采用连续时间马尔科夫链（CTMC）刻画多子导线的故障机理，描述覆冰增量下输电容量的变化过程。其次，在决策优化层面，构建了考虑条件风险价值（CVaR）的两阶段优化框架：第一阶段以最小化投资成本为目标，协同优化线路差异化抗冰等级与主动融冰装置布局；第二阶段以最小化运维成本与尾部风险为目标，对机组出力、分级负荷削减及融冰、除冰维修资源进行全过程协同调度。最后，基于改进IEEE RTS-96系统的算例分析表明：所提针对性投资策略相较于全线最高抗冰等级方案可节省约33%的投资成本；在运营阶段，通过多资源的时序接力配合，使单次极端灾害下的总运营成本降低74.9%。研究结果验证了该方法能有效实现输电网经济性与韧性的提升。

Addressing the conflict between the spatiotemporal uncertainty of extreme ice disasters and the economic constraints of transmission network hardening, this paper proposes a collaborative planning and operation method for enhancing grid resilience. First, regarding disaster modeling, a multi-stage dynamic icing model fusing Generalized Extreme Value Distribution (GEVD) and spatiotemporal random fields is constructed to capture the non-stationary evolution of disasters. Simultaneously, a Continuous-Time Markov Chain (CTMC) is employed to characterize the cascading failure mechanism of multi-split conductors, describing the dynamic decay of transmission capacity under ice accumulation. Second, regarding decision optimization, a two-stage stochastic optimization framework incorporating Conditional Value-at-Risk (CVaR) is proposed. The first stage (planning) co-optimizes differentiated line hardening levels and active de-icing device layout to minimize investment costs. The second stage (operation) minimizes operational costs and tail risks by coordinating generation redispatch, hierarchical load shedding, and active de-icing/repair resources. Case studies on a modified IEEE RTS-96 system demonstrate that the proposed targeted investment strategy saves approximately 33% of capital costs compared to a uniform maximum-hardening scheme. Furthermore, the multi-resource collaborative operation strategy reduces total operational costs by 74.9% under extreme scenarios. The research findings validate that this approach can effectively enhance the economic efficiency and resilience of transmission grids.