ABSTRACT The growing integration of renewable energy resources (RERs) introduces significant variability and uncertainty into modern power systems, reducing the effectiveness of traditional transmission expansion planning (TEP) approaches. This study proposes a comprehensive stochastic framework that combines operational reliability under normal conditions with resilience against N − 1 contingencies. Correlated wind–load uncertainties are modeled using a Dynamic Weibull Probability method integrated with a Refined Sampling Strategy (DWP–RSS) to capture short‐term wind fluctuations with high fidelity. To address the computational complexity of the resulting large‐scale mixed‐integer optimization problem, an adapted Benders decomposition algorithm is employed. Numerical analyses on an enhanced IEEE 24‐bus reliability test system demonstrate that neglecting these correlated uncertainties leads to an underestimation of required transmission reinforcements. The proposed model effectively incorporates both economic and reliability impacts of uncertainty and security constraints. The results highlight a clear trade‐off between investment cost and system reliability, providing practical insights for developing resilient and cost‐efficient transmission networks in renewable‐intensive environments.
Saran et al. (Mon,) studied this question.