To address the multiple uncertainties in transmission network planning， this paper proposes a source-grid-storage coordinated expansion planning model that integrates stochastic programming and robust optimization. First， to handle the short-term volatility and correlations of renewable energy output and load demand， a stochastic programming approach is adopted. This involves generating a large number of scenarios based on kernel density estimation and Copula theory， followed by scenario reduction using the k-means++ algorithm to obtain typical and extreme scenarios. Second， to address the low-frequency uncertainties associated with source， grid， and storage failures， a two-stage robust optimization model for source-grid-storage coordinated planning is constructed. A Big-M parameter tuning method is proposed to accelerate the solution process. Finally， the model is solved using the nested column-and-constraint generation （NC&CG） algorithm. Simulation results demonstrate that the proposed model ensures economic efficiency while effectively accommodating operational reliability， validating the rationality and effectiveness of the method.