Incorporating a significant amount of renewable energy sources (RESs) into power systems introduces a major challenge to solving transmission expansion planning (TEP) problems owing to the substantial uncertainty associated with these sources. Therefore, adaptive robust optimization (ARO) has attracted much attention in dealing with the uncertainty in the TEP models. In this approach, the planner formulates a min-max-min optimization problem to find the least-cost expansion plan while minimizing the highest operation cost over the predetermined uncertainty set. However, the current studies utilize a simplified transmission-constrained economic dispatch in the third level, ignoring unit commitment-related variables, resulting in a suboptimal expansion plan. This paper aims to fill this research gap by employing a network-constrained unit commitment (NCUC) problem in the third level, leading to an ARO-TEP problem with mixed-integer recourse. More importantly, an ARO approach also handles the uncertainty in the NCUC model. Hence, a min-max-min-max-min problem is established, where the first min-max-min formulates the traditional ARO-TEP, while the second minmax-min determines the ARO-NCUC problem. A nested column-and-constraint generation (NCCG) algorithm can solve the proposed five-level strategy. The effectiveness of the proposed approach is verified through a case study.
