  Increasing integration of renewable generation poses significant challenges
to ensure robustness guarantees in real-time energy system decision-making.
This work aims to develop a robust optimal transmission switching (OTS)
framework that can effectively relieve grid congestion and mitigate renewable
curtailment. We formulate a two-stage distributionally robust
chance-constrained (DRCC) problem that assures limited constraint violations
for any uncertainty distribution within an ambiguity set. Here, the
second-stage recourse variables are represented as linear functions of
uncertainty, yielding an equivalent reformulation involving linear constraints
only. We utilize moment-based (mean-mean absolute deviation) and distance-based
(infinity-Wasserstein distance) ambiguity sets that lead to scalable
mixed-integer linear program (MILP) formulations. Numerical experiments on the
IEEE 14-bus and 118-bus systems have demonstrated the performance improvements
of the proposed DRCC-OTS approaches in terms of guaranteed constraint
violations and reduced renewable curtailment. In particular, the computational
efficiency of the moment-based MILP approach, which is scenario-free with fixed
problem dimensions, has been confirmed, making it suitable for real-time grid
operations.
