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Increasing integration of renewable generation poses significant challengesto 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 renewablecurtailment. We formulate a two-stage distributionally robustchance-constrained (DRCC) problem that assures limited constraint violationsfor any uncertainty distribution within an ambiguity set. Here, thesecond-stage recourse variables are represented as linear functions ofuncertainty, yielding an equivalent reformulation involving linear constraintsonly. We utilize moment-based (mean-mean absolute deviation) and distance-based(infinity-Wasserstein distance) ambiguity sets that lead to scalablemixed-integer linear program (MILP) formulations. Numerical experiments on theIEEE 14-bus and 118-bus systems have demonstrated the performance improvementsof the proposed DRCC-OTS approaches in terms of guaranteed constraintviolations and reduced renewable curtailment. In particular, the computationalefficiency of the moment-based MILP approach, which is scenario-free with fixedproblem dimensions, has been confirmed, making it suitable for real-time gridoperations.

Distributionally Robust Chance-Constrained Optimal Transmission Switching for Renewable Integration