Distributionally Robust Chance-Constrained Optimal Transmission Switching Problems

Abstract

Increasing integration of renewable generation and flexible resources poses significant challenges to ensure robustness guarantees in real-time power system decision-making. This work aims to develop a robust optimal transmission switching (OTS) framework that can relieve grid congestion via judicious line switching. We formulate a two-stage distributionally robust chance-constrained (DRCC) problem that can ensure limited constraint violations for any uncertainty distribution within an ambiguity set. The second-stage recourse variables are first represented as linear functions of uncertainty, yielding an equivalent reformulation of linear constraints only. Furthermore, both moment-based (mean-mean absolute deviation) and distancebased (∞-Wasserstein distance) ambiguity sets lead to equivalent 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 over two CC-OTS counterparts, in terms of guaranteed constraint violations. 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.