AC-Informed DC Optimal Transmission Switching via Admittance Sensitivity-Augmented Constraints and Repair Costs

Abstract

AC optimal transmission switching (AC-OTS) is a computationally challenging problem due to the nonconvexity and nonlinearity of AC power-flow (PF) equations coupled with a large number of binary variables. A computationally efficient alternative is the DC-OTS model, which uses the DC PF equations, but it can yield infeasible or suboptimal switching decisions when evaluated under the full AC optimal power flow (AC-OPF). To tackle this issue, we propose an AC-Informed DC Optimal Transmission Switching (AIDC-OTS) scheme that enhances the DC-OTS model by leveraging first- and second-order admittance sensitivities-based constraints and repair/penalty costs that guide the DC OTS towards AC-feasible topologies. The resulting model initially is a Mixed-Integer Quadratically Constrained Quadratic Program (MIQCQP), which we further reformulate into solver-friendly representations, such as a Mixed-Integer Second-Order Cone Program (MISOCP) and a Mixed-Integer Linear Program (MILP). This proposed scheme yields switching topologies that are AC-feasible, while maintaining computational tractability. We validate the proposed scheme using extensive simulations across a large set of PGlib test cases, demonstrating its effectiveness, with performance benchmarks against original DC-OTS and other OTS formulations such as LPAC-OTS and QC-OTS.