Hybrid classical and quantum-inspired optimization for optimal transmission switching

Abstract

Optimal Transmission Switching (OTS) is a combinatorial optimization problem that aims to improve power system performance by selectively changing transmission line statuses. The inclusion of binary switching decisions increases computational complexity, while emerging quantum computing techniques offer new possibilities for handling such discrete problems despite current hardware limitations. This thesis investigates a hybrid classical-quantum optimization framework for OTS using the IEEE 14 bus test system. The approach separates continuous variables, solved through classical DC Optimal Power Flow, from binary switching decisions, represented through a Quadratic Unconstrained Binary Optimization (QUBO) model. A restricted candidate set is evaluated exhaustively to establish a classical benchmark. Results show that only a small subset of switching configurations is feasible and that all feasible topologies yield the same objective value as the baseline DC OPF solution under the studied operating condition. The QUBO model accurately captures the binary structure of the switching problem and produces energy rankings consistent with classical feasibility and cost evaluation. The hybrid workflow reproduces the classical optimal configuration, demonstrating that the decomposition maintains feasibility and economic consistency while enabling structured exploration of switching decisions. Although no computational advantage is observed, the study provides a clear methodological framework for integrating quantum compatible optimization models with classical power system analysis.