Joint generation-transmission expansion planning in renewables-dominated power systems based on hybrid quantum-classical computing

Abstract

The joint generation-transmission expansion planning (JGTEP) problem offers a broader range of feasible strategies than unilateral planning and enables more favorable cost-benefit outcomes in renewables-dominated power systems. However, it also imposes heavier computational burdens due to the proliferation of binary variables from generation units and transmission networks, as well as slower convergence caused by nonlinear, spatiotemporal coupling constraints. Leveraging the quantum tunneling effect, quantum annealing provides significant efficiency advantages in handling large-scale binary problems, while classical computing remains superior for continuous optimization and binary variable initialization. This complementarity motivates a hybrid quantum–classical framework to accelerate JGTEP. Specifically, the problem is first decomposed into a master problem and a subdual problem using Benders decomposition. A classical computer then generates multiple feasible solutions for the master problem, from which an initial warm-start solution is constructed, and the dimensionality of variables is reduced by exploiting the shared characteristics of these solutions. The master problem is subsequently reformulated as a quadratic unconstrained binary optimization model, which is efficiently solved on a quantum annealer using a warm-start quantum annealing algorithm. Finally, the master problem and subproblem are solved iteratively until convergence. Applied to the IEEE RTS 24-bus system, the proposed quantum-assisted method achieves more than a 70% reduction in computation time and a 50% decrease in iterations compared with classical methods, while also demonstrating strong potential for tackling larger-scale planning problems.