Quantum-Assisted Joint Generation-Transmission Expansion Planning for Modern Power Systems

Joint generation-transmission expansion planning (JGTEP) expands the set of feasible solutions compared with traditional approaches, but also introduces the challenge of variables proliferation across generations and transmission networks. Quantum computing offers efficiency advantages over classical methods in handling binary variables, presenting a potential to speed up the planning processes. This paper proposes a method to accelerate JGTEP based on a hybrid quantum-classical framework. Firstly, the JGTEP problem is divided into a master problem and a subdual problem by Benders decomposition. Secondly, a classical computer is then employed to generate multiple feasible solutions for the master problem. An initial solution for warm-starting is created together with the reduction of variable dimension under the characteristics of various feasible solutions. On this basis, the master problem is subsequently transformed into a quadratic unconstrained binary optimization problem, efficiently solved by a warm-starting quantum annealing algorithm. Finally, the master problem and subproblem are solved alternately until the complete results are presented.