Solving Power System Problems using Adiabatic Quantum Computing

This paper proposes a novel combinatorial optimization framework that reformulates existing power system problems into a format executable on quantum annealers. The proposed framework accommodates both normal and complex numbers and enables efficient handling of large-scale problems, thus ensuring broad applicability across power system problems. As a proof of concept, we demonstrate its applicability in two classical problems: (i) power system parameter identification, where we estimate the admittance matrix given voltage and current measurements, and (ii) power flow analysis, where we reformulate the nonlinear equations governing active and reactive power balance. The results show that the proposed framework effectively and efficiently solves both linear and nonlinear power system problems, and thus offers significant advantages in scenarios where traditional solvers face challenges, such as ill-conditioned systems and fault conditions.