A Framework for Solving Continuous Energy and Power System Problems using Adiabatic Quantum Computing
For researchers in energy and power systems, this work provides a proof-of-concept for using quantum annealing to solve continuous problems, but it is incremental as it only tests on small cases without comparison to classical solvers.
The paper proposes a combinatorial optimization framework to reformulate continuous energy and power system problems for execution on quantum/digital annealers, demonstrating its use on three small-scale test cases (heat transfer, parameter identification, power flow) with effective results.
The increasing scale and nonlinearity of modern energy and power system problems pose significant challenges to classical numerical solvers. In parallel, advances in quantum and quantum-inspired hardware are expected to improve scalability and offer performance advantages for large-scale optimization problems. Therefore, we propose a novel combinatorial optimization framework that reformulates continuous energy and power system problems into a format executable on quantum/digital annealers. The proposed framework accommodates both real and complex numbers and can represent both linear and nonlinear equations. As a proof of concept, we demonstrate its use in three applications: (i) 2D steady conductive heat transfer for a plate with constant temperature at each edge, where coefficient and boundary condition matrices are developed to solve linear system of equations, (ii) power system parameter identification, where the admittance matrix is estimated given voltage and current measurements, and (iii) power flow analysis, which solves the governing equations for active and reactive power balance. As a proof of concept, the applications are run on small test cases. The results show that the framework effectively and efficiently addresses the three applications and therefore suggest its potential to solve a wide range of energy and power system problems.