Quantum approach for electrical system maintenance scheduling problem

Maintenance scheduling is essential to ensure the reliability of power transmission systems while limiting operational risk and respecting technical and resource constraints. Increasing system complexity due to asset aging and renewable integration challenges classical optimization approaches traditionally used by transmission system operators. This work focuses on the optimization stage of a risk-based maintenance planning framework, where predefined risk indicators guide the construction of feasible schedules. After discussing the limitations of classical mixed-integer programming formulations in terms of scalability and computational effort, we investigate the applicability of quantum optimization methods to the maintenance scheduling problem. In particular, we explore the use of the Quantum Approximate Optimization Algorithm (QAOA) and Grover Adaptive Search to minimize operational risk under realistic constraints. While current quantum hardware limits practical deployment, this study provides insights into the potential of quantum approaches as complementary tools for future large-scale power system maintenance planning.