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              <p>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.</p>
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              <p>La planification des opérations de maintenance est essentielle pour garantir la fiabilité des réseaux de transport d’électricité tout en limitant le risque opérationnel et en respectant les contraintes techniques et de ressources. La complexité croissante des systèmes électriques, due au vieillissement des actifs et à l’intégration accrue des énergies renouvelables, met à l’épreuve les approches d’optimisation classiques traditionnellement utilisées par les gestionnaires de réseau de transport. Ce travail se concentre sur l’étape d’optimisation d’un cadre de planification de maintenance fondé sur le risque, dans lequel des indicateurs de risque prédéfinis guident la construction de plannings réalisables.Après avoir discuté les limites des formulations classiques en programmation linéaire en nombres entiers mixtes en termes de passage à l’échelle et de charge computationnelle, nous étudions l’applicabilité de méthodes d’optimisation quantique au problème de planification de la maintenance. En particulier, nous explorons l’utilisation de l’algorithme d’optimisation approximative quantique (QAOA) et de la recherche adaptative de Grover afin de minimiser le risque opérationnel sous des contraintes réalistes. Bien que les limitations actuelles du matériel quantique restreignent les applications pratiques, cette étude apporte des éléments de réflexion sur le potentiel des approches quantiques comme outils complémentaires pour la planification future de la maintenance des grands réseaux électriques.</p>
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