A receding horizon dynamic maintenance strategy with distribution network reconfiguration and multi-fault interaction

In the context of increasing extreme weather events and human-induced disasters, enhancing the resilience of distribution networks has become a critical challenge for power systems. Traditional post-disaster maintenance strategies often rely on static decision-making models, which fail to account for the dynamic evolution of network topology and the coupling effects of multi-type faults during the restoration process. To address these limitations, this paper proposes a dynamic maintenance restoration strategy with network reconfiguration for the distribution network. A novel multi-fault set, denoted as ( L O , N O , L C , N C ) , has been developed to address physical damage and malfunctions in protection systems comprehensively. This set is integrated into a multi-stage optimization framework that synchronizes maintenance scheduling with network reconfiguration. The framework utilizes a mixed-integer linear programming formulation aimed at maximizing the recovery efficiency of system load. In the IEEE 33-bus test system, the proposed method outperforms traditional and heuristic methods in various fault scenarios. It improves the RES by 3.06% to 22.42% over static strategies and by 0.07% to 12.42% over heuristic methods. Additionally, it reduces maintenance path lengths by 20.04% to 34.77% and enhances weighted load restoration by 4.85% to 51.86%. • A comprehensive multi-fault set model ( L O , N O , L C , N C ) is proposed, effectively capturing the interactions between physical damage and protection system failures. • A dynamic multi-stage optimization framework is developed, co-optimizing maintenance crew scheduling and network reconfiguration in a rolling horizon. • An efficient Mixed-Integer Linear Programming model with Receding Horizon Optimization is formulated, providing scalable solutions for sequential restoration decisions. • Simulations on the IEEE 33-bus system show the method improves load recovery efficiency by 3.06%–22.42%, reduces maintenance path length by 20.04%–34.77%, and enhances weighted load restoration by 4.85%–51.86%compared to static strategies.