Distribution network partitioning at high distributed-energy-resource penetration is challenged by operating-point-insensitive distance criteria, initialization dependence, and oversized or oscillatory zones. This paper presents a framework that combines a comprehensive electrical distance metric with an improved density peak clustering–label propagation algorithm (DPC-LPA). The metric integrates network impedance and voltage sensitivities to renewable-power variations. DPC determines the initial number and centers of zones, and the modified LPA uses partition-scale, load-balance, and historical-influence factors to regulate zone growth and label switching. Tests on medium- and low-voltage alternating-current/direct-current (AC/DC) networks show improved load balance relative to conventional LPA with only a modest change in modularity. These results support the method for the evaluated networks without implying performance beyond the tested conditions.

With the increasing integration of distributed energy sources, fault restoration in power distribution systems faces challenges in terms of real-time performance and adaptability. To effectively manage the uncertainty and volatility of distributed generation, this paper proposes a rapid self-healing strategy based on a dynamic operational grid. By enabling real-time topological reconfiguration and utilizing adaptive resource allocation, the proposed method accommodates the inherent fluctuations of distributed energy sources. First, a dynamic grid weighted graph theory model is constructed, and an emergency control strategy combining particle preprocessing and stepwise optimization is designed to achieve rapid fault response. Then, a “primary-secondary” two-tier restoration mechanism is established: the primary layer integrates the Floyd algorithm with optimized adaptive dynamic programming to achieve millisecond-level restoration of critical loads; the secondary layer employs an improved particle swarm algorithm incorporating Lévy flight perturbations and adaptive weighting to maximize the restoration of general loads. Simulations on a 56-node system demonstrate that this method achieves 100% restoration of critical loads under various fault scenarios. Even under extreme conditions, it can restore 90.88% of secondary loads and 44.63% of tertiary loads, forming a self-healing system characterized by “second-level detection and minute-level restoration,” thereby significantly enhancing system resilience.