Large-scale integration of photovoltaics (PV) introduces complex source-load temporal volatility and grid-connection/off-grid transitions. Traditional static reliability assessments fail to capture these dynamics, resulting in “considerable deviations” in system indices. This paper proposes a reliability evaluation framework that couples temporal source-load trajectories with a multi-stage fault recovery process. Unlike traditional methods that rely on a single static snapshot, the proposed model evaluates the system state across a continuous 5-h restoration window. The novelty lies in the unique integration of a Dynamic Time Warping (DTW)–Kmedoids method to preserve temporal phase-shifts and a multi-stage Mixed-Integer Linear Programming (MILP) model to simulate PV grid-connection transitions throughout this window. By capturing the intra-outage evolution of sources and loads, the framework fundamentally corrects the “considerable deviations” of static assessments. Case studies demonstrate high precision with an error of less than 0.71% and a 20-fold speedup. Crucially, the framework corrects the 22.31% risk underestimation bias inherent in static models by tracking real-time source-load evolution. This confirms that temporal coordination performance is the primary determinant of the reliability ceiling in active distribution networks. The findings reveal that the precise alignment of intermittent generation and fluctuating demand defines the actual operational safety margin, providing a superior quantitative foundation for grid resilience enhancement.
Dong et al. (Sat,) studied this question.