As distributed energy resources are increasingly integrated into low-voltage grids, there is growing interest in preventive control strategies to anticipate and mitigate voltage and line loading violations. Recent approaches combine probabilistic forecasts with corrective actions to trigger preventive interventions before violations occur. However, residual violations persist even in advanced simulation studies and preventive control frameworks. When planning system improvements, it is often unclear whether failures stem primarily from forecast uncertainty or from insufficient flexibility, as both factors interact and their individual contributions cannot be directly observed.

This paper introduces a diagnostic method that systematically attributes residual violations to their underlying operational limitation through controlled scenario tests. The approach acts as a post-operational analysis layer on top of an existing preventive control framework. For each violation event, two tests are performed: (i) an oracle test that replaces operational forecasts with perfect future information to isolate forecast error impacts, and (ii) a high-flexibility test that relaxes flexibility limits to isolate the impact of constrained control actions. Violations are then classified as forecast-limited, flexibility-limited, mixed, or operationally intractable. This enables a systematic assessment of which operational limitation dominates under different operating conditions and provides distribution system operators with quantitative evidence to prioritize investments in forecast improvement, flexibility procurement, or grid reinforcement.

The method is evaluated using Monte Carlo time-series simulations of a realistic German low-voltage distribution grid with photovoltaic generation, residential demand, and controllable curtailment. Operating regimes are analysed by systematically varying forecast quality, flexibility constraints (50 kW and 150 kW caps), and high-stress operating conditions. The analysis is based on a dataset that contains more than 24,000 simulated operating points from probabilistic preventive control studies. Prior results from the same framework demonstrate that probabilistic triggering benefits are strongly regime-dependent, motivating the need for systematic bottleneck attribution. The ongoing analysis is designed to quantify how dominant operational limitations vary across regimes and to support a more targeted interpretation of preventive control performance.

The framework provides operators with a simple and interpretable tool for post-deployment evaluation and evidence-based planning. It also establishes a methodological basis for adaptive control strategies that adjust behaviour based on detected operational bottlenecks. The analysis is based on original simulation studies using a validated low-voltage grid model and Monte Carlo-based uncertainty quantification.