Sensitivity Quantification for Distribution System State Estimation

TL;DR

This paper examines how the assumed distribution of pseudo-measurements affects uncertainty bounds in distribution system state estimation, revealing systematic overestimations with heavy-tailed and skewed distributions.

Contribution

It introduces a diagnostic framework using the Fisher Information Matrix to quantify the sensitivity of uncertainty bounds to distributional assumptions in DSSE.

Findings

Heavy-tailed and skewed distributions cause WLS to overstate uncertainty bounds.

Sensitivity to distributional assumptions varies across buses and scenarios.

The CRB ratio does not detect mean-shift biases, indicating a fundamental limitation.

Abstract

Pseudo-measurements are the dominant source of uncertainty in distribution system state estimation (DSSE), yet their distributional assumptions are treated as fixed inputs by existing uncertainty quantification methods. This paper investigates whether the uncertainty bounds assumed by weighted least squares (WLS)-based DSSE are sensitive to these distributional assumptions, and whether this sensitivity is quantifiable using the Fisher Information Matrix (FIM). We propose a diagnostic framework that compares the true Cram\'er-Rao Bound (CRB) against the WLS-assumed CRB via a per-bus, per-scenario ratio, computed directly from the converged WLS solution. Pseudo-measurement distributions are varied across five types in 22 variants matched at equal spread to isolate shape effects from variance. Experiments on the CIGRE MV network across 100 operating scenarios yield three findings. First, heavy-tailed and skewed distributions show consistently that WLS systematically overstates its uncertainty bounds. Second, the degree of miscalibration varies across buses and operating scenarios, confirming that distributional sensitivity is not uniform. Third, the CRB ratio is structurally blind to mean-shift bias, exposing a fundamental limitation of variance-based uncertainty diagnostics. Together, these results confirm the hypothesis and show that the choice of pseudo-measurement distribution directly distorts the confidence limits under WLS-based assumptions, which must be explicitly accounted for in any uncertainty-aware DSSE method.