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The ongoing electrification introduces new challenges to distribution systemoperators (DSOs). Controllable resources may simultaneously react to pricesignals, potentially leading to network violations. DSOs require reliable andaccurate low-voltage state estimation (LVSE) to improve awareness and mitigatesuch events. However, the influence of flexibility activations on LVSE has notbeen addressed yet. It remains unclear if flexibility-induced uncertainty canbe reliably quantified to enable robust DSO decision-making. In this work,uncertainty quantification in LVSE is systematically investigated for multiplescenarios of input availability and flexibility utilization, using real data.For that purpose, a Bayesian neural network (BNN) is compared to quantileregression. Results show that frequent flexibility activations cansignificantly deteriorate LVSE performance, unless secondary substationmeasurements are available. Moreover, it is demonstrated that the BNN capturesflexibility-induced voltage drops by dynamically extending the predictioninterval during activation periods, and that it improves interpretabilityregarding the cause of uncertainty.

Uncertainty Quantification in LV State Estimation Under High Shares of Flexible Resources