Leveraging Behavioral Correlation in Distribution System State Estimation for the Recognition of Critical System States

Eva Buchta (), Mathias Duckheim, Michael Metzger, Paul Stursberg and Stefan Niessen
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Eva Buchta: Technology, Sustainable Energy & Infrastructure, Siemens AG, 91056 Erlangen, Germany
Mathias Duckheim: Technology, Sustainable Energy & Infrastructure, Siemens AG, 91056 Erlangen, Germany
Michael Metzger: Technology, Sustainable Energy & Infrastructure, Siemens AG, 81739 Munich, Germany
Paul Stursberg: Technology, Sustainable Energy & Infrastructure, Siemens AG, 81739 Munich, Germany
Stefan Niessen: Technology, Sustainable Energy & Infrastructure, Siemens AG, 91056 Erlangen, Germany

Energies, 2023, vol. 16, issue 20, 1-21

Abstract: State estimation for distribution systems faces the challenge of dealing with limited real-time measurements and historical data. This work describes a Bayesian state estimation approach tailored for practical implementation in different data availability scenarios, especially when both real-time and historical data are scarce. The approach leverages statistical correlations of the state variables from a twofold origin: (1) from the physical coupling through the grid and (2) from similar behavioral patterns of customers. We show how these correlations can be parameterized, especially when no historical time series data are available, and that accounting for these correlations yields substantial accuracy gains for state estimation and for the recognition of critical system states, i.e., states with voltage or current limit violations. In a case study, the approach is tested in a realistic European-type, medium-voltage grid. The method accurately recognizes critical system states with an aggregated true positive rate of 98%. Compared to widely used approaches that do not consider these correlations, the number of undetected true critical cases can be reduced by a factor of up to 9. Particularly in the case where no historical smart meter time series data is available, the recognition accuracy of critical system states is nearly as high as with full smart meter coverage.

Keywords: Bayesianstate estimation; distribution system; distribution system state estimation; load correlations; medium voltage grid; smart meter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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