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

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  • Eva Buchta

    (Technology, Sustainable Energy & Infrastructure, Siemens AG, 91056 Erlangen, Germany
    Technology and Economics of Multimodal Energy Systems, Technical University of Darmstadt, 64289 Darmstadt, 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
    Technology and Economics of Multimodal Energy Systems, Technical University of Darmstadt, 64289 Darmstadt, Germany)

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.

Suggested Citation

  • Eva Buchta & Mathias Duckheim & Michael Metzger & Paul Stursberg & Stefan Niessen, 2023. "Leveraging Behavioral Correlation in Distribution System State Estimation for the Recognition of Critical System States," Energies, MDPI, vol. 16(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7180-:d:1264301
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    References listed on IDEAS

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    1. Steffen Meinecke & Džanan Sarajlić & Simon Ruben Drauz & Annika Klettke & Lars-Peter Lauven & Christian Rehtanz & Albert Moser & Martin Braun, 2020. "SimBench—A Benchmark Dataset of Electric Power Systems to Compare Innovative Solutions Based on Power Flow Analysis," Energies, MDPI, vol. 13(12), pages 1-19, June.
    2. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.
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