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Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids

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  • Karthikeyan Nainar

    (Department of Energy Technology, Aalborg Univerisity, 9220 Aalborg, Denmark)

  • Florin Iov

    (Department of Energy Technology, Aalborg Univerisity, 9220 Aalborg, Denmark)

Abstract

The installation of smart meters at customer premises provides opportunities for the monitoring of distribution grids. This paper addresses the problem of improving the observability of low-voltage distribution grids using smart metering infrastructure. In particular, this paper deals with the application of state estimation algorithm using smart meter measurements for near-real-time monitoring of low-voltage distribution grids. This application is proposed to use a nonlinear weighted least squares method-based algorithm for estimating the node voltages from minimum number of smart meter measurements. This paper mainly deals with sensitivity analysis of the state estimation algorithm with respect to multiple uncertainties for, e.g., measurements errors, line parameter errors, and pseudo-measurements. Simulation studies are conducted to estimate the accuracy of the DSSE under various operating scenarios of a real-life low-voltage grid, and cost-effective ways to improve the accuracy of the state estimation algorithm are also evaluated. The paper concludes that by using smart meter measurements from few locations, voltage profiles of the low-voltage grid can be estimated with reasonable accuracy in near-real-time.

Suggested Citation

  • Karthikeyan Nainar & Florin Iov, 2020. "Smart Meter Measurement-Based State Estimation for Monitoring of Low-Voltage Distribution Grids," Energies, MDPI, vol. 13(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5367-:d:428331
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    References listed on IDEAS

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    1. Ahmad, Fiaz & Rasool, Akhtar & Ozsoy, Emre & Sekar, Raja & Sabanovic, Asif & Elitaş, Meltem, 2018. "Distribution system state estimation-A step towards smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2659-2671.
    2. McDonald, Jim, 2008. "Adaptive intelligent power systems: Active distribution networks," Energy Policy, Elsevier, vol. 36(12), pages 4346-4351, December.
    3. Thiago Mota Soares & Ubiratan Holanda Bezerra & Maria Emília de Lima Tostes, 2019. "Full-Observable Three-Phase State Estimation Algorithm Applied to Electric Distribution Grids," Energies, MDPI, vol. 12(7), pages 1-16, April.
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    Cited by:

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    2. Ruipeng Guo & Lilan Dong & Hao Wu & Fangdi Hou & Chen Fang, 2021. "A Practical GERI-Based Method for Identifying Multiple Erroneous Parameters and Measurements Simultaneously," Energies, MDPI, vol. 14(12), pages 1-21, June.
    3. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    4. Karthikeyan Nainar & Florin Iov, 2021. "Three-Phase State Estimation for Distribution-Grid Analytics," Clean Technol., MDPI, vol. 3(2), pages 1-14, May.
    5. Fabio Napolitano & Juan Diego Rios Penaloza & Fabio Tossani & Alberto Borghetti & Carlo Alberto Nucci, 2021. "Three-Phase State Estimation of a Low-Voltage Distribution Network with Kalman Filter," Energies, MDPI, vol. 14(21), pages 1-19, November.
    6. Jingyeong Park & Daisuke Kodaira & Kofi Afrifa Agyeman & Taeyoung Jyung & Sekyung Han, 2021. "Adaptive Power Flow Prediction Based on Machine Learning," Energies, MDPI, vol. 14(13), pages 1-18, June.

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