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A Novel Multi-Area Distribution State Estimation Approach with Nodal Redundancy

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  • Luis Vargas

    (Electrical Engineering Department, Faculty of Physical and Mathematical Sciences, University of Chile, Santiago 8370458, Chile)

  • Henrry Moyano

    (Faculty of Economic and Administrative Sciences, University of Cuenca, Cuenca 010101, Ecuador)

Abstract

State estimators based on load flows and applied in electrical power systems (EPS) are a basic and crucial function in energy management systems (EMS), since they must guarantee the quality of their results for decision-making. In this research, we propose a new method for partitioning an electrical system within distributed estimation processes. This method is developed under the concept of nodal redundancy and considers the number of measurements associated with each bus of the electrical system. By distributing the measurements in subsystems, such that each redundancy is evenly distributed, the proposed method aims to improve the performance of both centralized and distributed estimation techniques developed in the literature. We evaluate the proposed method by using the IEEE 14-bus and IEEE 118-bus systems, considering several operating cases and a wide array of measurements of the electrical power system. Results demonstrate the quality of the estimate and the processing time for both traditional and distributed estimates under the proposed methodology.

Suggested Citation

  • Luis Vargas & Henrry Moyano, 2023. "A Novel Multi-Area Distribution State Estimation Approach with Nodal Redundancy," Energies, MDPI, vol. 16(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4138-:d:1148911
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    References listed on IDEAS

    as
    1. Mohammad Gholami & Ali Abbaspour Tehrani-Fard & Matti Lehtonen & Moein Moeini-Aghtaie & Mahmud Fotuhi-Firuzabad, 2021. "A Novel Multi-Area Distribution State Estimation Approach for Active Networks," Energies, MDPI, vol. 14(6), pages 1-19, March.
    2. Nikolaos M. Manousakis & George N. Korres, 2021. "Application of State Estimation in Distribution Systems with Embedded Microgrids," Energies, MDPI, vol. 14(23), pages 1-18, November.
    3. Md Jakir Hossain & Mia Naeini, 2022. "Multi-Area Distributed State Estimation in Smart Grids Using Data-Driven Kalman Filters," Energies, MDPI, vol. 15(19), pages 1-17, September.
    4. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2016. "State estimation of medium voltage distribution networks using smart meter measurements," Applied Energy, Elsevier, vol. 184(C), pages 207-218.
    5. Rachid Darbali-Zamora & Jay Johnson & Adam Summers & C. Birk Jones & Clifford Hansen & Chad Showalter, 2021. "State Estimation-Based Distributed Energy Resource Optimization for Distribution Voltage Regulation in Telemetry-Sparse Environments Using a Real-Time Digital Twin," Energies, MDPI, vol. 14(3), pages 1-21, February.
    6. Yang, Xuan & Zhang, Xiao-Ping & Zhou, Suyang, 2012. "Coordinated algorithms for distributed state estimation with synchronized phasor measurements," Applied Energy, Elsevier, vol. 96(C), pages 253-260.
    Full references (including those not matched with items on IDEAS)

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