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On the Classification of Low Voltage Feeders for Network Planning and Hosting Capacity Studies

Author

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  • Benoît Bletterie

    (Electric Energy Systems, Center for Energy, Austrian Institute of Technology, Vienna 1210, Austria)

  • Serdar Kadam

    (Electric Energy Systems, Center for Energy, Austrian Institute of Technology, Vienna 1210, Austria)

  • Herwig Renner

    (Institute of Electrical Power Systems, Faculty of Electrical and Information Engineering, Graz University of Technology, Graz 8010, Austria)

Abstract

The integration of large amounts of generation into distribution networks faces some limitations. By deploying reactive power-based voltage control concepts (e.g., volt/var control with distributed generators), the voltage rise caused by generators can be partly mitigated. As a result, the network hosting capacity can be accordingly increased, and costly network reinforcement might be avoided or postponed. This works however only for voltage-constrained feeders (opposed to current-constrained feeders). Due to the low level of monitoring in low voltage networks, it is important to be able to classify feeders according to the expected constraint in order to avoid the overloading risk. The main purpose of this paper is to investigate to which extent it is possible to predict the hosting capacity constraint (voltage or current) of low voltage feeders on the basis of a large network data set. Two machine-learning techniques have been implemented and compared: clustering (unsupervised) and classification (supervised). The results show that the general performance of the classification or clustering algorithms might be considered as rather poor at a first glance, reflecting the diversity of real low voltage feeders. However, a detailed analysis shows that the benefit of the classification is significant.

Suggested Citation

  • Benoît Bletterie & Serdar Kadam & Herwig Renner, 2018. "On the Classification of Low Voltage Feeders for Network Planning and Hosting Capacity Studies," Energies, MDPI, vol. 11(3), pages 1-23, March.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:651-:d:136274
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    References listed on IDEAS

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    1. Serdar Kadam & Benoît Bletterie & Wolfgang Gawlik, 2017. "A Large Scale Grid Data Analysis Platform for DSOs," Energies, MDPI, vol. 10(8), pages 1-24, July.
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    Cited by:

    1. Tobias Rösch & Peter Treffinger, 2019. "Cluster Analysis of Distribution Grids in Baden-Württemberg," Energies, MDPI, vol. 12(20), pages 1-25, October.
    2. Abouzar Estebsari & Luca Barbierato & Alireza Bahmanyar & Lorenzo Bottaccioli & Enrico Macii & Edoardo Patti, 2019. "A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration," Energies, MDPI, vol. 12(8), pages 1-27, April.
    3. Miha Grabner & Andrej Souvent & Nermin Suljanović & Andrej Košir & Boštjan Blažič, 2019. "Probabilistic Methodology for Calculating PV Hosting Capacity in LV Networks Using Actual Building Roof Data," Energies, MDPI, vol. 12(21), pages 1-15, October.
    4. Daniel-Leon Schultis, 2019. "Comparison of Local Volt/var Control Strategies for PV Hosting Capacity Enhancement of Low Voltage Feeders," Energies, MDPI, vol. 12(8), pages 1-27, April.
    5. Tobias Rösch & Peter Treffinger & Barbara Koch, 2020. "Remuneration of Distribution Grids for Enhanced Regenerative Electricity Deployment—An Analysis and Model for the Analysis of Grid Structures in Southern Germany Using Linear Programming," Energies, MDPI, vol. 13(20), pages 1-26, October.
    6. Ma, Chenjie & Menke, Jan-Hendrik & Dasenbrock, Johannes & Braun, Martin & Haslbeck, Matthias & Schmid, Karl-Heinz, 2019. "Evaluation of energy losses in low voltage distribution grids with high penetration of distributed generation," Applied Energy, Elsevier, vol. 256(C).

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