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Distribution Network Power Loss Analysis Considering Uncertainties in Distributed Generations

Author

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  • Hongmei Li

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221100, China
    These authors contributed equally to this work.)

  • Hantao Cui

    (Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA
    These authors contributed equally to this work.)

  • Chunjie Li

    (School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou 221100, China)

Abstract

Distribution network loss analysis is crucial for the economic operation in residential distribution networks. The increasing level of distributed generation (DG) has considerably improved the overall sustainability but raised the uncertainty in system losses and exacerbated voltage profiles. This paper presents a nodal distribution loss analysis approach in which the losses induced by loads and DGs are calculated recursively. In order to characterize the uncertainty, the Latin hypercube sampling (LHS)-based approach is presented for obtaining DG output samples. Further, the LHS-based sampling and loss analysis methods are combined into a proposed stochastic framework for loss analysis, which takes into account the DG output uncertainty. Case studies on a 36-bus radial distribution network verified the stochastic loss analysis method. Compared with the simple random sampling method, the proposed LHS-based stochastic loss analysis method can reach the same accuracy level for nodal voltages and losses more efficiently.

Suggested Citation

  • Hongmei Li & Hantao Cui & Chunjie Li, 2019. "Distribution Network Power Loss Analysis Considering Uncertainties in Distributed Generations," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1311-:d:210353
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    References listed on IDEAS

    as
    1. Kalambe, Shilpa & Agnihotri, Ganga, 2014. "Loss minimization techniques used in distribution network: bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 184-200.
    2. Anna Rita Di Fazio & Mario Russo & Sara Valeri & Michele De Santis, 2016. "Sensitivity-Based Model of Low Voltage Distribution Systems with Distributed Energy Resources," Energies, MDPI, vol. 9(10), pages 1-16, October.
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    Cited by:

    1. Mirosław Kornatka & Anna Gawlak, 2021. "An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators," Energies, MDPI, vol. 14(21), pages 1-12, October.
    2. Amro M Elshurafa & Abdel Rahman Muhsen, 2019. "The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study," Sustainability, MDPI, vol. 11(16), pages 1-20, August.

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