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Point Estimate Method for Voltage Unbalance Evaluation in Residential Distribution Networks with High Penetration of Small Wind Turbines

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  • Chao Long

    (School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Mohamed Emad A. Farrag

    (School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Donald M. Hepburn

    (School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

  • Chengke Zhou

    (School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK)

Abstract

Voltage unbalance (VU) in residential distribution networks (RDNs) is mainly caused by load unbalance in three phases, resulting from network configuration and load-variations. The increasing penetration of distributed generation devices, such as small wind turbines (SWTs), and their uneven distribution over the three phases have introduced difficulties in evaluating possible VU. This paper aims to provide a three-phase probabilistic power flow method, point estimate method to evaluate the VU. This method, considering the randomness of load switching in customers’ homes and time-variation in wind speed, is shown to be capable of providing a global picture of a network’s VU degree so that it can be used for fast evaluation. Applying the 2 m + 1 scheme of the proposed method to a generic UK distribution network shows that a balanced SWT penetration over three phases reduces the VU of a RDN. Greater unbalance in SWT penetration results in higher voltage unbalance factor (VUF), and cause VUF in excess of the UK statutory limit of 1.3%.

Suggested Citation

  • Chao Long & Mohamed Emad A. Farrag & Donald M. Hepburn & Chengke Zhou, 2014. "Point Estimate Method for Voltage Unbalance Evaluation in Residential Distribution Networks with High Penetration of Small Wind Turbines," Energies, MDPI, vol. 7(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:7717-7731:d:42560
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    References listed on IDEAS

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    1. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
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    Cited by:

    1. Xuexia Zhang & Zhiqi Guo & Weirong Chen, 2017. "Probabilistic Power Flow Method Considering Continuous and Discrete Variables," Energies, MDPI, vol. 10(5), pages 1-17, April.

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