• DocumentCode
    47732
  • Title

    Distribution System Reconfiguration for Annual Energy Loss Reduction Considering Variable Distributed Generation Profiles

  • Author

    Tahboub, Ahmad M. ; Pandi, V. Ravikumar ; Zeineldin, H.H.

  • Author_Institution
    Electr. Eng. & Comput. Sci. Dept., Inst. Center for Energy (iEnergy), Abu Dhabi, United Arab Emirates
  • Volume
    30
  • Issue
    4
  • fYear
    2015
  • fDate
    Aug. 2015
  • Firstpage
    1677
  • Lastpage
    1685
  • Abstract
    In this paper, a new formulation for distribution system reconfiguration (DSR) is proposed for minimizing the annual energy losses considering the variability in active and reactive power demand and distributed generation (DG) profiles. A fuzzy C-means clustering algorithm is used to obtain representative centroids from annual DG and power demand profiles. The DSR study is formulated as a mixed-integer nonlinear programming optimization problem and tested on a 33-bus and an 84-bus system. The minimization is subject to the power balance, bus voltage, and distribution system radiality constraints. Using this formulation, a single optimum configuration that minimizes annual energy losses is found and shown to be different from the optimum configuration, found in previous literature, which focuses on minimizing power losses at peak or average loads. In addition, the prospective advantages of grid automation on DSR are demonstrated to provide further energy loss reduction by including the possibility of interchanging a predefined number of configurations that minimize annual energy losses.
  • Keywords
    distributed power generation; fuzzy set theory; integer programming; minimisation; nonlinear programming; pattern clustering; power distribution lines; power grids; reactive power; DG; DSR; active power demand; annual energy loss reduction; bus voltage; distribution line system reconfiguration; fuzzy C-means clustering algorithm; mixed integer nonlinear programming optimization problem; power balance; power grid automation; power loss minimization; reactive power demand; variable distributed generation profiles; Clustering algorithms; Energy loss; Load flow; Minimization; Optimization; Reactive power; Switches; Distributed generation (DG); distribution system reconfiguration; energy losses; fuzzy C-means clustering; genetic algorithm (GA); load variability;
  • fLanguage
    English
  • Journal_Title
    Power Delivery, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8977
  • Type

    jour

  • DOI
    10.1109/TPWRD.2015.2424916
  • Filename
    7097066