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
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