Optimal distribution network reinforcement considering load growth, line loss, and reliability

Ziari, Iman, Ledwich, Gerard, Ghosh, Arindam, & Platt, Glenn (2012) Optimal distribution network reinforcement considering load growth, line loss, and reliability. IEEE Transactions on Power Systems, 28(2), pp. 587-597.

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In this paper, a new comprehensive planning methodology is proposed for implementing distribution network reinforcement. The load growth, voltage profile, distribution line loss, and reliability are considered in this procedure. A time-segmentation technique is employed to reduce the computational load. Options considered range from supporting the load growth using the traditional approach of upgrading the conventional equipment in the distribution network, through to the use of dispatchable distributed generators (DDG). The objective function is composed of the construction cost, loss cost and reliability cost. As constraints, the bus voltages and the feeder currents should be maintained within the standard level. The DDG output power should not be less than a ratio of its rated power because of efficiency. A hybrid optimization method, called modified discrete particle swarm optimization, is employed to solve this nonlinear and discrete optimization problem. A comparison is performed between the optimized solution based on planning of capacitors along with tap-changing transformer and line upgrading and when DDGs are included in the optimization.

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19 citations in Scopus
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15 citations in Web of Science®

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ID Code: 57370
Item Type: Journal Article
Refereed: Yes
Keywords: Distribution system, optimization method, reinforcement
DOI: 10.1109/TPWRS.2012.2211626
ISSN: 0885-8950
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Power and Energy Systems Engineering (excl. Renewable Power) (090607)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 IEEE
Deposited On: 19 Feb 2013 01:05
Last Modified: 23 Jan 2014 05:27

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