Integration of distributed generation in power networks considering constraints on discrete size of distributed generation units

Idris Musa, Shady Gadoue*, Bashar Zahawi

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

An optimization algorithm based on a novel discrete particle swarm optimization technique is proposed in this article for optimal sizing and location of distributed generation in a power distribution network. The proposed algorithm considers distributed generation size and location as discrete variables substantially reducing the search space and, consequently, computational requirements of the optimization problem. The proposed algorithm treats the generator sizes as real discrete variables with uneven step sizes that reflect the sizes of commercially available generators, meaning that it can handle a mixed search space of integer (generator location), discrete (generator sizes), and continuous (reactive power output) variables while substantially reducing the search space and, consequently, computational burden of the optimization problem. The validity of the proposed discrete particle swarm optimization algorithm is tested on a standard 69-bus benchmark distribution network with four different test cases. Two optimization scenarios are considered for each test case: a single objective optimization study where network real power loss is minimized and a multi-objective study in which network voltages are also considered. The proposed algorithm is shown to be effective in finding the optimal or near-optimal solution to the problem at a fraction of the computational cost associated with other algorithms.

Original languageEnglish
Pages (from-to)984-994
Number of pages11
JournalElectric Power Components and Systems
Volume42
Issue number9
Early online date28 May 2014
DOIs
Publication statusPublished - 4 Jul 2014

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Keywords

  • Dichotomy search algorithm
  • Distributed generation
  • Evolutionary computation
  • Particle swarm optimization
  • Power loss reduction
  • Power system optimization

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