Volume 16, Issue 3 (September 2020)                   IJEEE 2020, 16(3): 353-362 | Back to browse issues page

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Boukaroura A, Slimani L, Bouktir T. Optimal Placement and Sizing of Multiple Renewable Distributed Generation Units Considering Load Variations Via Dragonfly Optimization Algorithm. IJEEE 2020; 16 (3) :353-362
URL: http://ijeee.iust.ac.ir/article-1-1695-en.html
Optimal Placement and Sizing of Multiple Renewable Distributed Generation Units Considering Load Variations Via Dragonfly Optimization Algorithm
A. Boukaroura , L. Slimani , T. Bouktir
Abstract:   (3371 Views)
The progression towards smart grids, integrating renewable energy resources, has increased the integration of distributed generators (DGs) into power distribution networks. However, several economic and technical challenges can result from the unsuitable incorporation of DGs in existing distribution networks. Therefore, optimal placement and sizing of DGs are of paramount importance to improve the performance of distribution systems in terms of power loss reduction, voltage profile, and voltage stability enhancement. This paper proposes a methodology based on Dragonfly Optimization Algorithm (DA) for optimal allocation and sizing of DG units in distribution networks to minimize power losses considering variations of load demand profile. Load variations are represented as lower and upper bounds around base levels. Efficiency of the proposed method is demonstrated on IEEE 33-bus and IEEE 69-bus radial distribution test networks. The results show the performance of this method over other existing methods in the literature.
Keywords: Power Distribution Network , Renewable Distributed Generation , Load Variations , Dragonfly Algorithm , Power Losses , Cost of Energy Losses
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  • Optimal placement and sizing of Renewable Distributed Generation Units in a distribution network;
  • Use of Dragonfly Optimization Algorithm for finding the optimal locations and sizing of DGs.for deferent load levels;
  • Application on two radial distribution test networks.

Type of Study: Research Paper | Subject: Heuristics and Metaheuristics
Received: 2019/10/31 | Revised: 2020/04/06 | Accepted: 2020/04/10
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.