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Application of Bird Swarm Algorithm for Optimal Allocation of Renewable Energy Driven Distributed Generation
Sabarinath.G1, T. Gowri Manohar2
1Sabarinath G, Research Scholar, Department of Electrical and Electronics Engineering, S.V. University College of Engineering, Tirupati (Andhra Pradesh), India.
2Dr. T. Gowri Manohar, Professor, Department of Electrical and Electronics Engineering, S.V. University College of Engineering, Tirupati (Andhra Pradesh), India.
Manuscript received on 25 May 2019 | Revised Manuscript received on 03 June 2019 | Manuscript Published on 22 June 2019 | PP: 167-171 | Volume-8 Issue-3S, February 2019 | Retrieval Number: C10340283S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: With the rapid increase in electrical energy demand and consumption of fossil fuels, electric utilities are looking towards the new green power technologies which utilize non-conventional energy sources like solar, wind and biomass for power generation. Distributed generation (DG) is the most popular and efficient technology that utilizes non-conventional sources. In this article, renewable energy driven DG technologies are proposed to meet the increased load demand and diminish the power loss in the distribution network. Identification of appropriate buses to place DGs in distribution system plays a crucial role in improving the technical and economic benefits of a DG. A new sensitivity technique, index vector (IV) method is used to locate the most appropriate node to connect DG. A new meta-heuristic optimization technique, bird swarm algorithm (BSA) is used to determine the optimal size of DG. Two most popular test systems, IEEE 33 & 69 systems are considered to test and validate the proposed method.
Keywords: Bird Swarm Algorithm, Distribution System, Distributed Generation, Index Vector Method, Power Loss Minimization.
Scope of the Article: Algorithm Engineering