Hybrid Elephant Herding and Particle Swarm Optimizations for Optimal DG Integration in Distribution Networks


In this article, the amalgamation of two well-established meta-heuristic optimization methods is presented to solve the multi-objective distributed generation (DG) allocation problem of distribution systems. To overcome some of the shortcomings of newly developed elephant herding optimization (EHO), an improvement is suggested and then, a prominent feature of particle swarm optimization is introduced to the modified version of EHO. The suggested modifications are validated by solving a single objective DG integration problem where various performance parameters of the proposed hybrid method are compared with their individual standard variants. After validation, the proposed technique is exploited to solve a multi-objective DG allocation problem of distribution systems, aiming to minimize power loss and node voltage deviation while simultaneously maximizing the voltage stability index of three benchmark distribution systems namely, 33-bus, 69-bus and 118-bus. The obtained simulation results are further compared with that of the same available in the existing literature. This comparison reveals that the proposed hybrid approach is promising to solve the multi-objective DG integration problem of distribution systems as compared to many existing methods.

Publication DOI: https://doi.org/10.1080/15325008.2020.1797931
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis Group in Electric Power Components and Systems on 28 Aug 2020, available online at: http://www.tandfonline.com/10.1080/15325008.2020.1797931. Funding: This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 713694.
Uncontrolled Keywords: distributed generation,distribution network,elephant herding optimization,hybrid methods,multi-objective problem,particle swarm optimization,power loss,voltage stability,Energy Engineering and Power Technology,Mechanical Engineering,Electrical and Electronic Engineering
Publication ISSN: 1532-5016
Last Modified: 27 Jun 2024 10:30
Date Deposited: 07 Sep 2020 12:49
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.tan ... rnalCode=uemp20 (Publisher URL)
PURE Output Type: Article
Published Date: 2020-08-28
Accepted Date: 2020-06-21
Authors: Singh, Pushpendra
Meena, Nand K. (ORCID Profile 0000-0002-4092-3921)
Bishnoi, Shree Krishna
Singh, Balvinder
Bhadu, Mahendra



Version: Accepted Version

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