Modified Dragonfly Optimisation for Distributed Energy Mix in Distribution Networks


This article presents a two-stage optimization model aiming to determine optimal energy mix in distribution networks, i.e., battery energy storage, fuel cell, and wind turbines. It aims to alleviate the impact of high renewable penetration on the systems. To solve the proposed complex optimization model, a standard variant of the dragonfly algorithm (DA) has been improved and then applied to find the optimal mix of distributed energy resources. The suggested improvements are validated before their application. A heuristic approach has also been introduced to solve the second stage problem that determines the optimal power dispatch of battery energy storage as per the size suggested by the first stage. The proposed framework was implemented on a benchmark 33-bus and a practical Indian 108-bus distribution network over different test cases. The proposed model for energy mix and modified DA technique has significantly enhanced the operational performance of the network in terms of average annual energy loss reduction, node voltage profiles, and demand fluctuation caused by renewables.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
Additional Information: Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// 4.0/). Funding: This research was supported by project “Street2Grid—An electricity blockchain platform for P2P energy trading” (Reference: EP/S001778/2), funded by the Engineering and Physical Sciences Research Council (EPSRC), UK. The Author, Nand K. Meena would like to acknowledge the funding received from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 713694.
Uncontrolled Keywords: battery energy storage system,distribution networks,fuel cells,optimization,wind turbines
Publication ISSN: 1996-1073
Last Modified: 30 Apr 2024 07:31
Date Deposited: 14 Sep 2021 09:08
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Related URLs: https://www.mdp ... 1073/14/18/5690 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-09-10
Accepted Date: 2021-09-07
Authors: Singh, Pushpendra
Meena, Nand Kishor (ORCID Profile 0000-0002-4092-3921)
Yang, Jin
Bishnoi, Shree Krishna
Vega-Fuentes, Eduardo
Lou, Chengwei



Version: Published Version

License: Creative Commons Attribution

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