An improved MOPSO algorithm for optimal sizing and placement of distributed generation: a case study of the Tunisian offshore distribution network (ASHTART)

Sellami, R., Sher, F. ORCID: 0000-0003-2890-5912 and Neji, R., 2022. An improved MOPSO algorithm for optimal sizing and placement of distributed generation: a case study of the Tunisian offshore distribution network (ASHTART). Energy Reports, 8, pp. 6960-6975.

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Abstract

Investigation of power distribution systems is an attractive field in the power system analysis. The location and size of Distributed Generation (DG) are important and an uncontrollable choice might result in a negative impact on the system’s behavior. This paper presents an improved approach for optimum simultaneous DG placement and sizing to reduce power loss and improve the voltage profile and the stability of distribution networks. Numerous methods have been used for solving this difficult combinatorial problem like Particle Swarm Optimization (PSO) algorithm used in this work. The suggested MOPSO method combined with MATPOWER toolbox was tested on standard IEEE 33-bus and IEEE 69-bus radial distribution networks (RDN) throw different scenarios. The major contribution of this paper was that the procedure was applied to the Tunisian electricity distribution network (ASHTART) of the SEREPT (Society for Research and Exploitation of Petroleum) to prove the efficiency of applying this method on real-world systems. This paper will also compare the proposed method with previous findings methods and the obtained results testify to the robustness and effectiveness of the improved MOPSO algorithm in terms of reduction in total active power losses (TAPL), maximization of a percentage of minimum voltage improvement (%MVI), amelioration in voltage profiles and also maximization in voltage stability index (VSI) while finding the optimal location and size of the DG units (OPSDG).

Item Type:	Journal article
Publication Title:	Energy Reports
Creators:	Sellami, R., Sher, F. and Neji, R.
Publisher:	Elsevier BV
Date:	November 2022
Volume:	8
Identifiers:	Number Type 10.1016/j.egyr.2022.05.049 DOI S2352484722008952 Publisher Item Identifier 1601674 Other
Rights:	Copyright: © 2022 The Authors. Published by Elsevier Ltd. This article is available under the Creative Commons CC-BY-NC-ND license and permits non-commercial use of the work as published, without adaptation or alteration provided the work is fully attributed.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	26 Sep 2022 13:01
Last Modified:	26 Sep 2022 13:01
URI:	https://irep.ntu.ac.uk/id/eprint/47122

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