International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 8 (August 2023), Pages: 40-50

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 Original Research Paper

A soft computing technique based on PSO algorithm and energy management strategy for optimal allocation and placement of PVDG-BES units

 Author(s): 

 Imene Khenissi 1, Nasser Alkhateeb 2, Raida Sellami 1, Gharbi A. Alshammari 2, Naif A. Alshammari 2, Tawfik Guesmi 2, *, Rafik Neji 1

 Affiliation(s):

 1Department of Electrical Engineering, National Engineering School of Sfax (ENIS), University of Sfax, Sfax, Tunisia
 2Department of Electrical Engineering, College of Engineering, University of Ha’il, Ha’il, Saudi Arabia

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 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-8221-2610

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.08.005

 Abstract:

In the pursuit of achieving a harmonious equilibrium between electricity production and consumption, the integration of distribution generators (DG) has garnered substantial attention. Yet, the escalated integration of DG systems has given rise to the predicament of reverse power flow, instigating elevated system power losses and voltage profile distortions. Thus, an imperative emerges to judiciously apportion and dimension DG systems, complemented by the incorporation of battery energy storage (BES) systems, as a remedial measure against these challenges. In this scholarly work, we present an innovative approach rooted in a precise energy management strategy (EMS) aimed at the adept allocation and capacity optimization of PVDG-BES systems. The study employs a two-step optimization methodology, the former facet of which expounds on the influence of BES system integration on grid power losses and voltage profiles during stable operational conditions. Subsequently, a pioneering optimization technique is formulated in the latter facet to identify the optimal siting and capacity allocation of the aforementioned system based on an optimal EMS framework. The primary focal point of this investigation is the minimization of total power losses. Validation of our proposition is conducted on the IEEE 14-bus standard system, incorporating the particle swarm optimization (PSO) algorithm. Simulation outcomes incontrovertibly affirm the efficacy and robustness of the proposed EMS, yielding substantive reductions in power losses and noteworthy enhancements in voltage profile integrity. Notably, the implementation of EMS leads to a remarkable 31% reduction in total power losses as compared to the initial scenario, prior to the amalgamation of PVDG-BES components. In sum, this study epitomizes a comprehensive strategy for fortifying power grid efficiency by orchestrating the symbiotic interplay of distribution generators and battery energy storage systems through an adept energy management paradigm.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Electricity production, Distribution generators, Reverse power flow, Battery energy storage, Energy management strategy

 Article History: Received 12 February 2023, Received in revised form 15 June 2023, Accepted 25 June 2023

 Acknowledgment 

This research has been funded by Scientific Research Deanship at the University of Ha’il–Saudi Arabia through project number GR-22 044.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Khenissi I, Alkhateeb N, Sellami R, Alshammari GA, Alshammari NA, Guesmi T, and Neji R (2023). A soft computing technique based on PSO algorithm and energy management strategy for optimal allocation and placement of PVDG-BES units. International Journal of Advanced and Applied Sciences, 10(8): 40-50

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 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12 

 Tables

 Table 1 Table 2 Table 3 Table 4

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