Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution

Dhivya Swaminathan, Arul Rajagopalan (), Oscar Danilo Montoya, Savitha Arul and Luis Fernando Grisales-Noreña ()
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Dhivya Swaminathan: School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Arul Rajagopalan: School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
Oscar Danilo Montoya: Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia
Savitha Arul: Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai 602105, Tamil Nadu, India
Luis Fernando Grisales-Noreña: Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile

Energies, 2023, vol. 16, issue 5, 1-24

Abstract: Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.

Keywords: flower pollination algorithm; golden search; hybrid algorithm; loss minimization; network reconfiguration; radial distribution system; smart cities (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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