Optimal Placement of Wireless Smart Concentrators in Power Distribution Networks Using a Metaheuristic Approach

Cristoercio André Silva, Richard Wilcamango-Salas, Joel D. Melo, Jesús M. López-Lezama () and Nicolás Muñoz-Galeano
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Cristoercio André Silva: Graduate Program in Energy, Federal University of ABC (UFABC), Santo Andre 09280560, São Paulo, Brazil
Richard Wilcamango-Salas: Graduate Program in Energy, Federal University of ABC (UFABC), Santo Andre 09280560, São Paulo, Brazil
Joel D. Melo: Graduate Program in Energy, Federal University of ABC (UFABC), Santo Andre 09280560, São Paulo, Brazil
Jesús M. López-Lezama: Research Group on Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 56-108, Medellin 050010, Colombia
Nicolás Muñoz-Galeano: Research Group on Efficient Energy Management (GIMEL), Departamento de Ingeniería Eléctrica, Universidad de Antioquia, Calle 67 No. 56-108, Medellin 050010, Colombia

Energies, 2025, vol. 18, issue 17, 1-27

Abstract: The optimal allocation of Wireless Smart Concentrators (WSCs) in low-voltage (LV) distribution networks poses significant challenges due to signal attenuation caused by varying building densities and vegetation. This paper proposes a Variable Neighborhood Search (VNS) algorithm to optimize the placement of WSCs in LV distribution networks. To comprehensively assess the proposed approach, both linear and nonlinear mathematical formulations are considered, depending on whether the distance between meters and concentrators is treated as a fixed parameter or as a decision variable. The performance of the proposed VNS algorithm is benchmarked against both exact solvers and metaheuristics such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Tabu Search (TS). In the linear formulation, VNS achieved the exact optimal solution with execution times up to 75% faster than competing methods. For the more complex nonlinear model, VNS consistently identified superior solutions while requiring less computational effort. These results underscore the algorithm’s ability to balance solution quality and efficiency, making it particularly well-suited for large-scale, resource-constrained utility planning.

Keywords: wireless communication; wireless smart concentrators; wireless smart meters; Variable Neighborhood Search (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: 2025
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