Optimal Distribution Network Reconfiguration Using Particle Swarm Optimization-Simulated Annealing: Adaptive Inertia Weight Based on Simulated Annealing
Franklin Jesus Simeon Pucuhuayla,
Dionicio Zocimo Ñaupari Huatuco (),
Yuri Percy Molina Rodriguez and
Jhonatan Reyes Llerena
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Franklin Jesus Simeon Pucuhuayla: Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Av. Túpac Amaru 210, Lima 15333, Peru
Dionicio Zocimo Ñaupari Huatuco: Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Av. Túpac Amaru 210, Lima 15333, Peru
Yuri Percy Molina Rodriguez: Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil
Jhonatan Reyes Llerena: Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Av. Túpac Amaru 210, Lima 15333, Peru
Energies, 2025, vol. 18, issue 20, 1-23
Abstract:
The reconfiguration of distribution networks plays a crucial role in minimizing active power losses and enhancing reliability, but the problem becomes increasingly complex with the integration of distributed generation (DG). Traditional optimization methods and even earlier hybrid metaheuristics often suffer from premature convergence or require problem reformulations that compromise feasibility. To overcome these limitations, this paper proposes a novel hybrid algorithm that couples Particle Swarm Optimization (PSO) with Simulated Annealing (SA) through an adaptive inertia weight mechanism derived from the Lundy–Mees cooling schedule. Unlike prior hybrid approaches, our method directly addresses the original non-convex, combinatorial nature of the Distribution Network Reconfiguration (DNR) problem without convexification or post-processing adjustments. The main contributions of this study are fourfold: (i) proposing a PSO-SA hybridization strategy that enhances global exploration and avoids stagnation; (ii) introducing an adaptive inertia weight rule tuned by SA, more effective than traditional schemes; (iii) applying a stagnation-based stopping criterion to speed up convergence and reduce computational cost; and (iv) validating the approach on 5-, 33-, and 69-bus systems, with and without DG, showing robustness, recurrence rates above 80%, and low variability compared to conventional PSO. Simulation results confirm that the proposed PSO-SA algorithm achieves superior performance in both loss minimization and solution stability, positioning it as a competitive and scalable alternative for modern active distribution systems.
Keywords: distribution network reconfiguration; particle swarm optimization; simulated annealing; hybrid metaheuristics; power loss minimization; adaptive inertia weight; active distribution systems (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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