Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer

• Published in: Applied sciences Vol. 15; no. 12; p. 6423
• Main Authors: Wang, Chaoxue, Zhang, Yue
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025
• Subjects: adaptive feasibility model; Clustering; distributed generation; distribution network reconfiguration; Equilibrium; Feasibility; Fuzzy sets; Genetic algorithms; load period partitioning; Machine learning; Methods; modified equilibrium optimizer; multi-objective optimization; Optimization algorithms; Systems stability; Test systems; Iran
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15126423

To better address the reconfiguration problem of distribution networks with distributed generation (DG), a dynamic reconfiguration model is developed that accounts for the time-varying characteristics of both load demand and DG output. First, an enhanced fuzzy C-means clustering method is proposed for load period partitioning, which integrates spatiotemporal load features and optimal network structure similarity to improve clustering accuracy. Second, an adaptive ordered loop-based feasibility judgment model is developed to filter infeasible and low-quality solutions based on operational constraints. Third, an improved Equilibrium Optimizer (IEO), integrating Tent chaotic initialization, elite sorting, and mutation-crossover strategies, is proposed for multi-objective optimization. The proposed framework is validated on IEEE 33- and 69-bus systems. In the IEEE 33-bus system, it achieves a 44.8% reduction in power losses and a 35.9% improvement in voltage deviation. In the IEEE 69-bus system, power loss is reduced by 40.1%, and voltage deviation by 40.5%, demonstrating the proposed method’s robustness, adaptability, and effectiveness across systems of varying scales.