Optimization of Distribution Network Reconfiguration Based on Improved Genetic Algorithm

Zhiyu Chen

doi:10.47297/taposatWSP2633-456941.20250609

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Optimization of Distribution Network Reconfiguration Based on Improved Genetic Algorithm

Volume 6, Issue 9

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Optimization of Distribution Network Reconfiguration Based on Improved Genetic Algorithm
Optimization of Distribution Network Reconfiguration Based on Improved Genetic Algorithm
Belarusian National Technical University, Minsk, 220013, Belarus
Vol. 6, Issue 9, Pages: 217-220(2025)
Published：30 September 2025
DOI：10.47297/taposatWSP2633-456941.20250609
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Chen Zhiyu.Optimization of Distribution Network Reconfiguration Based on Improved Genetic Algorithm[J].Theory and Practice of Science and Technology,2025,06(09):217-220.
DOI：

Chen Zhiyu.Optimization of Distribution Network Reconfiguration Based on Improved Genetic Algorithm[J].Theory and Practice of Science and Technology,2025,06(09):217-220. DOI： 10.47297/taposatWSP2633-456941.20250609.

摘要

Abstract

This paper explores the significance of distribution network restructuring and its application in optimizing power systems

focusing on analyzing the advantages of genetic algorithms in distribution network restructuring and proposing improvement strategies. With the development of urbanization and smart grid technologies

the load characteristics of distribution networks have become increasingly complex

making restructuring a key approach to reduce losses and enhance power supply reliability and economic efficiency. To overcome issues such as parameter selection and premature convergence in traditional genetic algorithms

this paper proposes an improved genetic algorithm based on a multi-objective fitness function. Through methods including mixed integer encoding

partial matching crossover

and dynamically adjusted mutation probability

the distribution network restructuring process is optimized. Research results demonstrate that this algorithm significantly enhances network economic efficiency and safety

reduces carbon emissions

and promotes the intelligent development of distribution networks.

关键词

Keywords

references

Dong-Li Duan, Xiao-Dong Ling, Xiao-Yue Wu, Bin Zhong. Reconfiguration of distribution network for loss reduction and reliability improvement based on an enhanced genetic algorithm[J]. International Journal of Electrical Power and Energy Systems, 2015, 6488-95.

Salah Kamel, Mansur Khasanov, Mohamed H. Haasan, Jasur Abdubannaev, José Luis Dominguez Garcia. Quadratic Interpolation Manta Ray Foraging Optimization: A Meta-Heuristic Approach for Simultaneous DG Allocation and Distribution System Reconfiguration [J]. Cluster Computing, 2025, 28(9): 556-556.

Ling Lu, Hongwei Xu, Hu i Ma, Yang Lu, Deli Bu, Yi Li. Distribution Network Reconfiguration Based on Collaborative Optimization Including Source-Load Uncertainty and Demand Response with Energy Storage Access[J]. Journal of Electrical Engineering & Technology, 2025, (prepublish):1-18.

Zhihong Liu, Juan Su, Songhuai Du, Jinli Wang, Wen Zhang. A Novel Cooperative Dynamic Optimization Method of Network Reconfiguration and DG Scheduling in Active Distribution Network[J]. Journal of Electrical Engineering & Technology, 2025, (prepublish): 1-14.

Rao Fu, Guofeng Xia, Sining Hu, Yuhao Zhang, Handaoyuan Li, Jiachuan Shi. Integrated Carbon Flow Tracing and Topology Reconfiguration for Low-Carbon Optimal Dispatch in DG-Embedded Distribution Networks[J]. Mathematics, 2025, 13(15): 2395-2395.

Xin He, BaoLai Zhang, HaoYang Jia, Kaixuan Xin, Youpeng Wang, Cheng Yu, BenXi Zhang. Regret-aware optimization of hydrogen-assisted congestion control in a renewable-dominated reconfigurable distribution network[J]. Renewable Energy, 2025, 250123084-123084.

Mahmoud Ghofrani. Sensitivity Analysis of Distribution Network Reconfiguration Optimization for Electric Vehicle and Renewable Distributed Generator Integration [J]. Energies, 2025, 18(8): 1903-1903.

Bishwajit Dey, Srikant Misra, Gulshan Sharma, Pitshou N. Bokoro. Cost-effective optimal scheduling of PHEV integrated microgrid with load curve restructuring strategies[J]. Discover Computing, 2025, 28(1): 26-26.

Farishta Rehman, Neeraj Gupta. Reconfiguration by Hybrid Genetic Spanning Tree Optimization for Optimal Power Distribution Network Performance [J]. Arabian Journal for Science and Engineering, 2025, (prepublish): 1-13.

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