Optimization of single and double supply and distribution networks based on ant colony algorithm and genetic algorithm
Authors: 
Congrui Bai, Wanqi Hu, Siyuan Cheng, Tong WeiAuthors Info & Claims
Pages 765 - 769
Abstract
In order to improve the reliability and economy of power distribution networks, this paper aims to reduce construction costs and improve reliability by developing an optimization model for the total construction cost of single and double supply and distribution networks based on information such as the switch setting principles. Ant colony algorithm and Prim algorithm are applied to solve the topology of the single supply and distribution network, and genetic algorithm is used to solve the optimization model for the sum of the main line lengths of the two single supply and distribution networks, to establish the optimization model for the minimum electricity reliability and total construction cost of the double supply and distribution network, and to design the location and length of the contact lines using genetic algorithm to provide an optimization solution for the construction of the double supply and distribution network. The results show that the minimum cost of constructing a single supply and distribution network is RMB 52,729,000, and the reliability of electricity for each customer is above 78%, with most customers having a reliability of between 70% and 80%. The minimum construction costs for the two single supply and distribution networks are RMB 50.207 million and RMB 59.264 million respectively, and the reliability of electricity for most customers in the two single supply and distribution networks is around 0.8.
References
[1]
Chen YD, Liu YB, Shen XD, A review of edge intelligence technologies for distributed resources in urban energy systems [J]. Power System Automation, 2022: 1-11.
[2]
Li Tiecheng, Guo Shaofei, Wang Xianzhi, The principle of "petal" type distribution network protection based on voltage prediction error [J]. Journal of Power Systems and Automation, 2022, 34(6): 10.
[3]
Dorigo M. Ant colony optimization / Marco Dorigo, Thomas Stützle. 2004.
[4]
Fukushima K, Miyake S, Ito T. Neocognitron: A neural network model for a mechanism of visual pattern recognition [J]. Systems Man & Cybernetics IEEE Transactions on, 1982, SMC-13(5):826-834.
[5]
Polytechnique D L, Cnrs M S. Evolutionary Computation and Applications at Centre de Math'ematiques Appliqu'ees de l'Ecole Polytechnique Marc Schoenauer – CNRS. 1997.
[6]
Alpern B. Edsger W. Dijkstra [J]. ACM SIGACT News, 2018, 49(4):64-65.
[7]
Kruskal J. The Relationship between Multidimensional Scaling and Clustering - ScienceDirect[J]. Classification and Clustering, 1977:17-44.
[8]
Jarník, Vojtěch. On linear inhomogeneous Diophantine approximations. [J]. Rozpravy II Tídy Eské Akad, 1941(29).
[9]
Franck, Lebrin, Marie-José, Endoglin promotes endothelial cell proliferation and TGF-β/ALK1 signal transduction [J]. Embo Journal, 2004.
[10]
Fogel L J, Back T, Angeline P J . Evolutionary programming V : proceedings of the Fifth Annual Conference on Evolutionary Programming[M]. MIT Press, 1996.
[11]
Rechenberg I . Case studies in evolutionary experimentation and computation[J]. Computer Methods in Applied Mechanics & Engineering, 2000, 186(2/4):125-140.
[12]
Deb K, Reddy A R, Singh G . Optimal Scheduling of Casting Sequence Using Genetic Algorithms Kalyanmoy Deb, Abbadi Raji Reddy, and Gulshan Singh. 2002.
[13]
Zhang Changyong, Han Liang. Lidar obstacle detection based on optimized DBSCAN [J]. Advances in Laser and Optoelectronics, 2022, 59(12): 516-524.
[14]
Wan C.I., Hu Y., Chen B.S., A multi-source partial discharge classification and identification method based on energy spectrum analysis and DBSCAN improvement algorithm [J]. High Voltage Technology, 2022: 1-11.
[15]
Wu G, Cao L, Tian H, HY-DBSCAN: A hybrid parallel DBSCAN clustering algorithm scalable on distributed-memory computers [J]. Journal of Parallel and Distributed Computing, 2022, 168: 57-69.
[16]
Shi YJ. Robot path planning based on improved ant colony algorithm and improved informed-RRT* algorithm [D]. Jilin University, 2022.
[17]
Wei Xiaomin, Peng Chanhua. A Prim-based credibility model for IoT security data fusion [J]. Computer Simulation, 2022, 39(06): 421-424+443.
[18]
Alblooshi E, Alblooshi A, Poon K, A GA Based Approach for Solving Ring Design Telecommunication Network [C]//GECCO '22. Proceedings of the Genetic and Evolutionary Computation Conference Companion. Boston, Massachusetts: Association for Computing Ma- chinery, 2022. 415-418.
[19]
Liu, Chang, Liu, Wenxia, Gao, Xueqian, Master-slave game-based coordinated planning for distribution network-multi-integrated energy systems [J]. Power Automation Equipment, 2022, 42(06): 45-52.
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- Optimization of single and double supply and distribution networks based on ant colony algorithm and genetic algorithm
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Published In
May 2023
1025 pages
ISBN:9798400700705
DOI:10.1145/3603781
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Published: 27 July 2023
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CNIOT'23
CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things
May 26 - 28, 2023
Xiamen, China
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