Multi-Objective Optimization and Reconstruction of Distribution Networks with Distributed Power Sources Based on an Improved BPSO Algorithm

Dan Lu, Wenfeng Li (), Linjuan Zhang, Qiang Fu, Qingtao Jiao and Kai Wang
Additional contact information
Dan Lu: State Grid Henan Economic Research Institute, Zhengzhou 450000, China
Wenfeng Li: State Grid Henan Economic Research Institute, Zhengzhou 450000, China
Linjuan Zhang: State Grid Henan Economic Research Institute, Zhengzhou 450000, China
Qiang Fu: NARI-TECH Nanjing Control Systems Co., Ltd., Nanjing 210000, China
Qingtao Jiao: NARI-TECH Nanjing Control Systems Co., Ltd., Nanjing 210000, China
Kai Wang: NARI-TECH Nanjing Control Systems Co., Ltd., Nanjing 210000, China

Energies, 2024, vol. 17, issue 19, 1-12

Abstract: The continuous integration of distributed power into the distribution network has increased the complexity of the distribution network and created challenges in distribution-network reconfiguration. In order to make the distribution network operate in the optimal mode, this paper establishes a multi-objective reconfiguration-optimization model that takes into account active network loss, voltage offset, number of switching actions and distributed power output. For a distribution network with a distributed power supply, it is easy for the traditional binary particle swarm optimization algorithm to fall into a local optimum. In order to improve the convergence speed of the algorithm and avoid premature convergence, this paper adopts an improved binary particle swarm optimization algorithm to solve the problem. The IEEE33 node system is used as an example for simulation verification. The experimental results show that the algorithm improves the convergence speed and global search ability, effectively reduces the system network loss, and greatly improves the voltage level of each node. It improves the stability and economy of distribution-network operation and can effectively solve the problem of multi-objective reconfiguration.

Keywords: binary particle swarm optimization algorithm; multi-objective reconstruction; distributed power supply; genetic algorithm (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: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/19/4877/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/19/4877/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:19:p:4877-:d:1488225

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().