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Collaborative Optimization of Reactive Power and Reconfiguration of Active Distribution Network Based on Improved Grey Wolf Algorithm
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Xiao-meng WU, Meng-yi JING*, Xiao-xiao LI, Bo DANG
Science Technology and Engineering | 2025, 25(13) : 5447 - 5454
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Science Technology and Engineering | 2025, 25(13): 5447-5454
Papers·Electrical Technology
Collaborative Optimization of Reactive Power and Reconfiguration of Active Distribution Network Based on Improved Grey Wolf Algorithm
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Xiao-meng WU, Meng-yi JING*, Xiao-xiao LI, Bo DANG
Affiliations
  • Key Laboratory of Shaanxi Province for Gas-Oil Logging Technology, School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China
Published: 2025-05-08 doi: 10.12404/j.issn.1671-1815.2405006
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The reactive power optimization and reconfiguration of traditional distribution network are mostly studied separately, lacking the coordination and cooperation of different optimization techniques. A mathematical model of reactive power and reconfiguration collaborative optimization of active distribution network was established. Combined with the two optimization methods of reactive power optimization and reconfiguration of distribution network, the coordinated operation of the two was realized according to the actual situation of distribution network. Taking the minimum annual comprehensive cost as the objective function, the improved grey wolf algorithm was used to solve the problem under the constraints of network power balance, node voltage amplitude and network radial operation. Aiming at the problems of low population diversity, easy to fall into local optimal solution and slow running speed of traditional grey wolf algorithm, it is proposed to increase the explosion mechanism of fireworks algorithm on the basis of grey wolf update strategy. At the same time, in order to improve the computational efficiency and solution accuracy, the fireworks algorithm was used for integer solution optimization, and the nonlinear programming algorithm was introduced to optimize the continuous solution. The IEEE33 node distribution network was taken as an example to verify four different scenarios. The results show that the proposed collaborative optimization model can effectively reduce the network loss and annual comprehensive cost, suppress the node voltage fluctuation level, and show the superiority of the improved algorithm in convergence speed and calculation accuracy.

active distribution network  /  reactive power optimization  /  network reconstruction  /  grey wolf algorithm  /  fireworks algorithm  /  nonlinear programming algorithm
Xiao-meng WU, Meng-yi JING, Xiao-xiao LI, Bo DANG. Collaborative Optimization of Reactive Power and Reconfiguration of Active Distribution Network Based on Improved Grey Wolf Algorithm[J]. Science Technology and Engineering, 2025 , 25 (13) : 5447 -5454 . DOI: 10.12404/j.issn.1671-1815.2405006
Year 2025 volume 25 Issue 13
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Article Info
doi: 10.12404/j.issn.1671-1815.2405006
  • Receive Date:2024-07-04
  • Online Date:2025-07-09
  • Published:2025-05-08
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  • Received:2024-07-04
  • Revised:2025-02-08
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    Key Laboratory of Shaanxi Province for Gas-Oil Logging Technology, School of Electronic Engineering, Xi'an Shiyou University, Xi'an 710065, China
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表12种不同金属材料的力学参数

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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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