Active distribution network reconfiguration based on single loop optimization strategy

被引:0
|
作者
Yu A. [1 ]
Ding L. [1 ]
Wang Y. [1 ]
Li H. [1 ]
机构
[1] College of Power Engineering, Shanghai University of Electric Power, Shanghai
基金
中国国家自然科学基金;
关键词
Active distribution network reconfiguration; Distribution network topology; Improving the algorithm; Optimizing the solution space; Single loop optimization strategy;
D O I
10.19783/j.cnki.pspc.210323
中图分类号
学科分类号
摘要
DG access in a distribution network leads to an intelligent optimization algorithm which can easily fall into local convergence and low optimization rate in the reconstruction. Considering the relationship between the distribution network topological characteristics and the algorithm, an active distribution network reconstruction method based on single loop optimization strategy is proposed by deeply combining the two. First, Levy flight is introduced into the quantum particle swarm optimization algorithm to establish the Levy coefficient quantum particle swarm optimization (LCQPSO) algorithm. Secondly, the adaptive ordered ring matrix based on node voltage in a ring network is proposed as the solution space of the algorithm. Finally, from the corresponding relationship between the distribution network topology and the global optimal solution of the algorithm, a single loop optimization strategy is established. This is dynamically combined with the algorithm. By improving the algorithm, optimizing the solution space, and combining the distribution network topology with the algorithm depth, the global search ability and optimization efficiency of the algorithm is improved. The effectiveness and applicability of the proposed algorithm and strategy are verified by simulation analysis of several power systems. © 2022 Power System Protection and Control Press.
引用
收藏
页码:23 / 32
页数:9
相关论文
共 25 条
  • [1] LI Chao, MIAO Shihong, SHENG Wanxing, Et al., Optimization operation strategy of active distribution network considering dynamic network reconfiguration, Transactions of China Electrotechnical Society, 34, 18, pp. 3909-3919, (2019)
  • [2] GUO Qingyuan, MO Chao, WU Jiekang, Et al., Multi-type reactive power output optimization method of distribution system with distributed generations, Electric Power Engineering Technology, 39, 5, pp. 211-219, (2020)
  • [3] ZHANG Delong, LI Jianlin, HUI Dong, Coordinated control for voltage regulation of distribution network voltage regulation by distributed energy storage systems, Protection and Control of Modern Power Systems, 3, 3, pp. 35-42, (2018)
  • [4] LI Ruisheng, WONG Peter, WANG Kun, Et al., Power quality enhancement and engineering application with high permeability distributed photovoltaic access to low-voltage distribution networks in Australia, Protection and Control of Modern Power Systems, 5, 3, pp. 1-7, (2020)
  • [5] HUANG He, ZHU Lei, GAO Song, Et al., Reconfiguration method of distribution system for increasing the penetration of distributed generation, Journal of Electric Power Science and Technology, 34, 3, pp. 166-172, (2019)
  • [6] TAMI Y, SEBAA K, LAHDEB M, Et al., Mixed-integer quadratic constrained programming versus quadratic programming methods for distribution network reconfiguration, 2019 International Conference on Advanced Electrical Engineering (ICAEE), pp. 1-5, (2019)
  • [7] CHEN Yongjin, A coordinated optimization for active distribution network reconfiguration and volt/var optimization based on mixed integer convex programming, Power Capacitor & Reactive Power Compensation, 41, 6, pp. 21-29, (2020)
  • [8] SHAO Hua, HE Chunguang, AN Jiakun, Et al., Active distribution network planning model based on linearized constraints, Journal of Electric Power Science and Technology, 35, 5, pp. 66-74, (2020)
  • [9] DONG Jiadu, HUANG Qing, HUANG Yanquan, Et al., The power moment method for radial distribution network reconfiguration, Power System Protection and Control, 38, 6, pp. 22-25, (2010)
  • [10] TIAN Shuxin, LIU Lang, WEI Shurong, Et al., Dynamic reconfiguration of a distribution network based on an improved grey Wolf optimization algorithm, Power System Protection and Control, 49, 16, pp. 1-11, (2021)