Decoupling algorithms for unit commitment based on modification of particle swarm optimizatio

被引:0
|
作者
Wang, Nan [1 ]
Zhang, Li-Zi [1 ]
Shu, Jun [1 ]
机构
[1] School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China
来源
关键词
Optimal systems - Lagrange multipliers - Scheduling - Uranium compounds - Particle swarm optimization (PSO);
D O I
暂无
中图分类号
学科分类号
摘要
Due to the difficulty of achieving optimal solution theoretically, unit commitment (UC) is a hard task in optimal operation of power system. For this reason, in this paper a decoupling algorithm based on the modification of particle swarm optimization (PSO) is proposed. Firstly, by use of Lagrangian relaxation algorithm based on aggregative projection sub-gradient the dual solutions of UC is obtained; then according to the reserve multiplier and dual combination in dual information, the optimal space of particle swam is built; and then by use of unrestricted standard PSO algorithm the local updating of Lagrangian multiplier is implemented, by means of adjusting particles and the information transmission among particles the UC is changed, after that the Lagrangian dual solution is modified and finally the approximate optimal solution of UC is obtained. The solving speed and calculation accuracy of the proposed method are verified by simulation results of six test systems.
引用
收藏
页码:79 / 83
相关论文
共 50 条
  • [21] Solving Unit Commitment and Security Problems by Particle Swarm Optimization Technique
    Borra, Venkata Silpa
    Debnath, K.
    2019 IEEE PES GTD GRAND INTERNATIONAL CONFERENCE AND EXPOSITION ASIA (GTD ASIA), 2019, : 740 - 745
  • [22] Solving Unit Commitment problem using Hybrid Particle Swarm Optimization
    Ting, TO
    Rao, MVC
    Loo, CK
    Ngu, SS
    JOURNAL OF HEURISTICS, 2003, 9 (06) : 507 - 520
  • [23] Unit commitment problem using enhanced particle swarm optimization algorithm
    Xiaohui Yuan
    Anjun Su
    Hao Nie
    Yanbin Yuan
    Liang Wang
    Soft Computing, 2011, 15 : 139 - 148
  • [24] An improved particle swarm optimization algorithm for power system unit commitment
    Zhao, Bo
    Cao, Yi-Jia
    Power System Technology, 2004, 28 (21) : 6 - 10
  • [25] Solving the Unit Commitment Problem with Improving Binary Particle Swarm Optimization
    Liu, Jianhua
    Wang, Zihang
    Chen, Yuxiang
    Zhu, Jian
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2022, PT I, 2022, : 176 - 189
  • [26] Solving Unit Commitment Problem Using Hybrid Particle Swarm Optimization
    Tiew-On Ting
    M.V.C. Rao
    C.K. Loo
    S.S. Ngu
    Journal of Heuristics, 2003, 9 : 507 - 520
  • [27] Unit commitment problem using enhanced particle swarm optimization algorithm
    Yuan, Xiaohui
    Su, Anjun
    Nie, Hao
    Yuan, Yanbin
    Wang, Liang
    SOFT COMPUTING, 2011, 15 (01) : 139 - 148
  • [28] Unit Commitment with Vehicle-to-Grid using Particle Swarm Optimization
    Saber, Ahmed Yousuf
    Venayagamoorthy, Ganesh Kumar
    2009 IEEE BUCHAREST POWERTECH, VOLS 1-5, 2009, : 1091 - 1098
  • [29] Swarm Reinforcement Learning Algorithms Based on Particle Swarm Optimization
    Iima, Hitoshi
    Kuroe, Yasuaki
    2008 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC), VOLS 1-6, 2008, : 1109 - 1114
  • [30] Priority list and particle swarm optimization based Unit commitment of thermal units including renewable uncertainties
    Alam, Md. Sajid
    Kiran, Durga Hari B.
    Kumari, Matam Sailaja
    2016 IEEE INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON), 2016,