Unit commitment by an improved binary quantum GSA

被引:9
|
作者
Barani, Fatemeh [1 ]
Mirhosseini, Mina [1 ]
Nezamabadi-pour, Hossein [2 ]
Farsangi, Malihe M. [2 ]
机构
[1] Higher Educ Complex Bam, Dept Comp Sci, Bam, Iran
[2] Shahid Bahonar Univ Kerman, Dept Elect Engn, Kerman, Iran
关键词
Unit commitment problem; Economic dispatch; Quantum computing; Gravitational search algorithm; GRAVITATIONAL SEARCH ALGORITHM; LAGRANGIAN-RELAXATION; GENETIC ALGORITHM; OPTIMIZATION;
D O I
10.1016/j.asoc.2017.06.051
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Unit commitment (UC) problem is an important optimizing task for scheduling the on/off states of generating units in power system operation over a time horizon such that the power generation cost is minimized. Since, increasing the number of generating units makes it difficult to solve in practice, many approaches have been introduced to solve the UC problem. This paper introduces an improved version of the binary quantum-inspired gravitational search algorithm (BQIGSA) and proposes a new approach to solve the UC problem based on the improved BQIGSA, called QGSA-UC. The proposed approach is applied to unit commitment problems with the number of generating units in the range of 10120 along with 24-h scheduling horizon and is compared with nine state-of-the-art approaches. Furthermore, four different versions of gravitational approach are implemented for solving the UC problem and compared with those obtained by QGSA-UC. Comparative results clearly reveal the effectiveness of the proposed approach and show that it can be used as a reliable tool to solve UC problem. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:180 / 189
页数:10
相关论文
共 50 条
  • [41] Binary fireworks algorithm for profit based unit commitment (PBUC) problem
    Reddy, K. Srikanth
    Panwar, Lokesh Kumar
    Kumar, Rajesh
    Panigrahi, B. K.
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2016, 83 : 270 - 282
  • [42] A binary-real-coded differential evolution for unit commitment problem
    Datta, Dilip
    Dutta, Saptarshi
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2012, 42 (01) : 517 - 524
  • [43] 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
  • [44] Binary real coded firefly algorithm for solving unit commitment problem
    Chandrasekaran, K.
    Simon, Sishaj P.
    Padhy, Narayana Prasad
    INFORMATION SCIENCES, 2013, 249 : 67 - 84
  • [45] Binary Grey Wolf Optimizer for large scale unit commitment problem
    Panwar, Lokesh Kumar
    Reddy, Srikanth K.
    Verma, Ashu
    Panigrahi, B. K.
    Kumar, Rajesh
    SWARM AND EVOLUTIONARY COMPUTATION, 2018, 38 : 251 - 266
  • [46] Modified Binary Differential Evolution Algorithm to Solve Unit Commitment Problem
    Dhaliwal, Jatinder Singh
    Dhillon, Jaspreet Singh
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2018, 46 (08) : 900 - 918
  • [47] A Binary Differential Evolution Algorithm For Transmission And Voltage Constrained Unit Commitment
    Patra, S.
    Goswami, S. K.
    Goswami, B.
    2008 JOINT INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY (POWERCON) AND IEEE POWER INDIA CONFERENCE, VOLS 1 AND 2, 2008, : 155 - +
  • [48] Binary Bat Search Algorithm for Unit Commitment Problem in Power system
    Nidhi
    Reddy, Srikanth
    Kumar, Rajesh
    Panigrahi, B. K.
    2017 IEEE INTERNATIONAL WIE CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (IEEE WIECON-ECE 2017), 2017, : 121 - 124
  • [49] Binary whale optimization algorithm and its application to unit commitment problem
    Vijay Kumar
    Dinesh Kumar
    Neural Computing and Applications, 2020, 32 : 2095 - 2123
  • [50] An improved particle swarm optimization approach for unit commitment problem
    Guo, Yiran
    Zhang, Jingrui
    Fang, Zheng
    Open Automation and Control Systems Journal, 2014, 6 (01): : 629 - 636