OPTIMAL UTILIZATION OF ELECTRICAL ENERGY FROM POWER PLANTS BASED ON FINAL ENERGY CONSUMPTION USING GRAVITATIONAL SEARCH ALGORITHM

被引:27
作者
Montazeri, Z. [1 ]
Niknam, T. [1 ]
机构
[1] Islamic Azad Univ Marvdasht, Dept Elect Engn, Marvdasht, Iran
关键词
gravitational search algorithm; energy; electrical energy; economic distribution; final energy consumption;
D O I
10.20998/2074-272X.2018.4.12
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Purpose. Energy consumption is a standard measure to evaluate the progress and quality of life in a country. When used properly and logically it could be cause of progress in science, technology and welfare of the people in any country and otherwise irreparable economic losses and economic gross recession would happen. And finally, the quantity of energy consumption per GDP will increase day by day. Electrical energy, as the most prominent type of energy, is very important. In this article based on a different approach, according to the final consumption of electric energy, a proper economic planning in order to supply electrical energy is submitted. In this programming, the details of final energy consumption, will replace with the information of power network, by considering the network efficiency and power plants. Operation of power plants is based on the energy optimization entranced to a plant. By using the proposed method and gravitational search algorithm, the total cost of electrical energy can be minimized. The results of simulation and numerical studies show better convergence of gravitational search algorithm in comparison with other existing methods in this area. References 17, tables 2, figures 4.
引用
收藏
页码:70 / 73
页数:4
相关论文
共 17 条
  • [1] Coello CAC, 2003, LECT NOTES COMPUT SC, V2606, P398
  • [2] Theoretical study of Cu-Au nanoalloy clusters using a genetic algorithm
    Darby, S
    Mortimer-Jones, TV
    Johnston, RL
    Roberts, C
    [J]. JOURNAL OF CHEMICAL PHYSICS, 2002, 116 (04) : 1536 - 1550
  • [3] Ant system: Optimization by a colony of cooperating agents
    Dorigo, M
    Maniezzo, V
    Colorni, A
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01): : 29 - 41
  • [4] THE IMMUNE-SYSTEM, ADAPTATION, AND MACHINE LEARNING
    FARMER, JD
    PACKARD, NH
    PERELSON, AS
    [J]. PHYSICA D-NONLINEAR PHENOMENA, 1986, 22 (1-3) : 187 - 204
  • [5] Kennedy J., 1995, 1995 IEEE International Conference on Neural Networks Proceedings (Cat. No.95CH35828), P1942, DOI 10.1109/ICNN.1995.488968
  • [6] OPTIMIZATION BY SIMULATED ANNEALING
    KIRKPATRICK, S
    GELATT, CD
    VECCHI, MP
    [J]. SCIENCE, 1983, 220 (4598) : 671 - 680
  • [7] Kuhn H, 1951, NONLINEAR PROGRAMMIN, P481
  • [8] Multi-objective evolutionary biclustering of gene expression data
    Mitra, Sushmita
    Banka, Haider
    [J]. PATTERN RECOGNITION, 2006, 39 (12) : 2464 - 2477
  • [9] GSA: A Gravitational Search Algorithm
    Rashedi, Esmat
    Nezamabadi-Pour, Hossein
    Saryazdi, Saeid
    [J]. INFORMATION SCIENCES, 2009, 179 (13) : 2232 - 2248
  • [10] Real-Time Economic Dispatch Considering Renewable Power Generation Variability and Uncertainty Over Scheduling Period
    Reddy, S. Surender
    Bijwe, P. R.
    Abhyankar, A. R.
    [J]. IEEE SYSTEMS JOURNAL, 2015, 9 (04): : 1440 - 1451