Application of PSO algorithm in short-term optimization of reservoir operation

被引:0
|
作者
Kazem SaberChenari
Hirad Abghari
Hossein Tabari
机构
[1] Gorgan University of Agricultural Sciences & Natural Resources,Department of Watershed Management Engineering, Faculty of Natural Resources
[2] Urmia University,Department of Range and Watershed Management, Faculty of Natural Resources
[3] KU Leuven,Hydraulics Division, Department of Civil Engineering
来源
Environmental Monitoring and Assessment | 2016年 / 188卷
关键词
Reservoir operation; Particle swarm optimization; Mahabad dam; Iran; PSO model;
D O I
暂无
中图分类号
学科分类号
摘要
The optimization of the operation of existing water systems such as dams is very important for water resource planning and management especially in arid and semi-arid lands. Due to budget and operational water resource limitations and environmental problems, the operation optimization is gradually replaced by new systems. The operation optimization of water systems is a complex, nonlinear, multi-constraint, and multidimensional problem that needs robust techniques. In this article, the practical swarm optimization (PSO) was adopted for solving the operation problem of multipurpose Mahabad reservoir dam in the northwest of Iran. The desired result or target function is to minimize the difference between downstream monthly demand and release. The method was applied with considering the reduction probabilities of inflow for the four scenarios of normal and drought conditions. The results showed that in most of the scenarios for normal and drought conditions, released water obtained by the PSO model was equal to downstream demand and also, the reservoir volume was reducing for the probabilities of inflow. The PSO model revealed a good performance to minimize the reservoir water loss, and this operation policy can be an appropriate policy in the drought condition for the reservoir.
引用
收藏
相关论文
共 50 条
  • [21] Study on optimization of the short-term operation of cascade hydropower stations by considering output error
    Wang, Liping
    Wang, Boquan
    Zhang, Pu
    Liu, Minghao
    Li, Chuangang
    JOURNAL OF HYDROLOGY, 2017, 549 : 326 - 339
  • [22] An improved PSO technique for short-term optimal hydrothermal scheduling
    Hota, P. K.
    Barisal, A. K.
    Chakrabarti, R.
    ELECTRIC POWER SYSTEMS RESEARCH, 2009, 79 (07) : 1047 - 1053
  • [23] Renewable energy incorporating short-term optimal operation using oppositional grasshopper optimization
    Hazra, Sunanda
    Kumar Roy, Provas
    OPTIMAL CONTROL APPLICATIONS & METHODS, 2023, 44 (02) : 452 - 479
  • [24] Application of a modified PSO algorithm in PID controller optimization
    Jiang Shi-cheng
    Xu Wen-bo
    2011 TENTH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING AND APPLICATIONS TO BUSINESS, ENGINEERING AND SCIENCE (DCABES), 2011, : 343 - 346
  • [25] On the optimization of electromagnetic geophysical data: Application of the PSO algorithm
    Godio, A.
    Santilano, A.
    JOURNAL OF APPLIED GEOPHYSICS, 2018, 148 : 163 - 174
  • [26] Model of Flood Control Operation of Reservoir Based on Particle Swarm Optimization Algorithm and Its Application
    Peng Yong
    Ye Suigao
    Zhou Hui-cheng
    Kang Hai-gui
    2011 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), VOLS 1-4, 2012, : 368 - 371
  • [27] Application of Intelligent Water Drops Algorithm in Reservoir Operation
    Dariane, A. B.
    Sarani, S.
    WATER RESOURCES MANAGEMENT, 2013, 27 (14) : 4827 - 4843
  • [28] Application of Intelligent Water Drops Algorithm in Reservoir Operation
    A. B. Dariane
    S. Sarani
    Water Resources Management, 2013, 27 : 4827 - 4843
  • [29] Energy consumption optimization of tramway operation based on improved PSO algorithm
    Xing, Zongyi
    Zhu, Junlin
    Zhang, Zhenyu
    Qin, Yong
    Jia, Limin
    ENERGY, 2022, 258
  • [30] Gradient Evolution Optimization Algorithm to Optimize Reservoir Operation Systems
    Samadi-koucheksaraee, Arvin
    Ahmadianfar, Iman
    Bozorg-Haddad, Omid
    Asghari-pari, Seyed Amin
    WATER RESOURCES MANAGEMENT, 2019, 33 (02) : 603 - 625