Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm

被引:29
|
作者
Fang, Ruiming [1 ]
Popole, Zouthi [1 ]
机构
[1] Huaqiao Univ, Dept Elect Engn, Xiamen 362021, Peoples R China
关键词
Hydropower reservoir; Multi-objective optimized scheduling; Improved particle swarm optimization algorithm; Ecological protection; GENETIC ALGORITHM; OPERATION; DIVERSITY;
D O I
10.1007/s11356-019-04434-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In order to make hydropower station's development and operation harmonious with ecological protection, the optimal operation of hydropower stations to meet the needs of ecological protection is studied. Firstly, the ecological protection function of river course is defined according to the minimum ecological runoff and suitable ecological runoff. Then, a multi-objective optimal running model of reservoir which can maximize the capacity of ecological protection and generation is proposed. Finally, an improved multi-objective particle swarm optimization algorithm (MOPSO), which can construct a neighborhood for each particle and choose the neighborhood optimal solution by adopting self-organizing mapping (SOM) method, is proposed to solve the model. The model is applied to the Shui-Kou Hydropower Station in Minjiang, China. The results show that the model can get the optimal schedule with balanced consideration of ecological benefits and power generation benefits, which has not a great impact on the economic benefits of reservoirs while achieving the goal of ecological environment. The research results can provide theoretical basis and concrete scheme reference for reservoir operation.
引用
收藏
页码:12842 / 12850
页数:9
相关论文
共 50 条
  • [1] Multi-objective optimized scheduling model for hydropower reservoir based on improved particle swarm optimization algorithm
    Ruiming Fang
    Zouthi Popole
    [J]. Environmental Science and Pollution Research, 2020, 27 : 12842 - 12850
  • [2] Optimization of Hydropower Unit Startup Process Based on the Improved Multi-Objective Particle Swarm Optimization Algorithm
    Zhang, Qingquan
    Xie, Zifeng
    Lu, Mingming
    Ji, Shengyang
    Liu, Dong
    Xiao, Zhihuai
    [J]. ENERGIES, 2024, 17 (17)
  • [3] Multi-objective based Cloud Task Scheduling Model with Improved Particle Swarm Optimization
    Udatha, Chaitanya
    Lakshmeeswari, Gondi
    [J]. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2021, 12 (12) : 243 - 248
  • [4] An improved multi-objective particle swarm optimization algorithm
    Zhang, Qiuming
    Xue, Siqing
    [J]. ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2007, 4683 : 372 - +
  • [5] Improved multi-objective particle swarm optimization algorithm
    College of Automation, Northwestern Polytechnical University, Xi'an 710129, China
    不详
    [J]. Liu, B. (lbn1987113@163.com), 2013, Beijing University of Aeronautics and Astronautics (BUAA) (39):
  • [6] An improved multi-objective cultural algorithm based on particle swarm optimization
    Wu, Ya-Li
    Xu, Li-Qing
    [J]. Kongzhi yu Juece/Control and Decision, 2012, 27 (08): : 1127 - 1132
  • [7] Multi-Objective Optimal Scheduling of Microgrids Based on Improved Particle Swarm Algorithm
    Guan, Zhong
    Wang, Hui
    Li, Zhi
    Luo, Xiaohu
    Yang, Xi
    Fang, Jugang
    Zhao, Qiang
    [J]. ENERGIES, 2024, 17 (07)
  • [8] An improved multi-objective particle swarm optimization algorithm and its application in vehicle scheduling
    Xu, Wenxing
    Wang, Wanhong
    He, Qian
    Liu, Cai
    Zhuang, Jun
    [J]. 2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 4230 - 4235
  • [9] Optimal Scheduling of Microgrid Based on Multi-objective Particle Swarm Optimization Algorithm
    Yang, Di
    [J]. 2023 6TH INTERNATIONAL CONFERENCE ON RENEWABLE ENERGY AND POWER ENGINEERING, REPE 2023, 2023, : 191 - 195
  • [10] An Improved Hybrid Multi-objective Particle Swarm Optimization Algorithm
    Zhou, Zuan
    Dai, Guangming
    Fang, Pan
    Chen, Fangjie
    Tan, Yi
    [J]. ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2008, 5370 : 181 - 188