Education, Science, Technology, Innovation and Life
Open Access
Sign In

Application of Chaos Particle Swarm Optimization in Short-Term Optimal Scheduling of Reservoirs

Download as PDF

DOI: 10.23977/hyde.2022.020101 | Downloads: 7 | Views: 1724


Yan Jin 1, Lijun Luo 1, Yang Xiao 1, Kuidong He 1, Weibin Huang 2


1 Hydropower Industry Innovation Center, State Power Investment Corporation Limited, No. 188 Wuling Road, Tianxin District, Changsha City, Hunan Province, Changsha, China
2 College of Water Resource & Hydropower, Sichuan University, No. 24, South Section 1, First Ring Road, Chengdu City, Sichuan Province, Chengdu, China

Corresponding Author

Yan Jin


This paper combines particle swarm algorithm and chaos algorithm to solve the short-term optimal scheduling problem of reservoir. It takes advantage of the fast convergence velocity of the particle swarm optimization algorithm and the ergodicity and randomness of chaotic motion to modify the traditional particle swarm optimization algorithm, which gets rid of the shortcomings that particle swarm optimization algorithm easily falls into local extreme points in the later stage, while maintaining the search rapidity in the early stage. Through example calculation, the results show that the algorithm is obviously superior to the traditional particle swarm optimization algorithm in terms of convergence and stability, which is an effective search algorithm.


Hydropower station; short-term optimal scheduling; particle swarm algorithm; chaos search


Yan Jin, Lijun Luo, Yang Xiao, Kuidong He, Weibin Huang, Application of Chaos Particle Swarm Optimization in Short-Term Optimal Scheduling of Reservoirs. Advances in Hydraulic Engineering (2022) Vol. 2: 1-7. DOI:


[1]BASU M. Fuel constrained economic emission dispatch using nondominated sorting genetic algorithm-II. Energy, 2014, 78(4): 649-664.
[2]Abido M A. Multiobjective particle swarm optimization for environment economic dispatch problem.Electric Power Systems Research, 2009, 79(7): 1105-1113.
[3]Jin D, Chiang H,  Li P.Two-time scale multi-objective coordinated Volt/Var optimization for active distribution networks . IEEE Transactions on Power Systems, 2019, 34(6): 4418-4428.
[4]Basu M.Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch.International journal of electrical power & energy systems, 2013, 44(1): 591-596.
[5] ZHANG Ming, DING Yi, YUAN Xiaohui, LI Chengjun. Study of optimal generation scheduling in cascaded hydroelectric stations. Journal of Huazhong University of Science and Technology: Nature Science Edition, 2006, 36(4): 90-92.
[6] Christiano Lyra, Luiz Roberto. A multiobjective approach to the short-term scheduling of a hydroelectric oower system .IEEE Transon PAS, 1995, 10(4): 1750-1754.
[7] WU Zhengyi, YANG Jie, ZOU Jianguo. Study of economic operation for wujiang river cascade hydropower stations. Automation of Hydropower Plants, 2005(1): 109-113.
[8] ZHANG Shuanghu, HUANG Qiang, SUN Tingrong. Study on the optimal operation of hydropower station based on parallel recombination simulated annealing algorithms. Journal of Hydroelectric Engineering, 2004, 8, 23(4): 16.
[9] MA Guangwen, WANG Li. Hydro power optimization in competition with thermal generation. Chengdu: Sichuan Publishing House of Science and Technology, 2003, 12.
[10] LI Wei, LI Qiqiang. Survey on particle swarm optimization algorithm. Engineering Science, 2004, 5, 6(5): 87.
[11] LU Kan. Chaotic dynamics. Shanghai, Shanghai Translation Pbulishing House, 1990.
[12] LI Zhensu, HOU Zhirong. Particle swarm optimization with adaptive mutation. Acta Electronica Sinica, 2004, 32(3): 416-420.

Downloads: 32
Visits: 8332

All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.