The Short-Term Economical Operation Problem for Hydropower Station Using Chaotic Normal Cloud Model Based Discrete Shuffled Frog Leaping Algorithm

2020 ◽  
Vol 34 (3) ◽  
pp. 905-927
Author(s):  
Zhe Yang ◽  
Kan Yang ◽  
Lyuwen Su ◽  
Hu Hu
2018 ◽  
Vol 2018 ◽  
pp. 1-29
Author(s):  
Zhe Yang ◽  
Kan Yang ◽  
Lyuwen Su ◽  
Hu Hu

The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.


2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Haorui Liu ◽  
Fengyan Yi ◽  
Heli Yang

The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.


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