Volume 12 Issue 4
Dec.  2019
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Jing-qiao Mao, Ming-ming Tian, Teng-fei Hu, Kang Ji, Ling-quan Dai, Hui-chao Dai. 2019: Shuffled complex evolution coupled with stochastic ranking for      reservoir scheduling problems. Water Science and Engineering, 12(4): 307-318. doi: 10.1016/j.wse.2019.12.003
Citation: Jing-qiao Mao, Ming-ming Tian, Teng-fei Hu, Kang Ji, Ling-quan Dai, Hui-chao Dai. 2019: Shuffled complex evolution coupled with stochastic ranking for      reservoir scheduling problems. Water Science and Engineering, 12(4): 307-318. doi: 10.1016/j.wse.2019.12.003

Shuffled complex evolution coupled with stochastic ranking for      reservoir scheduling problems

doi: 10.1016/j.wse.2019.12.003
Funds:  This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFC0401702), the Fundamental Research Funds for the Central Universities (Grant No. 2018B11214), and the National Natural Science Foundation of China (Grants No. 51379059 and 51579002).
More Information
  • Corresponding author: Jing-qiao Mao
  • Received Date: 2018-11-25
  • Rev Recd Date: 2019-09-26
  • This paper introduces an optimization method (SCE-SR) that combines shuffled complex evolution (SCE) and stochastic ranking (SR) to solve constrained reservoir scheduling problems, ranking individuals with both objectives and constrains considered. A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals. This method is suitable for handling multiple conflicting constraints, and is easy to implement, requiring little parameter tuning. The search properties of the method are ensured through the combination of deterministic and probabilistic approaches. The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system, and its performance is compared with that of two classical methods (the dynamic programming and genetic algorithm). The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly, with sufficient global convergence properties and robustness. The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.

     

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