Water Science and Engineering 2019, 12(4) 307-318 DOI:   https://doi.org/10.1016/j.wse.2019.12.003  ISSN: 1674-2370 CN: 32-1785/TV

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Keywords
Reservoir scheduling
Optimization method
Constraint handling
Shuffled complex evolution
Stochastic ranking
Authors
PubMed

Shuffled complex evolution coupled with stochastic ranking for      reservoir scheduling problems

Jing-qiao Mao a, *, Ming-ming Tian a, Teng-fei Hu a, Kang Ji b, Ling-quan Dai c, Hui-chao Dai a

a College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
b Ningbo Hongtai Water Conservancy Information Technology Co., Ltd., Ningbo 315040, China
c College of Hydraulic and Environmental Engineering, Three Gorges University, Yichang 443002, China

Abstract

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.

Keywords Reservoir scheduling   Optimization method   Constraint handling   Shuffled complex evolution   Stochastic ranking  
Received 2018-11-25 Revised 2019-09-26 Online: 2019-12-30 
DOI: https://doi.org/10.1016/j.wse.2019.12.003
Fund:

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).

Corresponding Authors: Jing-qiao Mao
Email: maojq@hhu.edu.cn
About author:

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