Optimizing the reservoir operating rule curves by genetic algorithms

Genetic algorithms, founded upon the principle of evolution, are applicable to many optimization problems, especially popular for solving parameter optimization problems. Reservoir operating rule curves are the most common way for guiding and managing the reservoir operation. These rule curves traditionally are derived through intensive simulation techniques. The main aim of this study is to investigate the efficiency and effectiveness of two genetic algorithms (GAs), i.e., binary coded and real coded, to derive multipurpose reservoir operating rule curves. The curves are assumed to be piecewise linear functions where the coordinates of their inflection points are the unknowns and we want to optimize system performance. The applicability and effectiveness of the proposed methods are tested on the operation of the Shih-Men reservoir in Taiwan. The current M-5 operating curves of the Shih-Men reservoir are also evaluated. The results show that the GAs provide an adequate, effective and robust way for searching the rule curves. Both sets of operating rule curves obtained from GAs have better performance, in terms of water release deficit and hydropower, than the current M-5 operating rule curves, while the real-coded GA is more efficient than the binary-coded GA. Copyright © 2005 John Wiley & Sons, Ltd.