Abstract
This study aims to propose a methodology for establishing the optimal rule curves of reservoir operation based on a multi-use reservoir system. Located on the upper Saigon River, Dau Tieng Reservoir plays an important role in economic and social aspects: (1) flood control; (2) domestic and industrial demands; (3) flushing out salt water intrusion from the downstream area; and (4) agriculture irrigation. We propose a reservoir operation model using a constrained genetic algorithm (CGA), in which the fitness function was constrained by penalty functions. The proposed model was formulated by including various water demands configured into the objective function. The penalty functions were designed for various constraints and integrated into the objectives of the operation process to perform the fitness function. The model’s performance was simulated for the last 20 years with 1-month intervals and evaluated through a generalized shortage index (GSI). The derived results of three CGA cases with associated environmental flow requirements significantly improved the efficiency and effectiveness of water supply capability to various water demands as compared to current operation. Among the three cases, CGA case 3 achieved much better water releases from the reservoir as indicated by a small derived GSI value (0.33), the smallest shortage of environmental water (0.11 m3/s) and the highest water usage (63.8 %). Thus, the derived results of CGA case 3 were presented as the best rule curves for reservoir operation. To summarize, CGA was demonstrated as an effective and powerful tool for optimal strategy searching for multi-use reservoir operations.
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Acknowledgments
The authors greatly appreciate the support under FY2012-2014 JSPS Core-to-Core Program “Collaborative Project for Soil and Water Conservation in Southeast Asian Watersheds” and FY2011-2015 JSPS Grant-in-Aid for Scientific Research (B) (Project number: 23380144).
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Ngoc, T.A., Hiramatsu, K. & Harada, M. Optimizing the rule curves of multi-use reservoir operation using a genetic algorithm with a penalty strategy. Paddy Water Environ 12, 125–137 (2014). https://doi.org/10.1007/s10333-013-0366-2
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DOI: https://doi.org/10.1007/s10333-013-0366-2