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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 19, issue 8
Hydrol. Earth Syst. Sci., 19, 3557–3570, 2015
https://doi.org/10.5194/hess-19-3557-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 19, 3557–3570, 2015
https://doi.org/10.5194/hess-19-3557-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Aug 2015

Research article | 12 Aug 2015

Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

J. Chu1, C. Zhang1, G. Fu2, Y. Li1, and H. Zhou1 J. Chu et al.
  • 1School of Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China
  • 2Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, North Park Road, Harrison Building, Exeter EX4 4QF, UK

Abstract. This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

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This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses a global sensitivity analysis method to screen out insensitive decision variables and thus forms simplified problems with a significantly reduced number of decision variables. We find that it is important to consider variable interactions when formulating simplified problems, and problem decomposition dramatically improves search efficiency and effectiveness.
This study investigates the effectiveness of a sensitivity-informed method for multi-objective...
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