Abstract
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.
Highlights
Reservoirs are often operated considering a number of conflicting objectives related to environmental, economic, and public services
This study investigates the effectiveness of a sensitivityinformed optimization method for the reservoir operation system (ROS) multi-objective optimization problems
The method uses a global sensitivity analysis method to screen out insensitive decision variables and forms simplified problems with a significantly reduced number of decision variables
Summary
Reservoirs are often operated considering a number of conflicting objectives (such as different water uses) related to environmental, economic, and public services. ISO uses deterministic optimization, e.g., dynamic programming, to determine a set of optimal releases based on the current reservoir storage and likely inflow scenarios (Young, 1967; Karamouz and Houck, 1982; Castelletti et al, 2012; François et al, 2014). Instead the use of likely inflow scenarios, ESO incorporates inflow probability directly into the optimization process, including stochastic dynamic programming and Bayesian methods (Huang et al, 1991; Tejada-Guibert et al, 1995; Powell, 2007; Goor et al, 2010; Xu et al, 2014). Many challenges remain in application of these two approaches due to their complexity and ability to deal with conflicting objectives
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