Abstract
This study first compares how different formulations of a reservoir operation problem with conflicting objectives affect the quality of the generated solution set. Six models were developed for comparative analysis: three using dynamic programming and three using the evolutionary multi-objective direct policy search (EMODPS) algorithm. Subsequently, to improve the quality of the generated solution set, an EMODPS model was selected and coupled with a zone-based hedging policy that has been currently applied in real-world reservoir operations. The proposed methodology was applied to a multipurpose reservoir in South Korea. Among the different models, the EMODPS-Gaussian model with three parameters outperformed dynamic programming models. Moreover, coupling the status quo zone-based hedging rule with the optimization model improved the average duration of failure from the supply side by 37.4%. On the other hand, when measured from the demand side, the results indicated that the magnitude of failure also improved by 4.15% at the cost of frequency of water deficit. The overall results of this study suggest that the integrative use of optimization models with hedging rules is potentially applicable in future drought mitigation measures.
Highlights
Reservoirs have been widely adopted as the principal source of freshwater for various uses including irrigation, municipal, industrial, hydropower generation, and flood mitigation (Ahmad et al, 2014)
For comparative analysis of their performance, the solutions generated by the DDP-Perfect, DDPAverage, stochastic version of DP (SDP), evolutionary multi-objective direct policy search (EMODPS)-Linear, EMODPS-Root, and EMODPS-Gaussian models were all plotted and compared with the actual operating results of the same period, which include on-site, real-time decision making by experts in reservoir operations (Figure 5)
On-site release decisions are made with more information than those in the optimization algorithms, the results indicated that the adaptation of any kind of optimization model into actual reservoir operation would lead to improving the performance in stationary conditions
Summary
Reservoirs have been widely adopted as the principal source of freshwater for various uses including irrigation, municipal, industrial, hydropower generation, and flood mitigation (Ahmad et al, 2014). During the last half-century, the derivation of optimal reservoir control policies through the use of emerging technological advancements gained much popularity and has become one of the key topics in the field of water resources management (Yakowitz, 1982; Yeh, 1985; Klemeš, 1987; Labadie, 2004). Many optimization techniques including linear programming (Belaineh et al, 1999), nonlinear programming (Birhanu et al, 2014; Li and Huang, 2008), and dynamic programming (DP) (Stedinger et al, 1984; Kim and Palmer, 1997) far have been adopted as primary problem-solving tools for water resources engineers.
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