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This study aims 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 (DP) and three using the evolutionary multi-objective direct policy search (EMODPS) algorithm. Afterward, to improve the quality of the generated solution set, an EMODPS model was selected and coupled with zone-based hedging policy that is currently being applied in real-world reservoir operations. The solutions generated by each model were then evaluated regarding proximity to the ideal and three eminent performance indices (risk, resiliency, and vulnerability). The proposed methodology was applied to a multi-purpose reservoir located in South Korea, Boryeong Dam, which had suffered a multi-year drought recently. Consequently, the solution sets from the EMODPS model yielded closer results than those of the stochastic DP model for optimality and diversity. Although the solutions from the algorithm performed better than actual operation results under normal conditions, the actual operations executed based on the zone-based hedging rule outperformed the other two in case of droughts. Among the EMODPS models, one with the fewest parameters, the EMODPS-Gaussian model, resulted in better solutions for all cases. Finally, coupling the real-world policy with the optimally derived solutions in the case of droughts improved the frequency, duration, and magnitude of the water supplies whereas the water users experienced an improvement in scale at the expense of more recurrent failures.
Keywords
Reservoir operation, Dynamic programming, Evolutionary multiobjective direct policy search, Zone-based hedging rule
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Editorial decision: Major revisions
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This preprint is under consideration at Water Resources Management. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 06 Apr, 2021
No community comments so far
Editorial decision: Major revisions
On 04 May, 2021
Reviews received
Received 01 Apr, 2021
Editor assigned
On 15 Feb, 2021
First submitted
On 14 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
This study aims 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 (DP) and three using the evolutionary multi-objective direct policy search (EMODPS) algorithm. Afterward, to improve the quality of the generated solution set, an EMODPS model was selected and coupled with zone-based hedging policy that is currently being applied in real-world reservoir operations. The solutions generated by each model were then evaluated regarding proximity to the ideal and three eminent performance indices (risk, resiliency, and vulnerability). The proposed methodology was applied to a multi-purpose reservoir located in South Korea, Boryeong Dam, which had suffered a multi-year drought recently. Consequently, the solution sets from the EMODPS model yielded closer results than those of the stochastic DP model for optimality and diversity. Although the solutions from the algorithm performed better than actual operation results under normal conditions, the actual operations executed based on the zone-based hedging rule outperformed the other two in case of droughts. Among the EMODPS models, one with the fewest parameters, the EMODPS-Gaussian model, resulted in better solutions for all cases. Finally, coupling the real-world policy with the optimally derived solutions in the case of droughts improved the frequency, duration, and magnitude of the water supplies whereas the water users experienced an improvement in scale at the expense of more recurrent failures.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
The full text of this article is available to read as a PDF.
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