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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.

In this study, the zone-based hedging rule, which is the main operating policy adopted from multipurpose reservoirs in Korea is adjusted to re ect the multi-year droughts caused by climate change. Annual synthetic in ow series with different magnitudes of long memory were generated using the autoregressive fractional integrated moving average (ARFIMA) model. The generated in ow series were then disaggregated into 10-day series and utilized as input variables to derive the alternative hedging rules. The alternative hedging rules from this study were used in adaptive reservoir management by newly updated information. Finally, the performance of the suggested policy is measured in terms of frequency and magnitude under the historical in ow series. As a result, adaptive reservoir management demonstrated improvements in the following terms of the frequency of critical failures (water de cit ratio greater than 30%): 6.14% of the simulation period in the status quo (SQ) policy, and 2.99% in the adaptive management. However, the overall reliability of the reservoir during the simulation horizon was better when operated with the SQ policy (41.19%) than the results from adaptive management (26.42%). Because this result is in a good agreement with the original objective of the hedging rules, the adaptive policy suggested in this study holds promise and may be utilized in further reservoir management under drought conditions.

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