بهره برداري از مخزن سد چند منظوره در شرايط خشكسالي با استفاده از قوانين جيره بندي

Nowadays, due to limited water resources, population growth, economic development, urban expansion, an‎d recurrent drought events, the optimal operation of surface water resources in Iran has become increasingly important. Moreover, the uneven spatial an‎d temporal distribution of precipitation an‎d surface runoff, the predominance of arid an‎d semi-arid climates across much of the country, an‎d the impacts of climate change—including rising temperatures, altered precipitation patterns, an‎d intensified extreme events—have further increased the challenges of water resources management. Under such conditions, developing efficient an‎d flexible reservoir operation policies to mitigate the impacts of water scarcity an‎d drought is essential. In this study, a novel approach was proposed for deriving operation rules for the Maroon Dam reservoir to improve reservoir management under drought conditions. To this end, optimization models were formulated for both discrete an‎d continuous hedging rule curves, an‎d their performance was compared with the Stan‎dard Operating Policy (SOP). Discrete an‎d continuous hedging policies, along with SOP, were developed for single-objective water supply an‎d hydro‎power operation scenarios, while the discrete hedging model was further extended to a multi-objective operation framework. All models were solved using a Genetic Algorithm (GA). In this research, precipitation, minimum temperature, an‎d maximum temperature for the period 2020–2050 were downscaled an‎d simulated using the Statistical DownScaling Model (SDSM) based on outputs from the CanESM2 climate model under the RCP 8.5 emission scenario. Reservoir inflow was estimated through a hybrid approach combining Genetic Programming (GP) an‎d Artificial Neural Networks (ANNs). Furthermore, two drought indices, namely the Stan‎dardized Precipitation Index (SPI) an‎d the Stan‎dardized Runoff Index (SRI), were employed to characterize drought conditions over different periods. To enhance the adaptability of the discrete hedging models to drought conditions, the SPI was integrated into the operation models under single-objective water supply, hydro‎power, an‎d multi-objective operation scenarios. The results indicated that the discrete hedging policy outperformed both the continuous hedging policy an‎d SOP in preserving reservoir storage an‎d mitigating severe water shortages. Although the continuous hedging policy exhibited performance similar to SOP, it resulted in higher end-of-period storage due to reduced inflows. The findings revealed that the discrete hedging approach, by accepting more frequent but less severe shortages an‎d intentionally supplying only a portion of deman‎ds at each time step, enables water conservation within the reservoir an‎d prevents severe deficits during drought periods. Consequently, the maximum downstream water deficit under the discrete hedging policy was reduced compared to both the continuous hedging policy an‎d SOP. In the single-objective water supply model, incorporating the drought index reduced the objective function value from 5568 to 5133, while the average reservoir storage decreased from 546.5 to 499 million cubic meters, indicating a more efficient allocation of water resources for reducing downstream deficits. In the hydro‎power model, the objective function value decreased from 150 to 135, an‎d average energy production increased from 45,484 to 51,157 MW, whereas average reservoir storage declined from 600 to 356 million cubic meters. Under the multi-objective scenario, the objective function value increased when the drought index was incorporated, while the average energy production an‎d reservoir storage were 42,066 an‎d 45,523 MW, an‎d 696 an‎d 482 million cubic meters, respectively. Finally, the multi-objective discrete hedging model integrated with the drought index was selec‎ted as the final model an‎d eva‎luated under future climate conditions (2020–2050).

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172003