The purpose of this study is to select the best modeling approach (simulation or optimization) for operation the water supply system using multi-criteria decision-making method. For this purpose, the Geophysical Fluid Dynamics Laboratory-Earth System Models (GFDL-ESM2M) and the Model for Interdisciplinary Research on Climate-ESM (MIROC-ESM) models were selected to predict the changing trend of the climatic variables of rainfall and temperature, respectively. Then Artificial Neural Network (ANN) model and a decision support system tool named Cropwat were used to simulate water resources and consumption; and to model the behavior of the water supply system, the MODified SYMyld (MODSIM) (as simulator) and the modeling language and optimizer LINGO 18 (as optimizer) were used in the future time period (2026–2039) and the results were compared with the baseline period (1987–2000) for the Idoghmush reservoir (Iran). The results of MODSIM simulation model show that the indexes of reliability, vulnerability, reseiliency and flexibility in the future time period under the RCP2.6 emission scenario compared to the baseline time period decreased by 9%, decreased by 22%, increased by 4%, and decreased by 2%, respectively. The results of the LINGO 18 optimization model show that the reliability, vulnerability, resiliency and flexibility indexes in the future time period under the RCP2.6 emission scenario compared to the baseline time period decreased by 13%, decreased by 17%, increased by 14% and increased by 3%, respectively. Due to the different results obtained from optimization and simulation approaches for the study area, the Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) multi-criteria decision-making method was used to select a more appropriate approach. The results show that for water resources management planning, the simulation approach is given priority over the optimization approach due to its characteristics.
相似文献Hydropower is a low-carbon energy source, which may be adversely impacted by climate change. This work applies the Grasshopper Optimization Algorithm (GOA) to optimize hydropower multi-reservoir systems. Performance of GOA is compared with that of particle swarm optimization (PSO). GOA is applied to hydropower, three-reservoir system (Seymareh, Sazbon, and Karkheh), located in the Karkheh basin (Iran) for baseline period 1976–2005 and two future periods (2040–2069) and (2070–2099) under greenhouse gases pathway scenarios RCP2.6, RCP4.5, and RCP8.5. GOA minimizes the shortage of hydropower energy generation. Results from GOA optimization of Seymareh reservoir show that average objective function in baseline is 85 and minimum value of average objective function in 2040–2069 would be under RCP2.6 (equal to 0.278). Optimization of Seymareh-reservoir based on PSO shows that average value of objective function in baseline is less (that is, better) than value obtained with GOA (10.953). Optimization results for two-reservoir system (Sazbon and Karkheh) based on GOA optimization show that objective function in baseline is 5.44 times corresponding value obtained with PSO, standard deviation is 2.3 times that calculated with PSO, and run-time is 1.5 times PSO’s. Concerning three-reservoir systems it was determined that objective function based on PSO had the best value (the lowest energy deficit), especially in future. GOA converges close to the best objective function, especially in future-periods optimization, and convergence to solutions is more stable than PSO’s. A comparison of performance of GOA and PSO indicates PSO converges faster to optimal solution, and produces better objective function than GOA.
相似文献Reservoirs are used as one of the surface water sources for different and often conflicting water supply purposes. Given the complex management policies governing a basin, it is essential to simultaneously consider different goals and cope with the associated trade-off in water resources management. This purpose requires coupling a multi-objective optimization algorithm with a reservoir simulation model, which this approach increases required computational efforts. Various simulation–optimization approaches have been developed and used for solving the related problems. However, they often have complicated methods and certain limitations in real-world applications. In this study, a new multi-objective firefly algorithm—K nearest neighbor (MOFA-KNN) hybrid algorithm is developed which is time-efficient and is not as complicated as previous approaches. The proposed algorithm was evaluated for both benchmark and real problems. The results of the benchmark problem showed that the execution time of the MOFA-KNN hybrid algorithm was up to 99.98% less than that of the multi-objective firefly algorithm (MOFA). In the real problem, the MOFA-KNN algorithm was linked to the 2D hydrodynamic and water quality model, CE-QUAL-W2, to test the developed framework for reservoir operation. The Aidoghmoush reservoir as a case study investigated to minimize the total released dissolved solids (TDS) and the water temperature difference between the inflow and the outflow. The results demonstrated that the MOFA-KNN algorithm significantly reduced the simulation–optimization execution time (>?660 times compared with MOFA). The minimum released TDS from the reservoir was 13.6 mg /l and the minimum temperature difference was 0.005 °C.
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