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Sustainability assessment can be used as a basic managerial analysis to evaluate the capability of water resources in supplying associated demands. Alteration of management policies in order to improve the systems' performance and sustainability can lead to additional costs of system operation. In this study a novel framework is proposed for assessing economic life cycle costs of dams considering system's performance from sustainability aspect. For convenience, the term Life Cycle Costs (LCC) is used instead of Economic LCC throughout this paper. First, a fuzzy model is proposed to discount and estimate the LCC of dams in different time intervals of their life span including: Construction, Operation and Maintenance (M&O), and Disposal periods. The model is capable of reflecting uncertainties caused by estimation and previsions of different costs because of fuzzy theory application. Then, sustainability of dam performance in operation period is evaluated through triple criteria of Reliability, Reversibility, and Vulnerability (R-R-V). To provide more realistic results, different system performance levels are defined based on the system's capability to supply demands and the importance of each level is evaluated by weighting them. Furthermore, it is studied how changes in reservoirs operation strategies can reduce the LCC because of higher performance. The proposed methodology is applied to assess the LCC and performance of a dam located at North Eastern part of Iran. The results show that the system's performance is remarkably enhanced when the operating rules are revised and this change will intangibly reduce the economic benefits. [ABSTRACT FROM AUTHOR]

A common method for assessing the energy potential of hydropower systems in Iran is the sequential streamflow routing with control on the energy-yield reliability. The method results in developing a reliability-based simulation (RBS) model that is used to analyze single-reservoir hydropower systems. In most of the cascade hydropower systems in the country, a single-reservoir RBS model is usually employed in the design and operation of each of the systems reservoirs. This paper presents a multireservoir RBS model considering the integrated operation of the systems. The model employs the general algorithm of the single-reservoir RBS model; however, with a single-period optimization submodel in determining releases from reservoirs during the system simulation. The objective function of the submodels minimizes the sum of reservoir releases and/or maximizes the sum of reservoir storages in each of the time periods, subject to a reliability constraint on the system’s energy yield. The single and multireservoir RBS models are applied in the Khersan hydropower system located in Iran as a case study. The multireservoir RBS, compared to the single-reservoir model, results in 7.9% excess firm-energy yield in reliability of 90%. [ABSTRACT FROM AUTHOR]

In this study, the non-dominated sorting genetic algorithm (NSGA-II) is used for the multi-objective optimization of the Sefidrud reservoir in Northern Iran. The main objectives include water supply, hydropower generation and sediment flushing. In addition to some physical constraints such as the reservoir storage and the outlet flow, maximum flushing outflow and non-flushing in irrigation seasons, the environmental constraints as fish migration and spawning are taken into an account. After obtaining the Pareto optimal solutions by means of the weighted objective functions and non-symmetric Nash bargaining, various scenarios are defined. Then these scenarios are analyzed by introducing a new sustainability index. Furthermore, the different percentages of downstream water demand are considered in order to achieve a better evaluation of the scenarios. The results of study indicate that the optimal solutions are more sustainable than those of the current operation of Sefidrud reservoir, which increase the sediment flushing by 37 million tons compared to the current operation, with the same hydroelectric energy and downstream water supply. The proposed methodology could be used successfully in other reservoir operations including the sediment flushing. [ABSTRACT FROM AUTHOR]

Prediction of daily evaporation has an important role in reservoir management, regional water planning and evaluation of drinking-water supplies. The main purpose of this study was to assess different types of mother wavelet as activation functions instead of commonly used sigmoid for finding the main differences in the results of daily pan evaporation prediction in the Lar synoptic station. So, using conjunction of wavelet theory and multilayer perceptron (MLP) network, two mother Wavelets named Mexican Hat and polyWOG1 are considered for developing hybrid WNNs. The algorithms were trained and tested using a 6-year data record (1999 daily values) from 2005/01/01 to 2010/09/01. Instead of using common sigmoid activation functions in MLP network, wavelet function was applied to construct the wavelet neural network. Results show that Mexican hat wavelet neural network in the best topology presents 98.35 % accuracy in training phase and 96.04 % in testing and PolyWOG1 wavelet neural network in the best topology presents 95.92 % accuracy in training phase and 91.03 % in testing of model. In the MLP model with standard sigmoid function results were 90.6 % in training and 87.63 % in testing. Comparison of WNN and MLP shows that Mexican hat mother wavelet could have better accuracy in the daily pan evaporation modeling. [ABSTRACT FROM AUTHOR]

Reservoir system reliability is the ability of reservoir to perform its required functions under stated conditions for a specified period of time. In classical method of reservoir system reliability analysis, the operation policy is used in a simple simulation model, considering the historical/synthetic inflow series and a number of physical bounds on a reservoir system. This type of reliability analysis assumes a reservoir system as fully failed or functioning, called binary state assumption. A number of researchers from various research backgrounds have shown that the binary state assumption in the traditional reliability theory is not extensively acceptable. Our approach to tackle the present problem space is to implement the algorithm of advance first order second moment (AFOSM) method. In this new method, the inflow and reservoir storage are considered as uncertain variables. The mean, variance and covariance of uncertain variables are determined using moment values of reservoir state variables. For this purpose, a stochastic optimization model developed based on the constraint state formulation is applied. The proposed model of reliability analysis is used to a real case study in Iran. As a result, monthly probabilities of water allocation were computed from AFOSM method, and the outputs were compared with those from Monte Carlo method. The comparison shows that the outputs from AFOSM method are similar to those from the Monte Carlo method. In term of practical use of this study, the proposed method is appropriate to determine the monthly probability of failure in water allocation without the aid of simulation. [ABSTRACT FROM AUTHOR]

Two adaptive extensions of the varying chromosome length genetic algorithm (VLGA) model are proposed for optimal reservoir operation. The initial solutions for each year of the planning horizon are selected among the optimal solutions for the previous years based on the similarity in hydrologic characteristics of the years in terms of reservoir storage at the beginning of water years and annual reservoir inflows. The results from three case studies in Iran show an improvement in the convergence speed of VLGA between 21-47%. In an extended time horizon problem, the models improve the best fitness values with significantly less computation. [ABSTRACT FROM AUTHOR]

Abstract Optimization of a multi-reservoir system operation is challenging due to the non-linearity, stochasticity, and dimensionality involved in such a problem. In this research, a long-term planning model is presented for optimizing the operation of Iranian Karoon-Dez reservoir system using an interior-point algorithm. The system is the largest multi-purpose reservoir system in Iran with hydropower generation, water supply, and environmental objectives. The focus is on resolving the dimensionality of this problem while considering hydropower generation and water supply objectives. The weighting and constraints methods of multi-objective programming are used to assess the trade-off between water supply and hydropower objectives so as to find noninferior solutions. The computational efficiency of the proposed approach is demonstrated using historical data taken from Karoon-Dez reservoir system. [ABSTRACT FROM AUTHOR]