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Reservoir systems are built to deliver multiple functions, such as water supply, energy production, flood control, recreation, and fisheries protection in a safe, economic, and efficient manner. This paper presents a sample three-reservoir system with comprehensive physical and hydrologic characteristics. The sample reservoir system considers energy production, water supply, and recreational and flood-control objectives. The results from the operation of this paper's reservoir system under four different scenarios show that this system's characteristics and structure conform well with actual reservoir operational processes. The reliabilities associated with meeting reservoir-operation objectives were calculated for the sample reservoir system for the four operational scenarios, ranging from independent reservoir operation with single objectives to integrated reservoir operation with multiple objectives. The results show acceptable ranges of reliability for the three objectives considered under each operational scenario. The sample reservoir system can be used to test a wide range of optimization models that are commonly used in reservoir operation.

The optimal operation of reservoir systems to meet water demand is a complex and nonlinear problem. This paper applies the biogeography-based optimization (BBO) algorithm to solve reservoir operation problems. The BBO algorithm is first verified with the minimization of three mathematical benchmark functions (Sphere, Rosenbrock, and Bukin6). In addition, the BBO algorithm was applied to a single reservoir system and a four-reservoir system. The performance of the BBO algorithm was compared with that of the genetic algorithm (GA) in solving the three optimization problems. The results show that the BBO algorithm minimized the benchmark functions accurately, and outperformed the GA in this respect. In the case of the single-reservoir hydropower optimization problem the BBO reached a near-optimal solution. The values of the objective function averaged 1.228 and 1.746 with the BBO and GA, respectively. The global solution of this problem with the nonlinear programming method equals 1.213. In the four-reservoir system application the BBO converged to 99.94% of the optimal solution in its best-performing history, whereas the GA converged to 97.46% of the optimal solution. The results from the three test problems demonstrated the superior capacity of the BBO to optimize general mathematical problems and the operation of reservoir systems.

A novel tool is proposed that couples the nondominated sorting genetic algorithm (NSGAII) with support vector regression (SVR) and nonlinear programming (NLP) to optimize monthly operation rules for hydropower generation. The SVR-NSGAII is applied to calculate the optimized release for hydropower generation by minimizing (1) the error committed by the SVR in extracting the optimized operation rule, and (2) the number of input variables used as predictors (the parsimony feature) in a regression model. The SVR calculates the optimized reservoir release for hydropower generation based on input variables and parameters values that are found by the NSGAII. An evaluation of results obtained for the Karoon-4 reservoir of Iran indicates that the SVR-NSGAII is well suited to calculate the optimal hydropower reservoir operation rule in real time with approximately 90% accuracy.

Water shortages cause intermittent operation of distribution networks in many developing countries. Under limited economic resources and frequent water shortages, the expansion of water supply for municipal use is slow and sometimes infeasible, hence supply management becomes a viable solution for operating water supply networks. One form of demand management is intermittent supply, wherein some parts of the water supply network are cut off from service during certain times and the entire network is in service at other times. Because intermittent water supply causes consumer dissatisfaction and complicates the operation of water supply networks, it is crucial to consider the principles of equanimity and justice in its implementation. This paper develops an optimization model to find the optimal scheduling of intermittent supply that reaches the maximum number of network nodes with desired pressure under various conditions of water shortage and considering the principles of equanimity and justice in a water distribution network. The network operation optimization problem is solved using the honey bee mating optimization (HBMO) algorithm linked to in a hydraulic simulator. The efficiency of the developed scheduling method is demonstrated by implementing it to two distribution networks considering different scenarios of water shortage.

Multiobjective genetic programming is used to calculate optimal reservoir-operating rules under baseline and climatic-change conditions. The rules are calculated based on river inflows to the Aidoghmoush Reservoir (located in East Azerbaijan, Iran), storage volume, and downstream irrigation demands. The objective functions are the maximization of the reliability of meeting irrigation demand and the minimization of the vulnerability to irrigation deficits in a baseline period (1987-2000) and a future period (2026-2039), the latter influenced by climatic change. The optimization results show that reservoir-operating rules that take into account changing climate would lead to improvements in reservoir performance on the order of 29-32% relative to operating rules based on baseline climatic conditions.

Water resources, in time, place, and quantity, are often poorly matched to the needs of humanity. This disparity tends to be more accentuated in arid and semiarid regions, predominant in Iran. Reservoir systems are a common means to control and manage water resources. Due to limited resources and the increasing demands for water, these systems must be optimally operated to maximize the efficiency of water use. Evolutionary optimization algorithms provide reliable and simple methods for solving complex optimization problems. One of those methods, the water cycle algorithm (WCA), is used in this paper to find optimal operation strategies for the Karon-4 reservoir and a four-reservoir system in Iran. The results demonstrate the high efficiency and reliability of the WCA in solving reservoir operation problems.

Increases in greenhouse gases caused by human activities have raised global temperature. Global warming affects water resources systems and the hydrologic cycle and may impact the performance of water resource systems. Water resources managers face challenges balancing conflicting goals in reservoir operation given the uncertainties introduced by climatic change. The HadCM3 climate model is used in this paper to estimate temperature and precipitation for early (2025–2039), middle (2055–2069) and late (2085–2099) periods of the 21st century under the A2 greenhouse gases emission scenario. The estimated temperature and precipitation from the climate model are input to a calibrated hydrologic model (IHACRES) to simulate inflow in a river basin draining to the Karoon-4 reservoir in Iran. A meta-heuristic multi-objective optimization algorithm (NSGA-II) is used in conjunction to predicted hydrologic variables to optimize dynamic operation rules in the Karoon-4 reservoir. The Karoon4 reservoir is operated non-adaptively and adaptively under climatic change. Our results show that adaptive reservoir management increases the reliability and reduces the vulnerability associated with hydropower generation in early, middle, and late simulation periods of the 21st century. These findings establish the importance of factoring in climatic change and considering adaptive strategies in future reservoir operations.

Components of a water resource system (WRS) can be assessed and evaluated by employing simulation and optimization techniques. In general, simulation models are used to evaluate the behavior and function of systems under existing conditions. This paper presents a generic formulation for simulating reservoir systems at the watershed scale. The formulation is based on the continuity equation and can be used for every type of simulation or optimization problem that addresses reservoir systems by considering existing priorities of resource allocation and demand supply. The developed formulation can simulate reservoir systems using both operation policies, the standard operation policy (SOP) and a rule curve. Also, different outlets with their elevation can be considered in each reservoir for water release to supply various objectives. The concept of integrated management has been used in this formulation so that the effects of different system factors on each other, including various resources and consumptions, are considered in the reservoir system simulation. The developed formulation is applied in a two-reservoir system, producing results that are logical and acceptable. [ABSTRACT FROM AUTHOR]