m. h. afshar

Parallel Cellular Automata (PCA) previously has been employed for optimizing bi-objective reservoir operation, where one release is used to meet both objectives. However, if a single release can only be used for one objective, meaning two separate sets of releases are needed, the method is not applicable anymore. In this paper, Multi-Step Parallel Cellular Automata (MSPCA) has been developed for bi-objective optimization of single-reservoir systems’ operation. To this end, a novel cellular automata formulation is proposed for such problems so that PCA’s incapability when dealing with them will be overcome. In order to determine all releases throughout the operation period, in each iteration – unlike PCA – two updates take place so as to calculate releases individually. Since a bi-objective problem in Dez reservoir (in southern Iran) has been solved by PCA in earlier works, the same data is used here. The results are given for a 60-months operation period, and to evaluate this method, the results of Non-Dominated Sorting Genetic Algorithm (NSGAII) is also given for the same problem. The comparison shows MSPCA, beside remarkable reduction in computational costs, gives up solutions with higher quality as well.

Energy storage is an issue that scientists have been researching for many years. There are several energy storage alternatives, among which the pumped storage system, battery storage, superconducting magnets, ywheels, and compressed air are the most popular. Based on former research, pumped storage is the best method for energy storage. The pumped storage plant is an effective method to balance the dierence value between electricity demand and production, which has greatly expanded. The advantages of this system include a considerable capacity to store energy, a short-term response to power network uctuations, long-term energy storage and etc. Although there are many pumped storage plants under service, a comprehensive research have yet to be undertaken. The purpose of this research is optimization of daily planning, based on available data of an existing pumped storage plant. To achieve this aim, minimization of the dierence value between energy production and demand has been determined. A genetic algorithm was used for optimization. The genetic algorithm is a meta-heuristic algorithm. It is inspired by the genetic evolution of living creatures. This algorithm was used according to binary coding. The Siah Bishe pumped storage plant in Iran was the research case study. This plant includes two non-leveled reservoirs that are connected by two water tunnels. The dierence level of the reservoirs is approximately 500 meters. Two reversible pump-turbine units are applied in each tunnel, and all four pump-turbines have the same specications. There are three constraints for this problem: 1) the maximum and minimum water volume of upper and lower reservoirs, 2) the maximum and minimum water ow of the pump-turbine unit and 3) the stationary of exploitation. Based on the international electricity consumption pattern, it is assumed that energy consumption is constant during one hour, while it may change each individual hour. Finally, the solution is estimation of the ow rate into the tunnels over 24 hours.

Flood conducting and controlling systems are very huge and the construction and operation of these systems are very expensive. Any reduction in the construction cost of these system will lead to a major saving. This paper addresses the problem of optimal design of flood controlling systems using detention dams and proposes a methodology for the optimal design of these systems using evolutionary algorithms. The method uses Genetic Algorithm (GA) as a search engine and the Transport Module of the SWMM as the simulator. The simulator is able to analyze the unsteady flow in open channels and the basin of the dam. It is shown that interfacing GA as the optimizer and SWMM as the simulator leads to an efficient optimization tool for the optimal design of flood controlling systems. The effectiveness and efficiency of the model is tested against a simple analytical problem The applicability of the model for large scale real world problems is verified by solving the flood controlling system of South Pars Project and the results are compared with those proposed by the Consulting Engineers. The results show that proposed optimization model can considerably (about 23%) reduce the total costs of flood controlling systems.

Industrialization of the human life requires that more attention is paid to the environmental issues. Sewers are one of the main sources of pollution and must carefully be collected and disposed. The cost of construction and maintenance of the sewer systems is very high in particular the large scale systems required for populated cities. Any reduction in the design cost of these structures lead to a considerable savings. Many researchers have studied the use of optimization algorithms for the optimal design of sewer networks. In this paper the capabilities of mathematical programming and genetic algorithm for the optimal design of sewer networks are investigated and compared for networks with fixed layout. Two different models, namely modified Hazen-Williams and Manning formula with fixed and variable friction coefficient is used for hydraulic simulation of the network. Nodal elevations of the network are considered as the decision variables of the problem leading to the possibility of discrete determination of the pipe diameter. Successful application of these algorithms demonstrates the ability of proposed models to optimal design of sewer networks.

In the present paper, using evolutionary strategy, optimization of problems with continuous variables is performed. Evolutionary strategy is one of the meta-exploration methods that is basically provided for problems with continuous variables and can adjust its parameters during the optimization process. In order to evaluate, optimization of known functions is performed using evolutionary strategy and finally the results are presented and the effect of different parameters on the optimization process is presented by presenting tables and graphs. And the results obtained from the presented method are compared with the results obtained from other methods.

Analyzing the flow on dams by numerical methods compared to the physical model preparation is the most efficient way to reduce costs and time. In general the type of method used, flow analysis on overflows in its permanent condition requires solving the category of differential equations involved called Navirastox equations in order to make the free surface of the flow very complex and uneconomical.One type of equation used in overflows is the use of medium depth equation model, or in other words, shallow water equations that result from integrating the Navier-Stokes equations in depth and applying boundary conditions of surface and substrate. Intermediate depth equations, also known as shallow water equations, are mainly used to simulate currents where the velocity value is constant at depth of flow and the pressure distribution at depth is hydrostatic.Due to the complexity of the equations and the lack of a precise answer, different numerical methods have been developed to solve the equations governing the physical phenomena.One of the newest of these methods is the set of methods without a network, which has been introduced in the last two decades to solve differential equations, each of which has its own advantages and disadvantages.Dissociation of the problem in many non-networked methods leads to integral equations, the solution of which requires numerical integralization and the introduction of gaseous points and related weights along with networking.In this paper, the least squares method is used to solve shallow water equations.At least discrete squares have been used in the dissection phase of the differential equation to achieve algebraic equations, as well as the minimum weight squares of the data in order to obtain the values ​​of the form functions.The most important advantage of this method should be considered in eliminating the steps of integrating the process of calculating the matrices of the coefficients and also supporting it without networking in the real sense.Correction of the method has been done by numerical analysis of the flow on the overflow of one of the dams of the country and its comparison with the results related to the speed and water level in the physical model.