Introduction to Optimization Analysis in Hydrosystem Engineering
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Sign In. Access provided by: anon Sign Out. A novel culture particle swarm optimization algorithm for optimal operation problem in hydropower station is suggested. A local random search operator to achieve knowledge structure in belief space and enhance the population diversity and increase the capacity of global search with the introduction of culture algorithm, The simulation results of culture particle swarm optimization algorithm compares with particle swarm optimization algorithm and shows that this new algorithm can overcome the shortcomings of the traditional particle swarm algorithm and to gain better convergence speed and computational accuracy.
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This book presents the basics of linear and nonlinear optimization analysis for both single and multi-objective problems in hydrosystem engineering. The book. Request PDF on ResearchGate | Introduction to Optimization Analysis in Hydrosystem Engineering | This book presents the basics of linear and nonlinear .
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The Art Of Japanese Joinery. Basic Building and Construction Skills 5th Edition. Woodworking For Dummies For Dummies. Building Construction Illustrated. However Ferreira and Teegavarapu found the decreasing of power generation when in the environment constraint was merge with optimal operation because the reservoir need to provide enough water for conservation flow to intensify dilution for downstream water quality goal.
Introduction to Optimization Analysis in Hydrosystem Engineering
As a result, there will be less water allocate for power generation and reduce the production of electricity. The same argument is share by Yang et al. In their study the result shows the power generation output is lower because of the extortionate abandon water during high flow which cannot be use for power generation. Optimization and economic aspect in water resources is essential to analyse when the investment of the reservoir of development and operation must be compare with economic benefit of the user such as hydropower company and irrigation schemes.
From their research, there is potential to carried out optimization using economic objective for large and complex watershed with urban economic model through the advancement of computer technology and involvement of engineer solving it objective function. Harou et al. They have suggest the modeller, manager and operator to work together to embrace the hydo-economic component in their existing operation model.
This will help the water manager to achieve efficient water management to satisfied demand of the stakeholder economic. However the decision on which method to be used depends on the type of problem analysis for each water resources system.
More advanced classification is shown in Fig. Another advantage of this method is its versatility to be used for large scale system, convergence to global optima and supported by many software packages Rani and Moreira One of the studies is by Belaineh et al. In most cases the reservoir operation model exists as non linear since the optimized objective function and constraint such as reservoir capacity, pump and demand are in nonlinear forms. Among the common non linear programming variances are sequential linear programming Barros et al.
Consoli et al. The interactive process in this method allows predetermined criteria to be fixed by the planner and the decision makers are satisfied by the model performance. The nonlinear and stochastic nature of reservoir optimization problem can be formulated using dynamic programming. The optimal rule for water resources management for reservoir in arid condition has been studied by Alaya et al.
The objectives of the stochastic dynamic programming technique are irrigation water demand and minimum water level in the Nebhana reservoir by considering water irrigation release and decision periods. The problem begins when the number of discrete combinations of state variable raise exponentially as the number of state variable increase and it is a challenge for researchers to reduce the amount of probable discrete state needed to solve optimal large multi state problem Loucks et al.
Reservoir Optimization in Water Resources: a Review 3. One of the well known methods of optimization technique comes from the Computational Intelligent which is the Evolutionary Computation EC. Rapid development of computer science has made the EC to become popular with the researchers in operation research because of the ability to find solution near the optimal result with reasonable computation time.
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Rani and Moreira state that the evolutionary algorithms such as Genetic Algorithm, Simulated Annealing, Tabu Search, Particle Swarm Optimization and Honey Bees Mating Optimization are good prospective tools when dealing with nonlinear and multi-objective analysis plus most of them can be linked to the simulation model. Artificial intelligence especially Artificial Neural Network ANN is a method which simulates how the brain processes information for thinking and reasoning. Actually it is also called the black box model but with more complex regression and statistical calculation.
It has been used by many researchers in the water resources studies. One of the important aspects in reservoir management is inflow forecasting. El-Shafie et al. The Genetic Algorithm GA is used to find the ANN model parameter instead of the traditional approach which uses error matrices between calculated output and target values. The model is able to handle multiple decision variables concurrently with satisfactory results. The value of variable in the water resource analysis cannot always be specific because of the uncertainty or the fuzziness properties of the variable itself.
This is why the fuzzy logic optimization is applied by the water recourses community, by charactering the membership functions into fuzzy. Researchers such as Chen and Chang emphasize the use of fuzzy sets by incorporating objective and subjective uncertainties to study water resources redistribution possibility in a channel—reservoir system transponder. Chuntian develop a fuzzy optimal model of real time multi-reservoir operation for Yangtze River. He applies the fuzzy dynamic programming of multiple objectives to the flood operation system and subsystem.
To enhance the potential of this popular computational intelligence, researchers started to combine AAN and Fuzzy logic to become Neuro-fuzzy approach. Particle Swarm Optimization PSO is one of the swarm intelligent methods which simulates the social behavior of birds. It was proposed by Eberhart and Kennedy Since then, many researchers apply the PSO in many types of optimization problems.
Reddy and Kumar integrate Pareto dominance principle with Particle Swarm Optimization algorithm to develop a multi objective particle Swarm Optimization. It was tested at the Bhadra Reservoir in India and proves to be a viable tool for minimizing irrigation deficit and maximizing hydropower and water quality up to satisfactory level requirement in the downstream area. Baltar and Fontane develop multiple objectives Particle Swarm Optimization to solve the problem of multipurpose reservoir operation. Mousavi and Shourian use the PSO to optimize the design and operation of hydropower system in Iran.
The Genetic Algorithm GA is a search heuristic that imitates the process of natural evolution such as inheritance, mutation, selection, and crossover. Many researchers have contributed to the enhancement of the GA especially by combining it with other method hybrid and by comparing the performance. Among the researchers are Chang and Chen who compare real-coded GA and Binary-coded GA in the optimization of flood control reservoir model.
They conclude that the real-coded GA is more efficient and precise than the binary-coded GA. The new approach shows better performance in predicting the total water deficit and generalized shortage index. Chen et al.
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GA has also been applied to long term operation of Three Gorges Dam with integration of Incremental Dynamic Programming and performs better compared to a conventional genetic algorithm Li et al. They found that new method is robust and flexible to be used on any reservoir operating policies. The Genetic Algorithm is also used by Chang et al.
By combining it with reservoir simulation and sediment flushing model the result shows that the reservoir has a lower Shortage Index SI and higher Flushing Efficiency FE compared to the existing operation rule. This algorithm is further enhanced to constrain GA by Chang et al. In this new algorithm the constraint is incorporated into the objective function configured with proper penalty function to form the fitness function. Even though the constraint is allowed to breach, it can be penalized by the penalty terms. Although GA is a popular algorithm for reservoir optimization problem, it cannot analyze complex system such as a long term reservoir operation to find optimal operating procedure.
To solve this problem Wang et al. Simulation model produces output that will be used by the optimization strategy to find optimal solution. The optimal solution will lead better input to the simulation model. The report presents the capabilities and weakness of the reviewed model and is a good reference for water managers and researchers to find suitable program for reservoir modelling system.
One of their findings proposes the most efficient water allocation strategies especially in the region where getting enough water is hard.
In addition, the involvement of stakeholders in the development of the optimization model is important as they are the one who implemented the policies as proposed by the model. The integration of Decision Support System in the optimization model will act as a bridge, linking the theoretical and practical usage of the optimization rule or decision suggested by the model.
The combination of simulation and Genetic Algorithm Optimization Technique shows that by optimizing the rule curve, it is possible to improve the shortage of water in the Irrigation area near Tarbila Dam. In fact, many of the optimization techniques applied in water resources came from other fields such as biology, electric, electronics and thermodynamic.
However there are many new optimization algorithms ready to be explored or enhanced by researchers in water resources.