Optimization of Chain-Reservoirs’ Operation with a New Approach in Artificial Intelligence

Ehteram, Mohammad; Allawi, Mohammed Falah; Karami, Hojat; Mousavi, Sayed‐Farhad; Emami, Mohammadamin; El-Shafie, Ahmed; Farzin, Saeed

doi:10.1007/s11269-017-1625-6

Cited by 40 publications

(18 citation statements)

References 20 publications

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“…The probable inflows are random than the dependable inflows. JA has been developed with the help of MATLAB for population size of 30 after performing sensitivity analysis for population sizes of 5,10,15,20,25,30,35,40,45, and 50. The optimal value is achieved for 150,000 FEs; the square root of the objective function achieved is 218.88 MCM.…”

Section: Mula Reservoirmentioning

confidence: 99%

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Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm

et al. 2019

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In this paper, an application of the Jaya Algorithm (JA) is presented, to develop an operation optimization model for the Mula reservoir, located on the upper Godavari Basin, in India. The mentioned algorithm is a relatively new optimization technique, which is algorithm-specific and parameterless. In JA, there is no need for algorithm-specific parameter tuning, unlike with other heuristic techniques. To test its applicability, the model performance has been compared with that of other models for hypothetical four reservoir system studies available in the literature. Simulations for hypothetical four reservoir system have proven that JA is a better solution for a number of Function Evaluations when compared with the results obtained by means of other evolutionary methods such as Genetic Algorithms, Particle Swarm Optimization, Elitist Mutated Particle Swarm Optimization, and Weed Optimization Algorithm models reported in previous studies. Simulations have been carried out for real time operation of the Mula reservoir, and have revealed its superior performance when comparing the water releases proposed by it and the ones proposed by existing policy. Hence, from the two case studies presented, it can be concluded that the JA has potential in the field of reservoir operation and can be further explored to operation optimization of existing multi-reservoir system, with lower computations.Sustainability 2020, 12, 84 2 of 21 alternative to meet the present and future water requirements. Many researchers have been working towards rational approaches to allot the water very optimally in every essence. Many approaches can be found in the literature, such as can be read in in every essence; vagaries of approach have been commended in the literature with excellent opinions regarding the distribution of resources [2].The techniques suggested and implemented in reservoir operation studies are chance constrained Linear Programming (LP) by [3], in which multi-purpose reservoir operation optimization was carried out with the aim of maximization of hydropower generation and fulfillment of irrigation requirements according to the reliability level. The nonlinear power production function was linearized and a solution was obtained in the range of the specified tolerance. In a similar way, a multi-purpose reservoir operation optimization was carried out by using Non-Linear Programming (NLP) in [4] for the Koyna dam, Maharashtra, India. Dynamic Programming (DP) [5] was implemented in the integration with fuzzy rules and simulation studies to generate the general operating policies for Dez and Karoon reservoirs in Iran. Ant Colony Optimization (ACO) was implemented for the reservoir operation optimization of the Dez reservoir system, Iran, in [6], and resulted in a global optimal solution although the tuning of parameters was recommended as the model is parameter sensitive. The application of the Bat Algorithm (BA) to the Karoun-4 reservoir system in Iran and to another hypothetical reservoir system showed the merits of the BA over o...

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Section: Mula Reservoirmentioning

confidence: 99%

“…It was found that Elitist Mutated PSO performed better than GA and standar PSO. Shark Algorithm (SA) was applied by [20] to the complex cases of reservoir…”

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confidence: 99%

Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm

et al. 2019

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“…Each particle i is characterized by three vectors: the X i vector, which is the current position of the particle; the Y i vector, which is the best position that the particle has reached in its previous iteration; and the particle velocity vector shown with V i . The positions and velocities of particles in the algorithm are updated based on the following equations [23,24]:…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…(2) Vulnerability index: the Vulnerability index is defined as the maximum failure rate created during the operation period of a reservoir system. The smaller this index, the better the system performs [23]:…”

Section: Case Studymentioning

confidence: 99%

Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

et al. 2019

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One of the most important issues in the field of water resource management is the optimal utilization of dam reservoirs. In the current study, the optimal utilization of the Aydoghmoush Dam Reservoir is examined based on a hybrid of the bat algorithm (BA) and particle swarm optimization algorithm (PSOA) by increasing the convergence rate of the new hybrid algorithm (HA) without being trapped in the local optima. The main goal of the study was to reduce irrigation deficiencies downstream of this reservoir. The results showed that the HA reduced the computational time and increased the convergence rate. The average downstream irrigation demand over a 10-year period (1991–2000) was 25.12 × 106 m3, while the amount of water release based on the HA was 24.48 × 106 m3. Therefore, the HA was able to meet the irrigation demands better than some other evolutionary algorithms. Moreover, lower indices of root mean square error (RMSE) and mean absolute error (MAE) were obtained for the HA. In addition, a multicriteria decision-making model based on the vulnerability, reliability, and reversibility indices and the objective function performed better with the new HA than with the BA, PSOA, genetic algorithm (GA), and shark algorithm (SA) in terms of providing for downstream irrigation demands.

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“…The shark algorithm (SA) based on shark life and rotational movement was applied to reservoir operation to reduce irrigation deficits [20]. The rotational movement was considered as having powerful ability to avoid trapping in the local optimums.…”

Section: Introductionmentioning

confidence: 99%

Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm

Ehteram

Singh

Karami

et al. 2018

Water

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Water scarcity is a serious problem throughout the world. One critical part of this problem is supplying sufficient water to meet irrigation demands for agricultural production. The present study introduced an improved weed algorithm for reservoir operation with the aim of decreasing irrigation deficits. The Aswan High Dam, one of the most important dams in Egypt, was selected for this study to supply irrigation demands. The improved weed algorithm (IWA) had developed local search ability so that the exploration ability for the IWA increased and it could escape from local optima. Three inflows (low, medium and high) to the reservoir were considered for the downstream demands. For example, the average solution for the IWA at high inflow was 0.985 while it was 1.037, 1.040, 1.115 and 1.121 for the weed algorithm (WA), bat algorithm (BA), improved particle swarm optimization algorithm (IPSOA) and genetic algorithm (GA). This meant that the IWA decreased the objective function for high inflow by 5.01%, 5.20%, 11.65% and 12% compared to the WA, BA, IPSOA and GA, respectively. The computational time for the IWA at high inflow was 22 s, which was 12%, 18%, 24% and 29% lower than the WA, BA, IPSOA and GA, respectively. Results indicated that the IWA could meet the demands at all three inflows. The reliability index for the IWA for the three inflows was greater than the WA, BA, IPSOA and GA, meaning that the released water based on IWA could well supply the downstream demands. Thus, the improved weed algorithm is suggested for solving complex problems in water resources management.

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Optimization of Chain-Reservoirs’ Operation with a New Approach in Artificial Intelligence

Cited by 40 publications

References 20 publications

Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm

Computer Modeling for the Operation Optimization of Mula Reservoir, Upper Godavari Basin, India, Using the Jaya Algorithm

Development of a Novel Hybrid Optimization Algorithm for Minimizing Irrigation Deficiencies

Irrigation Management Based on Reservoir Operation with an Improved Weed Algorithm

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