Optimal operation of reservoir systems using the Wolf Search Algorithm (WSA)

2019 ◽  
Vol 19 (5) ◽  
pp. 1396-1404 ◽  
Author(s):  
Edris Ahmadebrahimpour
Keyword(s):  
Genetic Algorithm ◽  
Water Resources Management ◽  
Simple Structure ◽  
Search Algorithm ◽  
Global Optimum ◽  
Optimal Operation ◽  
Resources Management ◽  
Reservoir System ◽  
Hydropower Plants ◽  
Reservoir Systems

Abstract Optimizing hydropower plants is complex due to nonlinearity, complexity, and multidimensionality. This study introduces and evaluates the performance of the Wolf Search Algorithm (WSA) for optimizing the operation of a four-reservoir system and a single hydropower system in Iran. Results indicate WSA could reach 99.95 and 99.91 percent of the global optimum for the four-reservoir system and single reservoir system, respectively. Comparing the results of WSA with a genetic algorithm (GA) also indicates WSA's supremacy over GA. Thus, due to its simple structure and high capability, WSA is recommended for use in other water resources management problems.

scholarly journals Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

2017 ◽  
Vol 18 (4) ◽  
pp. 1484-1496 ◽  
Author(s):  
Afshin Mansouri ◽  
Babak Aminnejad ◽  
Hassan Ahmadi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Real World ◽  
Global Optimum ◽  
Optimal Operation ◽  
The Other ◽  
Reservoir System ◽  
Search Optimization ◽  
Real World Problem ◽  
Reservoir Systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

scholarly journals Optimal operation of multi-reservoir systems: comparative study of three robust metaheuristic algorithms

2020 ◽  
Author(s):  
Saeid Akbarifard ◽  
Mohammad Reza Sharifi ◽  
Kourosh Qaderi ◽  
Mohamad Reza Madadi
Keyword(s):  
Harmony Search ◽  
Imperialist Competitive Algorithm ◽  
Complex Problem ◽  
Global Optimum ◽  
Optimal Operation ◽  
Metaheuristic Algorithms ◽  
Reservoir System ◽  
Optimal Value ◽  
Reservoir Systems ◽  
Cpu Usage

Abstract In this study, the capability of recently introduced Moth Swarm Algorithm (MSA) was compared with two robust meta-heuristics of harmony search (HS) algorithm and imperialist competitive algorithm (ICA). First, the performance of these algorithms was assessed by seven benchmark functions having 2–30 dimensions. Next, they were compared in optimization of complex problem of 4-reservoir and 10-reservoir systems operation. Furthermore, the results of these algorithms were compared with nine other metaheuristic algorithms developed by several researchers. Sensitivity analysis was performed to determine the appropriate values of the algorithms parameters. The statistical indices of R2, RMSE, MAE, MSE, NMSE, MAPE, and Willmott's index of agreement were used to compare the algorithms performance. The results showed that the MSA was the superior algorithm in solving all benchmark functions in terms of obtaining the optimal value and saving the CPU usage. ICA and HS were placed in the next orders, respectively. It was found that by increasing the dimensions of the problem, the performance of ICA and HS dropped but the MSA has still performed extraordinary. In addition, the minimum CPU usage and the best solutions for optimal operation of four-reservoir system were obtained by MSA algorithm with values of (269.7s and 308.83) which are very close to the global optimum solution. Corresponding values for ICA and HS were (486.73, 306.47) and (638.61s, 264.61) respectively, which put them in the next ranks. Similar results were observed for ten-reservoir system; the CPU time and optimal value obtained by MSA were (722.5s, 1,195.58) while for ICA and HS were (1,421.62s, 1,136.22) and (1,963.41s, 1,060.76), respectively. The values of R2 and RMSE achieved by MSA were (0.951, 0.528) and (0.985, 0.521) for 4-reservoir and 10-reservoir systems which demonstrated the outstanding performance of this algorithm in optimal operation of multi-reservoir systems. In a general comparison, it was concluded that among the twelve investigated algorithms, MSA was the best, and it is recommended as a robust promising tool in optimal operation of multi-reservoir systems.

Solving Economical Operation Problem of Basin Cascade Plants Based on Bi-Level Coupling Genetic Algorithm

2013 ◽  
Vol 357-360 ◽  
pp. 2921-2924 ◽  
Author(s):  
Xiao Lu Yin ◽  
Yang Yang
Keyword(s):  
Genetic Algorithm ◽  
Load Distribution ◽  
Optimal Operation ◽  
Optimal Decision ◽  
System Management ◽  
Improved Genetic Algorithm ◽  
Hydropower Plants ◽  
Hydropower System ◽  
Upper Level ◽  
Coupling Algorithm

Bi-level coupling algorithm which is based on the improved genetic algorithm was proposed in this paper. Its upper-level module was designed to solve the load optimal distributing problem among the cascade hydropower plants and its lower-level module was designed to solve the generator group optimal-uniting problem among the generators in a hydropower system. This algorithm has advantages in getting the optimal operation schemes of the load distribution, as well as the generator group unitization at the same time. By using a practical basin cascade plants as an example, the results show that the software can carry on rational and optimal decision to the producing operation schemes. The Bi-level coupling algorithm has higher practical value in hydropower system management.

scholarly journals Optimisation of Multipurpose Reservoir Operation by coupling SWAT and Genetic Algorithm for Optimal Operating Policy (Case Study: Ganga River basin)

2018 ◽  
Author(s):  
Jatin Anand ◽  
A K Gosain ◽  
R Khosa
Keyword(s):  
Genetic Algorithm ◽  
Water Resources ◽  
Water Resources Management ◽  
Reservoir Operation ◽  
Integrated Water Resources Management ◽  
Optimal Operating ◽  
Resources Management ◽  
Decision Variables ◽  
Operating Policies

Reservoirs are recognized as one of the most efficient infrastructure components in integrated water resources management and development. At present, with the ongoing advancement of social economy and requirement of water, the water resources shortage problem has worsened, and the operation of reservoirs, in terms of consumption of flood water, has become significantly important. Reservoirs perform both regulation of flood and integrated water resources management, in which the flood limited water level is considered as the most important parameter for trade-off between regulation of flood and conservation. To achieve optimal operating policies for reservoirs, large numbers of simulation and optimization models have been developed in the course of recent decades, which vary notably in their applications and working. Since each model has their own limitations, the determination of fitting model for derivation of reservoir operating policies is challenging and most often there is always a scope for further improvement as the selection of model depends on availability of data. Subsequently, assessment and evaluation associated with the operation of reservoir stays conventional. In the present study, the Soil and Water Assessment Tool (SWAT) models and a Genetic Algorithm model has been developed and applied to two reservoirs in Ganga River basin, India to derive the optimal operational policies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. As a result, a simulation-based optimization model was recommended for optimal reservoir operation, such as allocation of water, flood regulation, hydropower generation, irrigation demands and navigation and e-flows using a definite combination of decision variables. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on simulated result, in the present case study it is concluded that GA-derived policies are promising and competitive and can be effectively used operation of the reservoir.

scholarly journals Dynamic Feasible Region Genetic Algorithm for Optimal Operation of a Multi-Reservoir System

Energies ◽  
2012 ◽  
Vol 5 (8) ◽  
pp. 2894-2910 ◽  
Author(s):  
Bin Xu ◽  
Ping-An Zhong ◽  
Xinyu Wan ◽  
Weiguo Zhang ◽  
Xuan Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimal Operation ◽  
Feasible Region ◽  
Reservoir System

scholarly journals Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Reza Sharifi ◽  
Saeid Akbarifard ◽  
Kourosh Qaderi ◽  
Mohamad Reza Madadi
Keyword(s):  
Objective Function ◽  
Optimization Algorithm ◽  
Optimal Operation ◽  
Engineering Problem ◽  
Performance Criteria ◽  
Reliable Technique ◽  
Reservoir System ◽  
Dam Reservoirs ◽  
Reservoir Systems ◽  
Swarm Algorithm

AbstractDeriving optimal operation policies for multi-reservoir systems is a complex engineering problem. It is necessary to employ a reliable technique to efficiently solving such complex problems. In this study, five recently-introduced robust evolutionary algorithms (EAs) of Harris hawks optimization algorithm (HHO), seagull optimization algorithm (SOA), sooty tern optimization algorithm (STOA), tunicate swarm algorithm (TSA) and moth swarm algorithm (MSA) were employed, for the first time, to optimal operation of Halilrood multi-reservoir system. This system includes three dams with parallel and series arrangements simultaneously. The results of mentioned algorithms were compared with two well-known methods of genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The objective function of the optimization model was defined as the minimization of total deficit over 223 months of reservoirs operation. Four performance criteria of reliability, resilience, vulnerability and sustainability were used to compare the algorithms’ efficiency in optimization of this multi-reservoir operation. It was observed that the MSA algorithm with the best value of objective function (6.96), the shortest CPU run-time (6738 s) and the fastest convergence rate (< 2000 iterations) was the superior algorithm, and the HHO algorithm placed in the next rank. The GA, and the PSO were placed in the middle ranks and the SOA, and the STOA placed in the lowest ranks. Furthermore, the comparison of utilized algorithms in terms of sustainability index indicated the higher performance of the MSA in generating the best operation scenarios for the Halilrood multi-reservoir system. The application of robust EAs, notably the MSA algorithm, to improve the operation policies of multi-reservoir systems is strongly recommended to water resources managers and decision-makers.

scholarly journals Verification of FPA and PSO algorithms for rule curve extraction and optimization of single- and multi-reservoir systems' operations considering their specific purposes

2020 ◽  
Author(s):  
Omid Bozorg-Haddad ◽  
Marzie Azad ◽  
Elahe Fallah-Mehdipour ◽  
Mohammad Delpasand ◽  
Xuefeng Chu
Keyword(s):  
Water Supply ◽  
Heuristic Algorithms ◽  
Optimal Operation ◽  
Optimal Solutions ◽  
Hydropower Generation ◽  
System Operation ◽  
Calculation Time ◽  
Reservoir System ◽  
Reservoir Systems ◽  
Rule Curve

Abstract The optimal operation of reservoirs is known as a complex issue in water resources management, which requires consideration of numerous variables (such as downstream water demand and power generation). For this optimization, researchers have used evolutionary and meta-heuristic algorithms, which are generally inspired by nature. These algorithms have been developed to achieve optimal/near-optimal solutions by a smaller number of function evaluations with less calculation time. In this research, the flower pollination algorithm (FPA) was used to optimize: (1) Aidoghmoush single-reservoir system operation for agricultural water supply, (2) Bazoft single-reservoir system operation for hydropower generation, (3) multi-reservoir system operation of Karun 5, Karun 4, and Bazoft, and (4) Bazoft single-reservoir system for rule curve extraction. To demonstrate the effectiveness of the FPA, it was first applied to solve the mathematical test functions, and then used to determine optimal operations of the reservoir systems with the purposes of downstream water supply and hydropower generation. In addition, the FPA was compared with the particle swarm optimization (PSO) algorithm and the non-linear programming (NLP) method. The results for the Aidoghmoush single-reservoir system showed that the best FPA solution was similar to the NLP solution, while the best PSO solution was about 0.2% different from the NLP solution. The best values of the objective function of the PSO were approximately 3.5 times, 28%, and 43% worse than those of the FPA for the Bazoft single-reservoir system for hydropower generation, the multi-reservoir system, and the Bazoft single-reservoir system for rule curve extraction, respectively. The FPA outperformed the PSO in finding the optimal solutions. Overall, FPA is one of the new evolutionary algorithms, which is capable of determining better (closer to the ideal solution) objective functions, decreasing the calculation time, simplifying the problem, and providing better solutions for decision makers.

Optimal Operation of Multi-Reservoir System Based-On Cuckoo Search Algorithm

2015 ◽  
Vol 29 (15) ◽  
pp. 5671-5687 ◽  
Author(s):  
Bo Ming ◽  
Jian-xia Chang ◽  
Qiang Huang ◽  
Yi-min Wang ◽  
Sheng-zhi Huang
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimal Operation ◽  
Reservoir System
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