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

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.

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

Water Science & Technology Water Supply ◽

10.2166/ws.2019.005 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1396-1404 ◽

Cited By ~ 4

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.

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

Water Science & Technology Water Supply ◽

10.2166/ws.2017.217 ◽

2017 ◽

Vol 18 (4) ◽

pp. 1484-1496 ◽

Cited By ~ 5

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.

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

Scientific Reports ◽

10.1038/s41598-021-95159-4 ◽

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.

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

Water Science & Technology Water Supply ◽

10.2166/ws.2020.274 ◽

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.

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Application of Cellular Automata in Bi-Objective Operation of Multi Reservoir Systems

Water ◽

10.3390/w13192740 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2740

Author(s):

Mohammad Hadi Afshar ◽

Reza Hajiabadi

Keyword(s):

Simulated Annealing ◽

Cellular Automata ◽

Water Resource Management ◽

Computational Cost ◽

Search Space ◽

Optimal Operation ◽

Reservoir System ◽

Conflicting Objectives ◽

Reservoir Systems ◽

Southwestern Iran

Optimal operation of multi-reservoir systems is one the most challenging problems in water resource management due to their multi-objective nature and time-consuming solving process. In this paper, Multi-Reservoir Parallel Cellular Automata-Simulated Annealing (MPCA-SA), a hybrid method based on cellular automata and simulated annealing is presented for solving bi-objective operations of multi-reservoir systems problems. The problem considers the bi-objective operation of a multi-reservoir system with the two conflicting objectives of water supply and hydropower generation. The MPCA-SA method uses two single-objective cellular automata acting in parallel to explore the problem search space and find the optimal solutions based on the probabilistic interaction with each other. Bi-objective operation of the Dez-Gotvand-Masjed Soleyman three-reservoir system, as a real-world system in southwestern Iran for a period of 60 months, is considered in order to evaluate the ability of the proposed method. In addition, a Non-dominated Sorting Genetic Algorithm (NSGAII) is also used to solve the problems and the results are compared with those of MPCA-SA, indicating the capabilities of the proposed MPCA-SA method. The results show that the MPCA-SA method is able to produce solutions comparable to those of NSGAII with a much-reduced computational cost equal to 1.2% of that required by the NSGAII, emphasizing the efficiency and practicality of the proposed method.

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Optimal operation of reservoir systems with the symbiotic organisms search (SOS) algorithm

Journal of Hydroinformatics ◽

10.2166/hydro.2017.085 ◽

2017 ◽

Vol 19 (4) ◽

pp. 507-521 ◽

Cited By ~ 9

Author(s):

Omid Bozorg-Haddad ◽

Ali Azarnivand ◽

Seyed-Mohammad Hosseini-Moghari ◽

Hugo A. Loáiciga

Keyword(s):

Optimization Problem ◽

Water Cycle ◽

Optimal Solution ◽

Global Optimum ◽

Optimal Operation ◽

Symbiotic Organisms Search ◽

Water Cycle Algorithm ◽

Reservoir Optimization ◽

Reservoir Systems ◽

Symbiotic Organisms

This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.

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Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

Energy Reports ◽

10.1016/j.egyr.2021.06.008 ◽

2021 ◽

Vol 7 ◽

pp. 3703-3725

Author(s):

Mohammad Ehteram ◽

Fatemeh Barzegari Banadkooki ◽

Chow Ming Fai ◽

Mohsen Moslemzadeh ◽

Michelle Sapitang ◽

...

Keyword(s):

Power Generation ◽

Optimization Algorithm ◽

Optimal Operation ◽

Reservoir Systems

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Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽

10.3390/en14030625 ◽

2021 ◽

Vol 14 (3) ◽

pp. 625

Author(s):

Xinyu Wu ◽

Rui Guo ◽

Xilong Cheng ◽

Chuntian Cheng

Keyword(s):

Dynamic Programming ◽

Stochastic Dynamic Programming ◽

Large Scale ◽

Simulation Optimization ◽

Optimal Operation ◽

Stochastic Dynamic ◽

Operation Model ◽

Two Stage ◽

Operation Rules ◽

Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

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