Coupled Operating Rules for Optimal Operation of Multi-Reservoir Systems

2017 ◽  
Vol 31 (14) ◽  
pp. 4505-4520 ◽  
Author(s):  
Seyed Mohammad Ashrafi ◽  
Alireza Borhani Dariane
Keyword(s):  
Optimal Operation ◽  
Operating Rules ◽  
Reservoir Systems

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

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

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

Energies ◽  
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.

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.

Optimal Operation of Multi-reservoir Systems Considering Time-lags of Flood Routing

2015 ◽  
Vol 30 (2) ◽  
pp. 523-540 ◽  
Author(s):  
Wang Zhang ◽  
Pan Liu ◽  
Xizhen Chen ◽  
Li Wang ◽  
Xueshan Ai ◽  
...  
Keyword(s):  
Optimal Operation ◽  
Flood Routing ◽  
Time Lags ◽  
Reservoir Systems

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 Optimization of Reservoir Operation Using Cuckoo Search Algorithm: Example of Adiguzel Dam, Denizli, Turkey

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Mutlu Yasar
Keyword(s):  
Energy Production ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Complex Problem ◽  
Cuckoo Search Algorithm ◽  
Optimal Operation ◽  
Operating Rules ◽  
Operation Rule ◽  
Production Regime

The Adiguzel Dam is located in Denizli in the western part of Turkey. It was built for irrigation purposes, but it also produces energy at the same time. The dam’s energy-production regime is not regular since there are no reservoir-operating rules. Thus, this study develops a reservoir optimization rule to generate a corresponding gain in energy production. It is well known that operating a reservoir is a complex problem that depends on many parameters such as inflow, storage capacity, water elevation, tailwater elevation, and evaporation. Therefore, in order to optimize energy production, there is a need to use heuristic algorithms such as the Cuckoo Search (CS). This study develops a CS algorithm-based solution to optimize the reservoir’s operational system and generate an optimal operation rule curve. Results show that the CS algorithm improves the system operation, and the energy production will be increased by about 10% to a value of 160000 MWh with a corresponding economic gain of about $12 × 106in total for 183 months.

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.

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