scholarly journals Evaluation of contemporary evolutionary algorithms for optimization in reservoir operation and water supply

2017 ◽  
Vol 67 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Mohammad Ehteram ◽  
Hojat Karami ◽  
Sayed-Farhad Mousavi ◽  
Saeed Farzin ◽  
Ozgur Kisi
Keyword(s):  
Evolutionary Algorithms ◽  
Water Supply ◽  
Reservoir Operation

scholarly journals Optimization of Water-Supply and Hydropower Reservoir Operation Using the Charged System Search Algorithm

Hydrology ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 5 ◽  
Author(s):  
Behzad Asadieh ◽  
Abbas Afshar
Keyword(s):  
Water Resources ◽  
Water Supply ◽  
Water Resources Management ◽  
Reservoir Operation ◽  
Big Bang ◽  
Alternative Methods ◽  
Resources Management ◽  
Charged System Search ◽  
Non Linear ◽  
Charged System

The Charged System Search (CSS) metaheuristic algorithm is introduced to the field of water resources management and applied to derive water-supply and hydro-power operating policies for a large-scale real-world reservoir system. The optimum algorithm parameters for each reservoir operation problems are also obtained via a tuning procedure. The CSS algorithm is a metaheuristic optimization method inspired by the governing laws of electrostatics in physics and motion from the Newtonian mechanics. In this study, the CSS algorithm’s performance has been tested with benchmark problems, consisting of highly non-linear constrained and/or unconstrained real-valued mathematical models, such as the Ackley’s function and Fletcher–Powell function. The CSS algorithm is then used to optimally solve the water-supply and hydropower operation of “Dez” reservoir in southern Iran over three different operation periods of 60, 240, and 480 months, and the results are presented and compared with those obtained by other available optimization approaches including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Constrained Big Bang–Big Crunch (CBB–BC) algorithm, as well as those obtained by gradient-based Non-Linear Programming (NLP) approach. The results demonstrate the robustness and superiority of the CSS algorithm in solving long term reservoir operation problems, compared to alternative methods. The CSS algorithm is used for the first time in the field of water resources management, and proves to be a robust, accurate, and fast convergent method in handling complex problems in this filed. The application of this approach in other water management problems such as multi-reservoir operation and conjunctive surface/ground water resources management remains to be studied.

Real-Time Reservoir Operation by Tree-Based Model Predictive Control Including Forecast Uncertainty

2020 ◽  
Author(s):  
Gokcen Uysal ◽  
Rodolfo-Alvarado Montero ◽  
Dirk Schwanenberg ◽  
Aynur Sensoy
Keyword(s):  
Water Supply ◽  
Real Time ◽  
Predictive Control ◽  
Reservoir Operation ◽  
Lead Time ◽  
Flood Control ◽  
Closed Loop ◽  
Multi Stage ◽  
Streamflow Forecasts ◽  
Related Technique

<p>Streamflow forecasts include uncertainties related with initial conditions, model forcings, hydrological model structure and parameters. Ensemble streamflow forecasts can capture forecast uncertainties by having spread forecast members. Integration of these forecast members into real-time operational decision models which deals with different objectives such as flood control, water supply or energy production are still rare. This study aims to use ensemble streamflows as input of the recurrent reservoir operation problem which can incorporate (i) forecast uncertainty, (ii) forecasts with a higher lead-time and (iii) a higher stability. A related technique for decision making is multi-stage stochastic optimization using scenario trees, referred to as Tree-based Model Predictive Control (TB-MPC). This approach reduces the number of ensemble members by its tree generation algorithms using all trajectories and then proper problem formulation is set by Multi-Stage Stochastic Programming. The method is relatively new in reservoir operation, especially closed-loop hindcasting experiments and its assessment is quite rare in the literature. The aim of this study is to set a TB-MPC based real-time reservoir operation with hindcasting experiments. To that end, first hourly deterministic streamflows having one single member are produced using an observed flood hydrograph. Deterministic forecasts are tested with conventional deterministic optimization setup. Secondly, hourly ensemble streamflow forecasts having a lead-time up to 48 hours are produced by a novel approach which explicitly presents dynamic uncertainty evolution. Produced ensemble members are directly provided to input to related technique. Uncertainty becomes much larger when managing small basins and small rivers. Thus, the methodology is applied to the Yuvacik dam reservoir, fed by a catchment area of 258 km<sup>2</sup> and located in Turkey, owing to its challenging flood control and water supply operation due to downstream flow constraints. According to the results, stochastic optimization outperforms conventional counterpart by considering uncertainty in terms of flood metrics without discarding water supply purposes. The closed-loop hindcasting experiment scenarios demonstrate the robustness of the system developed against biased information. In conclusion, ensemble streamflows produced from single member can be employed to TB-MPC for better real-time management of a reservoir control system.</p>

scholarly journals DEVELOPMENT OF RESERVOIR OPERATION FOR BATANG AI HYDROELECTRIC PROJECT

2016 ◽  
Vol 7 (2) ◽  
pp. 45-49
Author(s):  
Kiu Kwong Kiat ◽  
Frederik Josep Putuhena
Keyword(s):  
Power Generation ◽  
Water Supply ◽  
Power Supply ◽  
Water Demand ◽  
Reservoir Operation ◽  
Maximum Capacity ◽  
Hydroelectric Project ◽  
Continuous Power

Batang Ai Hydroelectric Project is meant to generate electricity. The Batang Ai Dam has a maximum capacity to generate around 108MW when it is fully operational. Efficient reservoir operation should be carried out very carefully in order to provide reliable water supply for power generation and water demand. The research of this project is to review the Batang Ai Dam storage conservation for the reservoir operation by using storage conservation simulation. The simulations are carried out with various outflow conditions using the principle of the storage equation. Lastly, the results from the simulation show that with proper regulation of the reservoir outflow, the Batang Ai Hydroelectric Project reliability to provide continuous power supply without shortage in water supply is assured.

scholarly journals Reducing the conflict of interest in the optimal operation of reservoirs by linking mesohabitat hydraulic modeling and metaheuristic optimization

2021 ◽  
Author(s):  
Mahdi Sedighkia ◽  
Bithin Datta ◽  
Asghar Abdoli
Keyword(s):  
Environmental Impact ◽  
Water Supply ◽  
Differential Evolution ◽  
Environmental Impacts ◽  
Reservoir Operation ◽  
Differential Evolution Algorithm ◽  
Hydraulic Modeling ◽  
Optimal Operation ◽  
Metaheuristic Optimization

Abstract The present study proposes a novel framework to optimize the reservoir operation through linking mesohabitat hydraulic modeling and metaheuristic optimization to mitigate environmental impact at downstream of the reservoir. Environmental impact function was developed by mesohabitat hydraulic simulation. Then, the developed function was utilized in the structure of the reservoir operation optimization. Different metaheuristic algorithms including practice swarm optimization, invasive weed optimization, differential evolution and biogeography-based algorithm were used to optimize reservoir operation. Root mean square error (RMSE) and reliability index were utilized to measure the performance of algorithms. Based on the results in the case study, the proposed method is robust for mitigating downstream environmental impacts and sustaining water supply by the reservoir. RMSE for mesohabitats is 8% that indicates the robustness of proposed method to mitigate environmental impacts at downstream. It seems that providing environmental requirements might reduce the reliability of water supply considerably. Differential evolution algorithm is the best method to optimize reservoir operation in the case study.

scholarly journals Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms

Data in Brief ◽  
2020 ◽  
Vol 29 ◽  
pp. 105048 ◽  
Author(s):  
Saeid Akbarifard ◽  
Mohammad Reza Sharifi ◽  
Kourosh Qaderi
Keyword(s):  
Evolutionary Algorithms ◽  
Reservoir Operation ◽  
Hydropower Reservoir

scholarly journals Reservoir operation based on evolutionary algorithms and multi-criteria decision-making under climate change and uncertainty

2018 ◽  
Vol 20 (2) ◽  
pp. 332-355 ◽  
Author(s):  
Mohammad Ehteram ◽  
Sayed Farhad Mousavi ◽  
Hojat Karami ◽  
Saeed Farzin ◽  
Vijay P. Singh ◽  
...  
Keyword(s):  
Climate Change ◽  
Evolutionary Algorithms ◽  
Reservoir Operation ◽  
High Reliability ◽  
Bat Algorithm ◽  
Particle Swarm Algorithm ◽  
Base Period ◽  
Future Period ◽  
Change Models ◽  
The Future

Abstract This study investigated reservoir operation under climate change for a base period (1981–2000) and future period (2011–2030). Different climate change models, based on A2 scenario, were used and the HAD-CM3 model, considering uncertainty, among other climate change models was found to be the best model. For the Dez basin in Iran, considered as a case study, the climate change models predicted increasing temperature from 1.16 to 2.5°C and decreasing precipitation for the future period. Also, runoff volume for the basin would decrease and irrigation demand for the downstream consumption would increase for the future period. A hybrid framework (optimization-climate change) was used for reservoir operation and the bat algorithm was used for minimization of irrigation deficit. A genetic algorithm and a particle swarm algorithm were selected for comparison with the bat algorithm. The reliability, resiliency, and vulnerability indices, based on a multi-criteria model, were used to select the base method for reservoir operation. Results showed the volume of water to be released for the future period, based on all evolutionary algorithms used, was less than for the base period, and the bat algorithm with high-reliability index and low vulnerability index performed better among other evolutionary algorithms.

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