Optimal Operation of Multireservoir Systems by Enhanced Water Cycle Algorithm

2019 ◽  
Vol 7 (1) ◽  
pp. 27-43
Author(s):  
Yanjun Kong ◽  
Yadong Mei ◽  
Weinan Li ◽  
Ben Yue ◽  
Xianxun Wang
Keyword(s):  
Water Cycle ◽  
Water System ◽  
System Size ◽  
Optimal Operation ◽  
Test Functions ◽  
Water Cycle Algorithm ◽  
Adaptive Parameter ◽  
Fitness Value ◽  
Better Than

In this article, an enhanced water cycle algorithm (EWCA) is proposed and applied to optimize the operation of multireservoir systems. Three improvements have been made to the water cycle algorithm (WCA). They refer to high-quality initial solutions obtained by the chaos-based method, balancing of exploration of streams using a dynamic adaptive parameter, and dynamic variation of sub-water system size using the fitness value of rivers. For the purpose of verifying the improvements, three typical benchmark functions were selected as test functions. It has shown that EWCA performs better than WCA and water cycle algorithm with evaporation rate (ER-WCA). And then these three algorithms were also applied to optimize the operation of a multireservoir system with complex constrains as the case study. By comparing the results, it is found that the EWCA has higher ability to find a feasible solution in a narrow searching space. The effectiveness of the improvements is confirmed.

scholarly journals Sampling/stochastic dynamic programming for optimal operation of multi-purpose reservoirs using artificial neural network-based ensemble streamflow predictions

2013 ◽  
Vol 16 (4) ◽  
pp. 907-921 ◽  
Author(s):  
Sedigheh Anvari ◽  
S. Jamshid Mousavi ◽  
Saeed Morid
Keyword(s):  
Dynamic Programming ◽  
Arid Regions ◽  
Water Reservoir ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Reservoir System ◽  
Artificial Neural ◽  
Increasing Demand ◽  
Better Than

Due to limited water resources and the increasing demand for agricultural products, it is significantly important to operate surface water reservoirs optimally, especially those located in arid and semi-arid regions. This paper investigates uncertainty-based optimal operation of a multi-purpose water reservoir system by using four optimization models. The models include dynamic programming (DP), stochastic DP (SDP) with inflow classification (SDP/Class), SDP with inflow scenarios (SDP/Scenario), and sampling SDP (SSDP) with historical scenarios (SSDP/Hist). The performance of the models was tested in Zayandeh-Rud Reservoir system in Iran by evaluating how their release policies perform in a simulation phase. While the SDP approaches were better than the DP approach, the SSDP/Hist model outperformed the other SDP models. We also assessed the effect of ensemble streamflow predictions (ESPs) that were generated by artificial neural networks on the performance of SSDP/Hist. Application of the models to the Zayandeh-Rud case study demonstrated that SSDP in combination with ESPs and the K-means technique, which was used to cluster a large number of ESPs, could be a promising approach for real-time reservoir operation.

Optimal operation of multi-reservoirs by water cycle algorithm

2018 ◽  
Vol 171 (4) ◽  
pp. 179-190 ◽  
Author(s):  
Kourosh Qaderi ◽  
Saeid Akbarifard ◽  
Mohamad Reza Madadi ◽  
Bahram Bakhtiari
Keyword(s):  
Water Cycle ◽  
Optimal Operation ◽  
Water Cycle Algorithm

The Hydroarchaeological Method: A Case Study at the Maya Site of Palenque

2012 ◽  
Vol 23 (1) ◽  
pp. 29-50 ◽  
Author(s):  
Kirk D. French ◽  
Christopher J. Duffy ◽  
Gopal Bhatt
Keyword(s):  
Climate Changes ◽  
Water Cycle ◽  
Climate Modeling ◽  
Regional Climate ◽  
Water System ◽  
Archaeological Site ◽  
Convincing Evidence ◽  
Watershed Land Use ◽  
Maya Site

AbstractThis research consists mainly of introducing the hydroarchaeological method, especially as related to issues of drought. The article outlines how this multidisciplinary method can provide insights into the success and failures of an archaeological site, in this case the Maya site of Palenque. We also detail convincing evidence that shows that the Maya of Palenque did not leave their city because of deficiencies of water, as some paleoclimatologists and archaeologists have asserted. The first logical step toward understanding any settlement’s water system is to use basic hydrologic methods and theory and to understand the local watershed. There is great potential for watershed-climate modeling in developing plausible scenarios of water use and supply and of the effect of extreme conditions (flood and drought), all of which cannot be fully represented by atmosphere-based climate and weather projections. The research demonstrates how the local watershed, land-use, and ecological conditions interact with regional climate changes. The archaeological implications for this noninvasive “virtual” method are many, including detecting periods of stress within a community, estimating population by developing caps based on the availability of water, and understanding settlement patterns, as well as assisting present local populations in understanding their water cycle.

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

2017 ◽  
Vol 19 (4) ◽  
pp. 507-521 ◽  
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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