Multi-objective operation optimization of hydropower reservoirs by MOPSO Case study: Karun Dam 5

Document Type : Technical Note (5 pages)

Authors

1 M.S. Graduated, Water Sciences and Engineering Department, Faculty of Engineering and Technology, Imam Khomeini International University, Qazvin, Iran.

2 Assistant Professor of Water Sciences and Engineering Department, Faculty of Engineering and Technology, Khomeini International University, Qazvin, Iran.

Abstract

Near the most of real-world decision-making issues, especially in the water resource management area, are multi-objective issues that are taken based on different and conflicting goals. Due to the wide range of application of these issues, different models have been proposed to solve them, NSGA-II and MOPSO are the most important of these multi-objective optimization models. The purpose of this study is to compare the performance of NSGA-II and MOPSO algorithms in solving multi-objective optimal operation of a hydropower reservoir. Due to the fact that the hydropower reservoirs are involved in providing the peak load of the network electricity, a neural network to predict daily energy prices in peak hours was developed initially, then the results were used to optimize the multi-objective operation of Karun 5 Dam reservoir, includes two goals of maximizing annual income and maximizing minimum daily energy production. Although the run time of the NSGA-II method is about twice as high as the MOPSO, the precision of its results is 20% better for both purposes than MOPSO.

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References

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Iran-Water Resources Research
Volume 15, Issue 1
February 2019
Pages 361-364

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History

  • Receive Date: 10 February 2018
  • Revise Date: 03 November 2018
  • Accept Date: 04 November 2018

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