New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems

Mohammad Ehteram, Suhana Binti Koting, Haitham Abdulmohsin Afan, Nuruol Syuhadaa Mohd, Marlinda Abdul Malek, Ali Najah Ahmed, Amr H. El-shafie, Chiu Chuen Onn, Sai Hin Lai, Ahmed El-Shafie

Research output: Contribution to journalArticle

Abstract

In recent decades, solving complex real-life optimization problems has attracted the full attention of researchers. Dam and reservoir operation rules are considered one of the most complicated optimization engineering problems. In fact, the operation rules of dams and reservoirs are multisystematic and highly stochastic and have highly nonlinear system constraints due to the direct influence of environmental conditions: Therefore, these rules are considered highly complex optimization problems. Recently, metaheuristic methods inferred from nature have been broadly utilized to elucidate the way optimal solutions are provided for several complex optimization engineering applications, and these methods have achieved interesting results. The major advantage of these metaheuristic methods over conventional methods is the unnecessity to identify a particular initial condition, convexity, continuity, or differentiability. The present study investigated the potential of using a new metaheuristic method (i.e., the crow algorithm (CA)) to provide optimal operations for multireservoir systems, with the aim of optimally improving hydropower generation. A multireservoir system in China was considered to examine the performance of the proposed optimization algorithm for several operation scenarios. The results obtained the average hydropower generation by considering all examined operation scenarios based on the operation rule achieved using the CA, which outperformed the other metaheuristic methods. In addition, compared to other metaheuristic methods, the proposed CA lessened the time required to search for the optimal solution. In conclusion, the proposed CA has high potential for achieving optimal solutions to complex optimization problems associated with dam and reservoir operations.

Original languageEnglish
Article number2280
JournalApplied Sciences (Switzerland)
Volume9
Issue number11
DOIs
Publication statusPublished - 01 Jun 2019

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Evolutionary algorithms
optimization
dams
Dams
engineering
convexity
Nonlinear systems
nonlinear systems
continuity
China

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Cite this

Ehteram, Mohammad ; Koting, Suhana Binti ; Afan, Haitham Abdulmohsin ; Mohd, Nuruol Syuhadaa ; Abdul Malek, Marlinda ; Ahmed, Ali Najah ; El-shafie, Amr H. ; Onn, Chiu Chuen ; Lai, Sai Hin ; El-Shafie, Ahmed. / New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems. In: Applied Sciences (Switzerland). 2019 ; Vol. 9, No. 11.
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Ehteram, M, Koting, SB, Afan, HA, Mohd, NS, Abdul Malek, M, Ahmed, AN, El-shafie, AH, Onn, CC, Lai, SH & El-Shafie, A 2019, 'New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems', Applied Sciences (Switzerland), vol. 9, no. 11, 2280. https://doi.org/10.3390/app9112280

New evolutionary algorithm for optimizing hydropower generation considering multireservoir systems. / Ehteram, Mohammad; Koting, Suhana Binti; Afan, Haitham Abdulmohsin; Mohd, Nuruol Syuhadaa; Abdul Malek, Marlinda; Ahmed, Ali Najah; El-shafie, Amr H.; Onn, Chiu Chuen; Lai, Sai Hin; El-Shafie, Ahmed.

In: Applied Sciences (Switzerland), Vol. 9, No. 11, 2280, 01.06.2019.

Research output: Contribution to journalArticle

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AU - Ehteram, Mohammad

AU - Koting, Suhana Binti

AU - Afan, Haitham Abdulmohsin

AU - Mohd, Nuruol Syuhadaa

AU - Abdul Malek, Marlinda

AU - Ahmed, Ali Najah

AU - El-shafie, Amr H.

AU - Onn, Chiu Chuen

AU - Lai, Sai Hin

AU - El-Shafie, Ahmed

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