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Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

Author

Listed:
  • Mohammad Abdullah Almubaidin

    (Universiti Tenaga Nasional (UNITEN)
    Khawarizmi University Technical College)

  • Ali Najah Ahmed

    (Sunway University
    Universiti Tenaga Nasional (UNITEN))

  • Lariyah Mohd Sidek

    (Universiti Tenaga Nasional (UNITEN))

  • Khlaif Abdul Hakim AL-Assifeh

    (Arab potash company)

  • Ahmed El-Shafie

    (University of Malaya (UM)
    United Arab Emirates University)

Abstract

Recently, there has been a growing interest in employing optimization techniques to ascertain the most efficient operation of reservoirs. This involves their application to various facets of the reservoir operating system, particularly in determining optimal rule curves. This study delves into the exploration of different algorithms, including Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), Teaching Learning-Based Optimization (TLBO), and Harmony Search (HS). Each algorithm was integrated into a reservoir simulation model, focusing on finding optimal rule curves for the Mujib reservoir in Jordan from 2004 to 2019. The primary objective was to evaluate the long-term impact of water shortages and excess releases on the Mujib reservoir. Furthermore, the study aimed to determine the effects of water demand management by reducing it by 10%, 20%, and 30%. The results revealed that the used algorithms effectively mitigated water shortages and excess releases compared to the current operational strategy. Notably, the Teaching Learning-Based Optimization (TLBO) algorithm yielded the most favorable outcomes, reducing the frequency and average of water shortages to 55.09% and 56.26%, respectively. Additionally, it curtailed the frequency and average of excess releases to 63.16% and 73.31%, respectively.

Suggested Citation

  • Mohammad Abdullah Almubaidin & Ali Najah Ahmed & Lariyah Mohd Sidek & Khlaif Abdul Hakim AL-Assifeh & Ahmed El-Shafie, 2024. "Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(4), pages 1207-1223, March.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:4:d:10.1007_s11269-023-03716-5
    DOI: 10.1007/s11269-023-03716-5
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    References listed on IDEAS

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    1. Pedro Beça & António C. Rodrigues & João P. Nunes & Paulo Diogo & Babar Mujtaba, 2023. "Optimizing Reservoir Water Management in a Changing Climate," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3423-3437, July.
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    4. Hamid Bashiri-Atrabi & Kourosh Qaderi & David Rheinheimer & Erfaneh Sharifi, 2015. "Application of Harmony Search Algorithm to Reservoir Operation Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(15), pages 5729-5748, December.
    5. Vijendra Kumar & S. M. Yadav, 2018. "Optimization of Reservoir Operation with a New Approach in Evolutionary Computation Using TLBO Algorithm and Jaya Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4375-4391, October.
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