Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

Mohammad Abdullah Almubaidin (), Ali Najah Ahmed (), Lariyah Mohd Sidek (), Khlaif Abdul Hakim AL-Assifeh () and Ahmed El-Shafie ()
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Mohammad Abdullah Almubaidin: Universiti Tenaga Nasional (UNITEN)
Ali Najah Ahmed: Sunway University
Lariyah Mohd Sidek: Universiti Tenaga Nasional (UNITEN)
Khlaif Abdul Hakim AL-Assifeh: Arab potash company
Ahmed El-Shafie: University of Malaya (UM)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 4, No 1, 1207-1223

Abstract: 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.

Keywords: Reservoir rule curves; Standard operating policy; Simulation model; Meta-heuristic algorithm; Optimization techniques (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-023-03716-5

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