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Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms

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 e...

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Main Authors: Almubaidin, Mohammad Abdullah, Ahmed, Ali Najah, Sidek, Lariyah Mohd, AL-Assifeh, Khlaif Abdul Hakim, El-Shafie, Ahmed
Format: Article
Published: Springer Verlag 2024
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/45656/
https://doi.org/10.1007/s11269-023-03716-5
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spelling my.um.eprints.456562024-11-07T04:00:38Z http://eprints.um.edu.my/45656/ Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms Almubaidin, Mohammad Abdullah Ahmed, Ali Najah Sidek, Lariyah Mohd AL-Assifeh, Khlaif Abdul Hakim El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) 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. Springer Verlag 2024-03 Article PeerReviewed Almubaidin, Mohammad Abdullah and Ahmed, Ali Najah and Sidek, Lariyah Mohd and AL-Assifeh, Khlaif Abdul Hakim and El-Shafie, Ahmed (2024) Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms. Water Resources Management, 38 (4). pp. 1207-1223. ISSN 0920-4741, DOI https://doi.org/10.1007/s11269-023-03716-5 <https://doi.org/10.1007/s11269-023-03716-5>. https://doi.org/10.1007/s11269-023-03716-5 10.1007/s11269-023-03716-5
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Almubaidin, Mohammad Abdullah
Ahmed, Ali Najah
Sidek, Lariyah Mohd
AL-Assifeh, Khlaif Abdul Hakim
El-Shafie, Ahmed
Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
description 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.
format Article
author Almubaidin, Mohammad Abdullah
Ahmed, Ali Najah
Sidek, Lariyah Mohd
AL-Assifeh, Khlaif Abdul Hakim
El-Shafie, Ahmed
author_facet Almubaidin, Mohammad Abdullah
Ahmed, Ali Najah
Sidek, Lariyah Mohd
AL-Assifeh, Khlaif Abdul Hakim
El-Shafie, Ahmed
author_sort Almubaidin, Mohammad Abdullah
title Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
title_short Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
title_full Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
title_fullStr Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
title_full_unstemmed Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms
title_sort deriving optimal operation rule for reservoir system using enhanced optimization algorithms
publisher Springer Verlag
publishDate 2024
url http://eprints.um.edu.my/45656/
https://doi.org/10.1007/s11269-023-03716-5
_version_ 1816130433828519936
score 13.222552

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