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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my.uniten.dspace-366932025-03-03T15:43:58Z Deriving Optimal Operation Rule for Reservoir System Using Enhanced Optimization Algorithms Almubaidin M.A. Ahmed A.N. Sidek L.M. AL-Assifeh K.A.H. El-Shafie A. 57476845900 57214837520 35070506500 58062951100 16068189400 Jordan Curve fitting Genetic algorithms Heuristic algorithms Invasive weed optimization Learning algorithms Particle swarm optimization (PSO) Reservoir management Meta-heuristics algorithms Operating policies Optimization algorithms Optimization techniques Reservoir rule curve Rule curves Simulation model Standard operating policy Teaching-learning-based optimizations Water shortages genetic algorithm modeling reservoir water demand water management Reservoirs (water) 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. ? The Author(s), under exclusive licence to Springer Nature B.V. 2024. Final 2025-03-03T07:43:58Z 2025-03-03T07:43:58Z 2024 Article 10.1007/s11269-023-03716-5 2-s2.0-85186195557 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186195557&doi=10.1007%2fs11269-023-03716-5&partnerID=40&md5=074a51541d4d7e087ae7cd6f4e4806f5 https://irepository.uniten.edu.my/handle/123456789/36693 38 4 1207 1223 All Open Access; Green Open Access Springer Science and Business Media B.V. Scopus |
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Jordan Curve fitting Genetic algorithms Heuristic algorithms Invasive weed optimization Learning algorithms Particle swarm optimization (PSO) Reservoir management Meta-heuristics algorithms Operating policies Optimization algorithms Optimization techniques Reservoir rule curve Rule curves Simulation model Standard operating policy Teaching-learning-based optimizations Water shortages genetic algorithm modeling reservoir water demand water management Reservoirs (water) |
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Jordan Curve fitting Genetic algorithms Heuristic algorithms Invasive weed optimization Learning algorithms Particle swarm optimization (PSO) Reservoir management Meta-heuristics algorithms Operating policies Optimization algorithms Optimization techniques Reservoir rule curve Rule curves Simulation model Standard operating policy Teaching-learning-based optimizations Water shortages genetic algorithm modeling reservoir water demand water management Reservoirs (water) Almubaidin M.A. Ahmed A.N. Sidek L.M. AL-Assifeh K.A.H. El-Shafie A. 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. ? The Author(s), under exclusive licence to Springer Nature B.V. 2024. |
| author2 |
57476845900 |
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57476845900 Almubaidin M.A. Ahmed A.N. Sidek L.M. AL-Assifeh K.A.H. El-Shafie A. |
| format |
Article |
| author |
Almubaidin M.A. Ahmed A.N. Sidek L.M. AL-Assifeh K.A.H. El-Shafie A. |
| author_sort |
Almubaidin M.A. |
| 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 Science and Business Media B.V. |
| publishDate |
2025 |
| _version_ |
1825816191946457088 |
| score |
13.244109 |