Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms

In engineering and scientific disciplines, there are extensive Optimization Application Problems (OAPs) such as economic dispatch, structural design, and water resources. One of the major OAPs is the operation of dams and reservoirs to minimize the gap between water supply for irrigation and demand...

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Main Authors: Chong, Kai Lun, Lai, Sai Hin, Ahmed, Ali Najah, Zaafar, Wan Zurina Wan, Rao, Ravipudi Venkata, Sherif, Mohsen, Sefelnasr, Ahmed, El-Shafie, Ahmed
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Published: Institute of Electrical and Electronics Engineers 2021
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Online Access:http://eprints.um.edu.my/34065/
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spelling my.um.eprints.340652022-07-20T02:09:30Z http://eprints.um.edu.my/34065/ Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms Chong, Kai Lun Lai, Sai Hin Ahmed, Ali Najah Zaafar, Wan Zurina Wan Rao, Ravipudi Venkata Sherif, Mohsen Sefelnasr, Ahmed El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) In engineering and scientific disciplines, there are extensive Optimization Application Problems (OAPs) such as economic dispatch, structural design, and water resources. One of the major OAPs is the operation of dams and reservoirs to minimize the gap between water supply for irrigation and demand patterns such as hydropower generation. Drawing optimal operation for dams and reservoirs is often categorized as discontinuity, multimodality, non-differentiability and non-convexity. Classical mathematical programming-based methods for optimization might be inappropriate or unrealizable in drawing optimal operation rules for dam and reservoir operation. During the last two decades, new optimization methods-based on nature-inspired meta-heuristic algorithms (MHAs) have motivated hydrologists to investigate MHAs as better alternative optimization tools for identifying the optimal dam and reservoir operation rules. To solve the dam and reservoir-optimization applications better, this review presents the past, present, and prospective research directions using MHAs. The problem of dam and reservoir optimization requires a fundamental shift of focus towards enhancing not only the problem formulation and decomposition but also the computational efficiency of MHAs. Institute of Electrical and Electronics Engineers 2021 Article PeerReviewed Chong, Kai Lun and Lai, Sai Hin and Ahmed, Ali Najah and Zaafar, Wan Zurina Wan and Rao, Ravipudi Venkata and Sherif, Mohsen and Sefelnasr, Ahmed and El-Shafie, Ahmed (2021) Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms. IEEE Access, 9. pp. 19488-19505. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3054424 <https://doi.org/10.1109/ACCESS.2021.3054424>. 10.1109/ACCESS.2021.3054424
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)
Chong, Kai Lun
Lai, Sai Hin
Ahmed, Ali Najah
Zaafar, Wan Zurina Wan
Rao, Ravipudi Venkata
Sherif, Mohsen
Sefelnasr, Ahmed
El-Shafie, Ahmed
Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
description In engineering and scientific disciplines, there are extensive Optimization Application Problems (OAPs) such as economic dispatch, structural design, and water resources. One of the major OAPs is the operation of dams and reservoirs to minimize the gap between water supply for irrigation and demand patterns such as hydropower generation. Drawing optimal operation for dams and reservoirs is often categorized as discontinuity, multimodality, non-differentiability and non-convexity. Classical mathematical programming-based methods for optimization might be inappropriate or unrealizable in drawing optimal operation rules for dam and reservoir operation. During the last two decades, new optimization methods-based on nature-inspired meta-heuristic algorithms (MHAs) have motivated hydrologists to investigate MHAs as better alternative optimization tools for identifying the optimal dam and reservoir operation rules. To solve the dam and reservoir-optimization applications better, this review presents the past, present, and prospective research directions using MHAs. The problem of dam and reservoir optimization requires a fundamental shift of focus towards enhancing not only the problem formulation and decomposition but also the computational efficiency of MHAs.
format Article
author Chong, Kai Lun
Lai, Sai Hin
Ahmed, Ali Najah
Zaafar, Wan Zurina Wan
Rao, Ravipudi Venkata
Sherif, Mohsen
Sefelnasr, Ahmed
El-Shafie, Ahmed
author_facet Chong, Kai Lun
Lai, Sai Hin
Ahmed, Ali Najah
Zaafar, Wan Zurina Wan
Rao, Ravipudi Venkata
Sherif, Mohsen
Sefelnasr, Ahmed
El-Shafie, Ahmed
author_sort Chong, Kai Lun
title Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
title_short Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
title_full Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
title_fullStr Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
title_full_unstemmed Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
title_sort review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms
publisher Institute of Electrical and Electronics Engineers
publishDate 2021
url http://eprints.um.edu.my/34065/
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score 13.18916