Optimization of energy management and conversion in the water systems based on evolutionary algorithms
In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have been used as successful algorithms in such engineering application for optimizations in order to introd...
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Springer Verlag (Germany)
2019
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| author | Karami, Hojat Ehteram, Mohammad Mousavi, Sayed-Farhad Farzin, Saeed Kisi, Ozgur El-Shafie, Ahmed |
| author_facet | Karami, Hojat Ehteram, Mohammad Mousavi, Sayed-Farhad Farzin, Saeed Kisi, Ozgur El-Shafie, Ahmed |
| author_sort | Karami, Hojat |
| collection | UM |
| description | In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have been used as successful algorithms in such engineering application for optimizations in order to introduce a comprehensive comparison analysis with the results of WOA. Two case studies were considered in this research. The aim of the first case study was to minimize irrigation water deficits for Aydoughmoush dam in Iran. Results showed that average solution of the operation rule of the WOA is close to the global solution and that reliability index and resiliency index for water supply, based on WOA, are higher than the GA and PSOA. Also, the operation rule achieved by the WOA has well-supplied water to match the water demands for the operation period (1991–2000). The second case study was related to minimize the electricity power deficits in Karun-4 reservoir. In fact, the water should be released such that the electricity power generation is maximized or the electricity power deficit should be minimized. Results showed that the operation rule for the water achieved by the WOA produces more electrical power than GA and PSOA. Furthermore, the average solution accomplished by WOA is very close to the global solution. Different indexes were used for evaluating the proposed methods, namely reliability index, vulnerability index, and resiliency index. For the first case study, the proposed WOA could achieve 98% reliability which is outperformed the other GA and PSOA methods. On the other hand, for the second case study, the proposed WOA method could attain electricity power 12 and 15% more than the one generated while using the GA and PSOA methods. Thus, WOA has high potential to be applied for achieving optimal operation rule for dam and reservoir operation and better water resources management and plan. © 2018, The Natural Computing Applications Forum. |
| first_indexed | 2024-03-06T06:02:03Z |
| format | Article |
| id | um.eprints-24190 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:02:03Z |
| publishDate | 2019 |
| publisher | Springer Verlag (Germany) |
| record_format | dspace |
| spelling | um.eprints-241902020-04-09T11:12:02Z http://eprints.um.edu.my/24190/ Optimization of energy management and conversion in the water systems based on evolutionary algorithms Karami, Hojat Ehteram, Mohammad Mousavi, Sayed-Farhad Farzin, Saeed Kisi, Ozgur El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) In this article, an application of weed optimization algorithm (WOA) for reservoir operation was proposed. In addition, genetic algorithm (GA) and particle swarm optimization algorithm (PSOA) have been used as successful algorithms in such engineering application for optimizations in order to introduce a comprehensive comparison analysis with the results of WOA. Two case studies were considered in this research. The aim of the first case study was to minimize irrigation water deficits for Aydoughmoush dam in Iran. Results showed that average solution of the operation rule of the WOA is close to the global solution and that reliability index and resiliency index for water supply, based on WOA, are higher than the GA and PSOA. Also, the operation rule achieved by the WOA has well-supplied water to match the water demands for the operation period (1991–2000). The second case study was related to minimize the electricity power deficits in Karun-4 reservoir. In fact, the water should be released such that the electricity power generation is maximized or the electricity power deficit should be minimized. Results showed that the operation rule for the water achieved by the WOA produces more electrical power than GA and PSOA. Furthermore, the average solution accomplished by WOA is very close to the global solution. Different indexes were used for evaluating the proposed methods, namely reliability index, vulnerability index, and resiliency index. For the first case study, the proposed WOA could achieve 98% reliability which is outperformed the other GA and PSOA methods. On the other hand, for the second case study, the proposed WOA method could attain electricity power 12 and 15% more than the one generated while using the GA and PSOA methods. Thus, WOA has high potential to be applied for achieving optimal operation rule for dam and reservoir operation and better water resources management and plan. © 2018, The Natural Computing Applications Forum. Springer Verlag (Germany) 2019 Article PeerReviewed Karami, Hojat and Ehteram, Mohammad and Mousavi, Sayed-Farhad and Farzin, Saeed and Kisi, Ozgur and El-Shafie, Ahmed (2019) Optimization of energy management and conversion in the water systems based on evolutionary algorithms. Neural Computing and Applications, 31 (10). pp. 5951-5964. ISSN 0941-0643, DOI https://doi.org/10.1007/s00521-018-3412-6 <https://doi.org/10.1007/s00521-018-3412-6>. https://doi.org/10.1007/s00521-018-3412-6 doi:10.1007/s00521-018-3412-6 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Karami, Hojat Ehteram, Mohammad Mousavi, Sayed-Farhad Farzin, Saeed Kisi, Ozgur El-Shafie, Ahmed Optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| title | Optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| title_full | Optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| title_fullStr | Optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| title_full_unstemmed | Optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| title_short | Optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| title_sort | optimization of energy management and conversion in the water systems based on evolutionary algorithms |
| topic | TA Engineering (General). Civil engineering (General) |
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