Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq
Achievement of the optimal hydropower generation from operation of water reservoirs, is a complex problems. The purpose of this study was to formulate and improve an approach of a genetic algorithm optimization model (GAOM) in order to increase the maximization of annual hydropower generation for a...
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| Language: | English |
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SpringerOpen
2016
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| Online Access: | http://psasir.upm.edu.my/id/eprint/54160/ http://psasir.upm.edu.my/id/eprint/54160/1/Enhanced%20genetic%20algorithm%20optimization%20model%20for%20a%20single%20reservoir%20operation%20based%20on%20hydropower%20generation.pdf |
| _version_ | 1848852469945204736 |
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| author | Al‑Aqeeli, Yousif H. Lee, Teang Shui Abd Aziz, Samsuzana |
| author_facet | Al‑Aqeeli, Yousif H. Lee, Teang Shui Abd Aziz, Samsuzana |
| author_sort | Al‑Aqeeli, Yousif H. |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Achievement of the optimal hydropower generation from operation of water reservoirs, is a complex problems. The purpose of this study was to formulate and improve an approach of a genetic algorithm optimization model (GAOM) in order to increase the maximization of annual hydropower generation for a single reservoir. For this purpose, two simulation algorithms were drafted and applied independently in that GAOM during 20 scenarios (years) for operation of Mosul reservoir, northern Iraq. The first algorithm was based on the traditional simulation of reservoir operation, whilst the second algorithm (Salg) enhanced the GAOM by changing the population values of GA through a new simulation process of reservoir operation. The performances of these two algorithms were evaluated through the comparison of their optimal values of annual hydropower generation during the 20 scenarios of operating. The GAOM achieved an increase in hydropower generation in 17 scenarios using these two algorithms, with the Salg being superior in all scenarios. All of these were done prior adding the evaporation (Ev) and precipitation (Pr) to the water balance equation. Next, the GAOM using the Salg was applied by taking into consideration the volumes of these two parameters. In this case, the optimal values obtained from the GAOM were compared, firstly with their counterpart that found using the same algorithm without taking into consideration of Ev and Pr, secondly with the observed values. The first comparison showed that the optimal values obtained in this case decreased in all scenarios, whilst maintaining the good results compared with the observed in the second comparison. The results proved the effectiveness of the Salg in increasing the hydropower generation through the enhanced approach of the GAOM. In addition, the results indicated to the importance of taking into account the Ev and Pr in the modelling of reservoirs operation. |
| first_indexed | 2025-11-15T10:38:35Z |
| format | Article |
| id | upm-54160 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:38:35Z |
| publishDate | 2016 |
| publisher | SpringerOpen |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-541602018-03-01T08:23:09Z http://psasir.upm.edu.my/id/eprint/54160/ Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq Al‑Aqeeli, Yousif H. Lee, Teang Shui Abd Aziz, Samsuzana Achievement of the optimal hydropower generation from operation of water reservoirs, is a complex problems. The purpose of this study was to formulate and improve an approach of a genetic algorithm optimization model (GAOM) in order to increase the maximization of annual hydropower generation for a single reservoir. For this purpose, two simulation algorithms were drafted and applied independently in that GAOM during 20 scenarios (years) for operation of Mosul reservoir, northern Iraq. The first algorithm was based on the traditional simulation of reservoir operation, whilst the second algorithm (Salg) enhanced the GAOM by changing the population values of GA through a new simulation process of reservoir operation. The performances of these two algorithms were evaluated through the comparison of their optimal values of annual hydropower generation during the 20 scenarios of operating. The GAOM achieved an increase in hydropower generation in 17 scenarios using these two algorithms, with the Salg being superior in all scenarios. All of these were done prior adding the evaporation (Ev) and precipitation (Pr) to the water balance equation. Next, the GAOM using the Salg was applied by taking into consideration the volumes of these two parameters. In this case, the optimal values obtained from the GAOM were compared, firstly with their counterpart that found using the same algorithm without taking into consideration of Ev and Pr, secondly with the observed values. The first comparison showed that the optimal values obtained in this case decreased in all scenarios, whilst maintaining the good results compared with the observed in the second comparison. The results proved the effectiveness of the Salg in increasing the hydropower generation through the enhanced approach of the GAOM. In addition, the results indicated to the importance of taking into account the Ev and Pr in the modelling of reservoirs operation. SpringerOpen 2016 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/54160/1/Enhanced%20genetic%20algorithm%20optimization%20model%20for%20a%20single%20reservoir%20operation%20based%20on%20hydropower%20generation.pdf Al‑Aqeeli, Yousif H. and Lee, Teang Shui and Abd Aziz, Samsuzana (2016) Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq. SpringerPlus, 5 (1). pp. 1-21. ISSN 2193-1801 https://springerplus.springeropen.com/articles/10.1186/s40064-016-2372-5 Genetic algorithm; Optimal operation; Hydropower generation; Single reservoir; Mosul reservoir; Iraq 10.1186/s40064-016-2372-5 |
| spellingShingle | Genetic algorithm; Optimal operation; Hydropower generation; Single reservoir; Mosul reservoir; Iraq Al‑Aqeeli, Yousif H. Lee, Teang Shui Abd Aziz, Samsuzana Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq |
| title | Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq |
| title_full | Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq |
| title_fullStr | Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq |
| title_full_unstemmed | Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq |
| title_short | Enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of Mosul reservoir, northern Iraq |
| title_sort | enhanced genetic algorithm optimization model for a single reservoir operation based on hydropower generation: case study of mosul reservoir, northern iraq |
| topic | Genetic algorithm; Optimal operation; Hydropower generation; Single reservoir; Mosul reservoir; Iraq |
| url | http://psasir.upm.edu.my/id/eprint/54160/ http://psasir.upm.edu.my/id/eprint/54160/ http://psasir.upm.edu.my/id/eprint/54160/ http://psasir.upm.edu.my/id/eprint/54160/1/Enhanced%20genetic%20algorithm%20optimization%20model%20for%20a%20single%20reservoir%20operation%20based%20on%20hydropower%20generation.pdf |