Optimal operation of hydropower reservoirs under climate change

The current research aims to optimize the water release to generate optimal hydropower generation for the future up to the year 2039. The study�s novelty is the adaptive and nonadaptive rule curves for power production using optimization algorithms under the climate change model. In addition, the st...

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Main Authors: Ehteram M., Ahmed A.N., Chow M.F., Latif S.D., Chau K.-W., Chong K.L., El-Shafie A.
Other Authors: 57113510800
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Published: Springer Science and Business Media B.V. 2024
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spelling my.uniten.dspace-339542024-10-14T11:17:30Z Optimal operation of hydropower reservoirs under climate change Ehteram M. Ahmed A.N. Chow M.F. Latif S.D. Chau K.-W. Chong K.L. El-Shafie A. 57113510800 57214837520 57214146115 57216081524 7202674661 57208482172 16068189400 Adaptive rule curves Hydropower generation Metaheuristic algorithm Nonadaptive rule curves algorithm climate change climate conditions climate modeling heuristics power generation precipitation (climatology) vulnerability The current research aims to optimize the water release to generate optimal hydropower generation for the future up to the year 2039. The study�s novelty is the adaptive and nonadaptive rule curves for power production using optimization algorithms under the climate change model. In addition, the study used the RCP 8.5 scenario based on seven climate change models. A weighting method was used to select the best climate change models. The method can allocate more weights to more accurate models. The results revealed that the temperature increased by about 26% in the future, while precipitation would decreased by around 3%. The bat algorithm was also used, given it is a powerful method in solving optimization problems in water resources management. The results indicated that less power could be generated during the future period in comparison with the base period as there will be less inflow to the reservoir and released water for hydropower generation. However, by applying adaptive rule curves, the hydropower generation may be improved even under the climate change conditions. For example, the volumetric reliability index obtained when using adaptive rule curves (92%) was higher than when nonadaptive rule curves (90%) were applied. Also, the adoption of adaptive rule curves decreased the vulnerability index for the future period. Therefore, the bat algorithm with adaptive rule curves has a high potential for optimizing reservoir operations under the climate change conditions. � 2022, The Author(s), under exclusive licence to Springer Nature B.V. Final 2024-10-14T03:17:30Z 2024-10-14T03:17:30Z 2023 Article 10.1007/s10668-022-02497-y 2-s2.0-85134348570 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134348570&doi=10.1007%2fs10668-022-02497-y&partnerID=40&md5=9a3005aa421cf8a25f7ef85e2b9f7bef https://irepository.uniten.edu.my/handle/123456789/33954 25 10 10627 10659 Springer Science and Business Media B.V. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic Adaptive rule curves
Hydropower generation
Metaheuristic algorithm
Nonadaptive rule curves
algorithm
climate change
climate conditions
climate modeling
heuristics
power generation
precipitation (climatology)
vulnerability
spellingShingle Adaptive rule curves
Hydropower generation
Metaheuristic algorithm
Nonadaptive rule curves
algorithm
climate change
climate conditions
climate modeling
heuristics
power generation
precipitation (climatology)
vulnerability
Ehteram M.
Ahmed A.N.
Chow M.F.
Latif S.D.
Chau K.-W.
Chong K.L.
El-Shafie A.
Optimal operation of hydropower reservoirs under climate change
description The current research aims to optimize the water release to generate optimal hydropower generation for the future up to the year 2039. The study�s novelty is the adaptive and nonadaptive rule curves for power production using optimization algorithms under the climate change model. In addition, the study used the RCP 8.5 scenario based on seven climate change models. A weighting method was used to select the best climate change models. The method can allocate more weights to more accurate models. The results revealed that the temperature increased by about 26% in the future, while precipitation would decreased by around 3%. The bat algorithm was also used, given it is a powerful method in solving optimization problems in water resources management. The results indicated that less power could be generated during the future period in comparison with the base period as there will be less inflow to the reservoir and released water for hydropower generation. However, by applying adaptive rule curves, the hydropower generation may be improved even under the climate change conditions. For example, the volumetric reliability index obtained when using adaptive rule curves (92%) was higher than when nonadaptive rule curves (90%) were applied. Also, the adoption of adaptive rule curves decreased the vulnerability index for the future period. Therefore, the bat algorithm with adaptive rule curves has a high potential for optimizing reservoir operations under the climate change conditions. � 2022, The Author(s), under exclusive licence to Springer Nature B.V.
author2 57113510800
author_facet 57113510800
Ehteram M.
Ahmed A.N.
Chow M.F.
Latif S.D.
Chau K.-W.
Chong K.L.
El-Shafie A.
format Article
author Ehteram M.
Ahmed A.N.
Chow M.F.
Latif S.D.
Chau K.-W.
Chong K.L.
El-Shafie A.
author_sort Ehteram M.
title Optimal operation of hydropower reservoirs under climate change
title_short Optimal operation of hydropower reservoirs under climate change
title_full Optimal operation of hydropower reservoirs under climate change
title_fullStr Optimal operation of hydropower reservoirs under climate change
title_full_unstemmed Optimal operation of hydropower reservoirs under climate change
title_sort optimal operation of hydropower reservoirs under climate change
publisher Springer Science and Business Media B.V.
publishDate 2024
_version_ 1814060050652594176
score 13.214268