Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models

The Perak cascading scheme located in the state of Perak, Malaysia, consists of four reservoirs, namely, Temenggor, Bersia, Kenering and Chenderoh. The reservoirs are used for hydroelectric power generation and flood control. The hydroelectric power potential of the cascading scheme is 578 MW,...

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Main Author: DERIB ASFAW, TILAHUN
Format: Thesis
Language:English
Published: 2012
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Online Access:http://utpedia.utp.edu.my/21611/1/2012%20-CIVIL%20-%20REAL-TIME%20CASCADING%20RESERVOIRS%20OPERATION%20USING%20THE%20GENETIC%20ALGORITHM%20SEASONALLY%20VARIED%20MODELS%20-%20TILAHUN%20DERIB%20ASFAW.pdf
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spelling my-utp-utpedia.216112021-09-23T09:58:57Z http://utpedia.utp.edu.my/21611/ Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models DERIB ASFAW, TILAHUN TA Engineering (General). Civil engineering (General) The Perak cascading scheme located in the state of Perak, Malaysia, consists of four reservoirs, namely, Temenggor, Bersia, Kenering and Chenderoh. The reservoirs are used for hydroelectric power generation and flood control. The hydroelectric power potential of the cascading scheme is 578 MW, while the annual long-term historical average (HA) hydroelectric power generation was around 228 MW. It was about 39.46% of the potential capacity. Accordingly, the study aimed to improve the hydroelectric power generation of the scheme. The genetic algorithm and the seasonally varied models have been developed to maximize the annual hydroelectric power generation of the Perak cascading reservoir. The fitness function of the genetic algorithm model (GAM) was to minimize the difference between the potential capacity and actual generation of the scheme. GAM was established with a total of 208 and I 04 equality and inequality constraints, respectively. The optimal release decisions were found after checking the optimality of the population size (PS), the crossover probability (CRP) and the generation number (GN). Whereas, a seasonally varied model (SVM) has been developed after the analysis of the long-term HA operation data. Consequently, from the annual variation of the headrace level of Temenggor, the most upstream reservoir in the cascading scheme, four seasons are identified. The seasons are the refill, upper level, deplete and lower level that occurs in sequentially and repeatedly per year. The two seasons that require a ranking order to maximize the energy-storage and to minimize the spill of water in the scheme are the refill and deplete. Hence, the refill rank order performed according to the decrease order of the change of power production in the change of storage volume, while the order of depletion has been conducted with the increase order of the storage effectiveness ratio. 2012-08 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/21611/1/2012%20-CIVIL%20-%20REAL-TIME%20CASCADING%20RESERVOIRS%20OPERATION%20USING%20THE%20GENETIC%20ALGORITHM%20SEASONALLY%20VARIED%20MODELS%20-%20TILAHUN%20DERIB%20ASFAW.pdf DERIB ASFAW, TILAHUN (2012) Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models. PhD thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
DERIB ASFAW, TILAHUN
Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models
description The Perak cascading scheme located in the state of Perak, Malaysia, consists of four reservoirs, namely, Temenggor, Bersia, Kenering and Chenderoh. The reservoirs are used for hydroelectric power generation and flood control. The hydroelectric power potential of the cascading scheme is 578 MW, while the annual long-term historical average (HA) hydroelectric power generation was around 228 MW. It was about 39.46% of the potential capacity. Accordingly, the study aimed to improve the hydroelectric power generation of the scheme. The genetic algorithm and the seasonally varied models have been developed to maximize the annual hydroelectric power generation of the Perak cascading reservoir. The fitness function of the genetic algorithm model (GAM) was to minimize the difference between the potential capacity and actual generation of the scheme. GAM was established with a total of 208 and I 04 equality and inequality constraints, respectively. The optimal release decisions were found after checking the optimality of the population size (PS), the crossover probability (CRP) and the generation number (GN). Whereas, a seasonally varied model (SVM) has been developed after the analysis of the long-term HA operation data. Consequently, from the annual variation of the headrace level of Temenggor, the most upstream reservoir in the cascading scheme, four seasons are identified. The seasons are the refill, upper level, deplete and lower level that occurs in sequentially and repeatedly per year. The two seasons that require a ranking order to maximize the energy-storage and to minimize the spill of water in the scheme are the refill and deplete. Hence, the refill rank order performed according to the decrease order of the change of power production in the change of storage volume, while the order of depletion has been conducted with the increase order of the storage effectiveness ratio.
format Thesis
author DERIB ASFAW, TILAHUN
author_facet DERIB ASFAW, TILAHUN
author_sort DERIB ASFAW, TILAHUN
title Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models
title_short Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models
title_full Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models
title_fullStr Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models
title_full_unstemmed Real-time Cascading Reservoirs Operation using the Genetic Algorithm and Seasonally Varied Models
title_sort real-time cascading reservoirs operation using the genetic algorithm and seasonally varied models
publishDate 2012
url http://utpedia.utp.edu.my/21611/1/2012%20-CIVIL%20-%20REAL-TIME%20CASCADING%20RESERVOIRS%20OPERATION%20USING%20THE%20GENETIC%20ALGORITHM%20SEASONALLY%20VARIED%20MODELS%20-%20TILAHUN%20DERIB%20ASFAW.pdf
http://utpedia.utp.edu.my/21611/
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score 13.211869