Stochastic And Modified Sequent Peak Algorithm For Reservoir Planning Analysis Considering Performance Indices
This study is on modeling the critical period and total storage capacity of reservoir systems employing performance criteria and synthetic data generation technique. Three sites in the Southern part of Peninsular Malaysia are selected as conceptual reservoirs to be the case studies: Johor at Rant...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://eprints.usm.my/47028/1/Stochastic%20And%20Modified%20Sequent%20Peak%20Algorithm%20For%20Reservoir%20Planning%20Analysis%20Considering%20Performance%20Indices.pdf http://eprints.usm.my/47028/ |
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| Summary: | This study is on modeling the critical period and total storage capacity of reservoir
systems employing performance criteria and synthetic data generation technique. Three
sites in the Southern part of Peninsular Malaysia are selected as conceptual reservoirs to
be the case studies: Johor at Rantau Panjang; Melaka at Pantai Belimbing and Muar at
Buluh Kasap gauging stations. Statistical data analysis of both annual and monthly
streamflow data of the study sites is carried out prior to the time series analysis. The tests
are implemented for testing consistency, stationarity, randomness and determining the
most appropriate probability distribution function of the historical data. Subsequently,
Auto-regressive lag one, AR(1), coupled with Valencia-Schaake (V-S) disaggregation
model are applied to generate synthetic streamflow data. In the next stage, the modified
Sequent Peak Algorithm (SPA) is employed for the Storage-yield planning analysis of
reservoir systems at different demands, reliability and vulnerability performance metrics
employing the synthetic streamflow data. The results show that the reliability and
vulnerability metrics are significant in critical period and storage capacity modeling.
Subsequently, using the simulation results, new regression equations are developed to
model the critical period and total storage capacity of study systems individually and
three systems together applying standard demand parameter, reliability and vulnerability
performance measures and coefficient of variation and skewness of annual flows. The R2
obtained over the complete range of the critical period and storage capacity prediction is
high, being 0.9810 and 0.9856, respectively for the three systems together. Hence, the
obtained equations could reproduce the simulated critical period and storage capacity for
different demands, reliability and vulnerability indices efficiently. |
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