Sensitivity Analysis of Hydroelectric Power Generation from Cascading Reservoirs

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Abstract:

The cascading reservoirs in Perak, Malaysia, were used to test the sensitivity analysis of hydroelectric power generation during refill and deplete period of the reservoirs. The cascading scheme comprises four reservoirs namely Temenggor, Bersia, Kenering and Chenderoh. The test was conducted after the analysis of water balance and stage-storage relationship of each reservoir in the cascading scheme. The result showed that power generation from the smaller reservoir, Bersia, is more sensitive to the change of headrace level, while the larger storage capacity and rated head reservoir is the most sensitive to the change of release. Therefore, to maximize the power generation from the cascading reservoir, the refill operations should be ranked according to the increasing order of the reservoir storage capacity and a reverse order should be followed during deplete period.

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Advanced Materials Research (Volumes 622-623)

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1152-1156

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] M. -Y. Tu, N. -S. Hsu, and W. W. -G. Yeh, Optimization of Reservoir Management and Operation with Hedging Rules, Journal of Water Resources Planning and Management, vol. 129, pp.86-97, (2003).

DOI: 10.1061/(asce)0733-9496(2003)129:2(86)

Google Scholar

[2] L. L. Ngo, Optimising reservoir operation A case study of the Hoa Binh reservoir, Vietnam, PhD, Institute of Environment & Resources, Technical University of Denmark, (2006).

Google Scholar

[3] P. C. Deka and V. Chandramouli, Fuzzy Neural Network Modeling of Reservoir Operation, Journal of Water Resources Planning and Management, vol. 135, pp.5-12, (2009).

DOI: 10.1061/(asce)0733-9496(2009)135:1(5)

Google Scholar

[4] X. Liu, S. Guo, P. Liu, L. Chen, and X. Li, Deriving Optimal Refill Rules for Multi-Purpose Reservoir Operation, Water Resources Management, vol. 25, pp.431-448, (2011).

DOI: 10.1007/s11269-010-9707-8

Google Scholar

[5] S. P. Simonovic and D. A. Savic, Intelligent Decision Support and Reservoir Management and Operations, Journal of Computing in Civil Engineering, vol. 3, pp.367-385, (1989).

DOI: 10.1061/(asce)0887-3801(1989)3:4(367)

Google Scholar

[6] B. J. V. Lienden and J. R. Lund, Spatial Complexity and Reservoir Optimization Results, Civil Engineering and Environmental Systems, vol. 21, pp.1-17, (2004).

DOI: 10.1080/10286600310001616496

Google Scholar

[7] T. Kim, J. -H. Heo, D. -H. Bae, and J. -H. Kim, Single-reservoir operating rules for a year using multiobjective genetic algorithm, Journal of Hydroinformatics vol. 10, pp.163-179, (2008).

DOI: 10.2166/hydro.2008.019

Google Scholar

[8] S. S. H. Bu and L. T. Seng, Fish parasite communities in tropical reservoirs along the Perak River, Malaysia, Hydrobiologia vol. 356, pp.175-181, (1997).

Google Scholar

[9] K. Madani and J. R. Lund, Modeling California's high-elevation hydropower systems in energy units, Water Resources Research, vol. 45, (2009).

DOI: 10.1029/2008wr007206

Google Scholar