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Optimal Reservoir Operation Using Stochastic Dynamic Programming
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
- 1 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
- 2 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
- 3 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
- 4 State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China
Journal of Water Resource and Protection·Volume 04 (2012)·Pages 342–345·Published 20 June 2012·DOI10.4236/jwarp.2012.46038
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Abstract
This paper focused on the applying stochastic dynamic programming (SDP) to reservoir operation. Based on the two stages decision procedure, we built an operation model for reservoir operation to derive operating rules. With a case study of the China’s Three Gorges Reservoir, long-term operating rules are obtained. Based on the derived operating rules, the reservoir is simulated with the inflow from 1882 to 2005, which the mean hydropower generation is 85.71 billion kWh. It is shown that the SDP works well in the reservoir operation.
KeywordsReservoir OperationStochastic Dynamic ProgrammingOperating Rules
- P. Kall and S. W. Wallace, “Stochastic Programming,” John Wiley & Sons, Chichester, 1994.
- H. Vladimirou, “Brief Introduction to Stochastic Programming (and Financial Modeling Applications),” University of Cyprus, 2003.
- A. Turgeon, “Optimal Operation of Multi-Reservoir Power Systems with Stochastic Inflows,” Water Resources Research, Vol. 16, No. 2, 1980, pp. 275-283. doi:10.1029/WR016i002p00275
- T. W. Archibald, K. I. M. McKinnon and L. C. Thomas, “An Aggregate Stochastic Dynamic Programming Model of Multi-Reservoir Systems,” Water Resources Research, Vol. 33, No. 2, 1997, pp. 333-340. doi:10.1029/96WR02859
- A. Tilmant, D. Pinte and Q. Goor, “Assessing Marginal Water Values in Multipurpose Multireservoir Systems via Stochastic Programming,” Water Resources Research, Vol. 44, 2008, W12431. doi:10.1029/2008WR007024
- C. Cervellera, V. Chen and A. Wen, “Optimization of a Large-Scale Water Reservoir Network by Stochastic Dynamic Programming with Efficient State Space Discretization,” European Journal of Operational Research, Vol. 171, No. 3, 2006, pp. 1139-1151. doi:10.1016/j.ejor.2005.01.022
- M. V. F. Pereira and L. M. V. G. Pinto, “Stochastic Optimization of a Multireservoir Hydroelectric System: A Decomposition Approach,” Water Resources Research, Vol. 21, No. 6, 1985, pp. 779-792. doi:10.1029/WR021i006p00779
- Y. P. Li, G. H. Huang and S. L. Nie, “An Interval-Parameter Multistage Stochastic Programming Model for Water Resources Management under Uncertainty,” Advanced in Water Resources, Vol. 29, No. 4, 2006, pp. 776-789. doi:10.1016/j.advwatres.2005.07.008
- Y. P. Li, G. H. Huang and X. Chen, “Multistage Scenario-based Interval-Stochastic Programming for Planning Water Resources Allocation,” Stochastic Environmental Research and Risk Assessment, Vol. 23, No. 2, 2009, pp. 781-792. doi:10.1007/s00477-008-0258-y