Testing Stochastic Dynamic Programming Models Conditioned on Observed or Forecasted Inflows
Publication: Journal of Water Resources Planning and Management
Volume 117, Issue 1
Abstract
This paper presents and compares four types of stochastic dynamic programming (SDP) for on‐line reservoir operation, relying on observed or forecasted inflows. The models are different because of the assumptions regarding the inflow in the next time period. If this inflow is known (or a forecast is possible with 100% reliability) models with expected value of the future returns are possible (present returns are deterministic). Otherwise, a simple forecast based on conditional probabilities is necessary, and present and future returns are random. The objective is to maximize expected annual hydropower generation. In a case study of the Feitsui Reservoir in Taiwan, SDP models appear to provide efficient long‐term operating policies. The simulation of on‐line operation of the reservoir reveals that the SDP model that relies on the observed inflows of the preceeding time step provides the best performance. Nevertheless, under different hydrological regimes this finding might be not universal, but dependent upon the characteristics of the particular water resources system.
Get full access to this article
View all available purchase options and get full access to this article.
References
1.
Bellman, R., and Dreyfus, S. E. (1962). Applied dynamic programming. Princeton University Press, Princeton, N.J.
2.
Bogardi, J. J., Budhakooncharoen, S., Shrestha, D. L., and Nandalal, K. D. W. (1988). “Effect of state space and inflow discretization on stochastic dynamic programming‐based reservoir operation rules and system performance.” Proc., VI Congress Asian‐Pacific Division—Int. Assoc. of Hydraulic Research, Kyoto, Japan, I, 429–436.
3.
Goulter, I. C., and Tai, F. K. (1985). “Practical implications in the use of stochastic dynamic programming for reservoir operations.” Water Resour. Bull., 21(1), 65–74.
4.
Harboe, R. C. (1977). “A stochastic optimization model of the Lech River system.” Stochastic processes in water resources engineering, L. Gottschalk, G. Lindh, and L. de Mare, eds., Water Resources Publications, Fort Collins, Colorado.
5.
Huang, W. C. (1989). “Multiobjective decision making in the on‐line operation of a multipurpose reservoir,” thesis presented to the Asian Institute of Technology, at Bangkok, Thailand, in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
6.
Laabs, H., and Harboe, R. (1988). “Generation of operating rules with stochastic dynamic programming and multiple objectives.” Seminar on Conflict Analysis in Reservoir Management, Bangkok, Thailand.
7.
Loucks, D. P., Stedinger, J. R., and Haith, D. A. (1981). Water resources system planning and analysis. Prentice‐Hall Inc., Englewood Cliffs, N.J.
8.
Nandalal, K. D. W. (1986). “Operation policies for two multipurpose reservoirs of the Mahaweli development scheme in Sri Lanka,” thesis presented to the Asian Institute of Technology, at Bangkok, Thailand, in partial fulfillment of the requirements for the degree of Master of Science.
9.
Nemhauser, G. L. (1966). Introduction to dynamic programming. John Wiley and Sons, New York, N.Y.
10.
Yakowitz, S. (1982). “Dynamic programming applications in water resources.” Water Resour. Res., 10(4), 673–696.
11.
Yeh, W. W.‐G. (1985). “Reservoir management and operations models: A state‐of‐the‐art review.” Water Resour. Res., 21(12), 1797–1818.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 117 • Issue 1 • January 1991
Pages: 28 - 36
Copyright
Copyright © 1991 ASCE.
History
Published online: Jan 1, 1991
Published in print: Jan 1991
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.