Optimal Stochastic Operation of Salt River Project, Arizona
Publication: Journal of Water Resources Planning and Management
Volume 117, Issue 5
Abstract
The application of extended linear quadratic Gaussian (ELQG) control, a nonlinear stochastic optimization method, to the Salt River Project reservoir system is described. The optimization problem was posed as the maximization of total hydropower avoided cost, subject to constraints that represented system mass balance and physical or operating limitations on releases. The six surface reservoirs, ground‐water storage, and planned cyclic ground‐water storage were modeled as six equivalent reservoirs. The ELQG control algorithm was applied to the historic reservoir inflow for the 51‐yr period 1931–1981 using a 12‐month sliding window. Operating policies for each of the 600 months in the test period were developed and compared with the present operating policy for two reservoir configurations: the existing configuration, and a reduced storage configuration that mimicked temporary system constraints during the construction period for the planned alteration of Roosevelt Dam. The ELQG control results showed that significant increases in hydropower avoided costs could be achieved while maintaining water supply reliability at current levels or higher.
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References
1.
Bertsekas, D. (1982). Constrained optimization and Lagrange multiplier methods. Academic Press, New York, N.Y.
2.
Gelb, A., ed. (1974). Applied optimal estimation. The M.I.T. Press, Cambridge, Mass.
3.
Georgakakos, A. P. (1989). “Extended linear quadratic Gaussian control: Further extensions.” Water Resour. Res., 25(2), 191–201.
4.
Georgakakos, A. P., and Marks, D. H. (1985). “Real time control of reservoir systems.” Report No. 301, Ralph M. Parsons Laboratory, Massachusetts Inst. of Tech., Cambridge, Mass.
5.
Georgakakos, A. P., and Marks, D. H. (1987). “A new method for the real‐time operation of reservoir systems.” Water Resour. Res., 23(7), 1376–1390.
6.
Johnson, S. A. (1989). “Spline approximation in discrete dynamic programming with application to stochastic multi‐reservoir systems,” thesis presented to Cornell University, at Ithaca, N.Y., in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
7.
Hooper, E. R., and Lettenmaier, D. P. (1989). “Optimal operation of he Salt River Project, Arizona.” Water Resources Series Technical Report 115, Department of Civil Engineering, University of Washington, Seattle, Wash.
8.
Hooper, E. R., Lettenmaier, D. P., and Clemm, N. (1989). “Evaluating the effectiveness of operating policy through the use of Monte Carlo simulation in conjunction with synthetic streamflow sequences.” P‐2194‐1, Electric Power Research Institute, Palo Alto, Calif.
9.
Kitanidis, P. K., and Foufoula‐Georgiou, E. (1987). “Error analysis of conventional discrete and gradient dynamic programming.” Water Resour. Res., 23(5), 845–858.
10.
Toebes, G. H., and Sheppard, A. A., eds. (1979). Proc. National Workshop on Reservoir System Operations, ASCE, New York, N.Y.
11.
Wasimi, S. A., and Kitanidis, P. K. (1983). “Real‐time forecasting and daily operation of a multireservoir system during floods by linear quadratic gaussian control.” Water Resour. Res., 19(6), 1511–1522.
12.
Yakowitz, S. (1982). “Dynamic programming applications in water resources.” Water Resour. Res., 18(4), 673–696.
13.
Yeh, W. W.‐G. (1985). “Reservoir management and operations models: A state‐of‐the‐art review.” Water Resour. Res., 21(12), 1797–1818.
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Copyright © 1991 ASCE.
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Published online: Sep 1, 1991
Published in print: Sep 1991
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