: An optimal control methodology and computational model are developed to evaluate multi-reservoir release schedules that minimize sediment scour and deposition in rivers and reservoirs. The sedimentation problem is formulated within a discrete-time optimal control framework in which reservoir releases represent control variables and reservoir bed elevations, storage levels, and river bed elevations represent state variables. Constraints imposed on reservoir storage levels and releases are accommodated using a penalty function method. The optimal control model consists of two interfaced components: a one-dimensional finite-difference simulation module used to evaluate flow hydraulics and sediment transport dynamics, and a successive approximation linear quadratic regulator (SALQR) optimization algorithm used to update reservoir release policies and solve the augmented control problem. Hypothetical two-reservoir and five-reservoir networks are used to demonstrate the methodology and its capabilities, which is a vital phase towards the development of a more robust optimal control model and application to an existing multiple-reservoir river network.
[1]
L. Liao,et al.
Convergence in unconstrained discrete-time differential dynamic programming
,
1991
.
[2]
Larry W. Mays,et al.
Optimization Modeling for Sedimentation in Alluvial Rivers
,
1995
.
[3]
Larry W. Mays,et al.
Optimal control approach for sedimentation control in alluvial rivers
,
1995
.
[4]
C. Shoemaker,et al.
Optimal time-varying pumping rates for groundwater remediation: Application of a constrained optimal control algorithm
,
1992
.
[5]
Larry W. Mays,et al.
Differential Dynamic Programming for Estuarine Management
,
1994
.
[6]
William W.-G. Yeh,et al.
Reservoir Management and Operations Models: A State‐of‐the‐Art Review
,
1985
.
[7]
Ralph A. Wurbs.
Reservoir‐System Simulation and Optimization Models
,
1993
.
[8]
C. Shoemaker,et al.
Dynamic optimal control for groundwater remediation with flexible management periods
,
1992
.
[9]
S. Yakowitz,et al.
Computational aspects of discrete-time optimal control
,
1984
.