scholarly journals Controlled approximation of the value function in stochastic dynamic programming for multi-reservoir systems

2015 ◽  
Vol 12 (4) ◽  
pp. 539-557 ◽  
Author(s):  
Luckny Zéphyr ◽  
Pascal Lang ◽  
Bernard F. Lamond
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Value Function ◽  
Stochastic Dynamic ◽  
Reservoir Systems ◽  
The Value Function

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

Optimal Parametric Control of Nonlinear Random Vibrating Systems

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Wenwen Chang ◽  
Xiaoling Jin ◽  
Zhilong Huang
Keyword(s):  
Dynamic Programming ◽  
Value Function ◽  
High Efficiency ◽  
Quadratic Function ◽  
Optimal Feedback Control ◽  
Stochastic Dynamic ◽  
Parametric Control ◽  
Dynamic Programming Equation ◽  
The Value Function ◽  
Vibrating Systems

Abstract Due to the great progresses in the fields of smart structures, especially smart soft materials and structures, the parametric control of nonlinear systems attracts extensive attentions in scientific and industrial communities. This paper devotes to the derivation of the optimal parametric control strategy for nonlinear random vibrating systems, in which the excitations are confined to Gaussian white noises. For a prescribed performance index balancing the control performance and control cost, the stochastic dynamic programming equation with respect to the value function is first derived by the principle of dynamic programming. The optimal feedback control law is established according to the extremum condition. The explicit expression of the value function is determined by approximately expressing as a quadratic function of state variables and by solving the final dynamic programming equation. The application and efficacy of the optimal parametric control are illustrated by a random-excited Duffing oscillator and a dielectric elastomer balloon with random pressure. The numerical results show that the optimal parameter control possesses good effectiveness, high efficiency, and high robustness to excitation intensity, and is superior than the associated optimal bounded parametric control.

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