scholarly journals A simulation-based approach to stochastic dynamic programming

2011 ◽  
Vol 27 (2) ◽  
pp. 151-163 ◽  
Author(s):  
Nicholas G. Polson ◽  
Morten Sorensen
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Simulation Based

scholarly journals Real-Time Implementation and Hardware Testing of a Hybrid Vehicle Energy Management Controller Based on Stochastic Dynamic Programming

2012 ◽  
Vol 135 (2) ◽  
Author(s):  
Daniel F. Opila ◽  
Xiaoyong Wang ◽  
Ryan McGee ◽  
J. W. Grizzle
Keyword(s):  
Dynamic Programming ◽  
Real Time ◽  
Energy Management ◽  
Fuel Economy ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic ◽  
Engine Torque ◽  
Simulation Based ◽  
Completion Times ◽  
Engine Start

An energy management controller based on shortest path stochastic dynamic programming (SP-SDP) is implemented and tested in a prototype vehicle. The controller simultaneously optimizes fuel economy and powertrain activity, namely gear shifts and engine on–off events. Previous work reported on the controller's design and its extensive simulation-based evaluation. This paper focuses on implementation of the controller algorithm in hardware. Practical issues concerning real-time computability, driver perception, and command timing are highlighted and addressed. The SP-SDP controllers are shown to run in real-time, gracefully handle variations in engine start and gear-shift-completion times, and operate in a manner that is transparent to the driver. A hardware problem with the test vehicle restricted its maximum engine torque, which prevented a reliable fuel economy assessment of the SP-SDP controller. The data that were collected indicated that SP-SDP controllers could be straightforwardly designed to operate at different points of the fuel economy tradeoff curve and that their fuel economy may equal or exceed that of a baseline industrial controller designed for the vehicle.

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.

scholarly journals MDPtoolbox: a multi-platform toolbox to solve stochastic dynamic programming problems

Ecography ◽  
2014 ◽  
Vol 37 (9) ◽  
pp. 916-920 ◽  
Author(s):  
Iadine Chadès ◽  
Guillaume Chapron ◽  
Marie-Josée Cros ◽  
Frédérick Garcia ◽  
Régis Sabbadin
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Stochastic Dynamic
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