Conference paper
A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Scientific Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Dynamical Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Center for Energy Resources Engineering, Centers, Technical University of Denmark4
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark5
This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP that decomposes into small independent subproblems.
We test the decomposition algorithm using a simple power management case study, in which the OCP is formulated as a convex quadratic program. Simulations show that the decomposition algorithm scales linearly in the number of uncertainty scenarios. Moreover, a parallel implementation of the algorithm is several orders of magnitude faster than state-of-the-art convex quadratic programming algorithms, provided that the number of uncertainty scenarios is large.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2014 |
| Pages: | 1086-1093 |
| Proceedings: | 2014 IEEE Multi-Conference on Systems and Control |
| ISBN: | 1479974064 , 1479974072 , 9781479974061 and 9781479974078 |
| ISSN: | 21589879 and 21589860 |
| Types: | Conference paper |
| DOI: | 10.1109/ISIC.2014.6967612 |
| ORCIDs: | Dammann, Bernd , Madsen, Henrik and Jørgensen, John Bagterp |
