A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
Author
Summary, in English
A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O(1/k^2), where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, non-smoothness in the power-production functions, and a globally
coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX.
coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX.
Department/s
Publishing year
2013
Language
English
Pages
1594-1605
Publication/Series
Control Engineering Practice
Volume
21
Issue
11
Links
Document type
Journal article
Publisher
Elsevier
Topic
- Control Engineering
Keywords
- Distributed optimization
- Hydro power control
- Accelerated gradient algorithm
- Distributed model predictive control
- Model predictive control
Status
Published
Project
- LCCC
Research group
- LCCC
ISBN/ISSN/Other
- ISSN: 0967-0661