A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley
Doan, Minh Dang ; Giselsson, Pontus LU
; Keviczky, Tamás
; De Schutter, Bart
and Rantzer, Anders
LU
(2013)
In Control Engineering Practice
21(11).
p.1594-1605
- Abstract
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3958240
- author
- Doan, Minh Dang
; Giselsson, Pontus
LU
; Keviczky, Tamás
; De Schutter, Bart
and Rantzer, Anders
LU
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Distributed optimization, Hydro power control, Accelerated gradient algorithm, Distributed model predictive control, Model predictive control
- in
- Control Engineering Practice
- volume
- 21
- issue
- 11
- pages
- 1594 - 1605
- publisher
- Elsevier
- external identifiers
-
- wos:000326361500013
- scopus:84884713405
- ISSN
- 0967-0661
- DOI
- 10.1016/j.conengprac.2013.06.012
- project
- LCCC
- language
- English
- LU publication?
- yes
- id
- 9650c8d9-16ef-47cf-9d3a-eb379e753856 (old id 3958240)
- date added to LUP
- 2016-04-01 10:59:35
- date last changed
- 2024-05-20 04:57:43
@article{9650c8d9-16ef-47cf-9d3a-eb379e753856,
abstract = {{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<br/><br>
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.}},
author = {{Doan, Minh Dang and Giselsson, Pontus and Keviczky, Tamás and De Schutter, Bart and Rantzer, Anders}},
issn = {{0967-0661}},
keywords = {{Distributed optimization; Hydro power control; Accelerated gradient algorithm; Distributed model predictive control; Model predictive control}},
language = {{eng}},
number = {{11}},
pages = {{1594--1605}},
publisher = {{Elsevier}},
series = {{Control Engineering Practice}},
title = {{A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley}},
url = {{http://dx.doi.org/10.1016/j.conengprac.2013.06.012}},
doi = {{10.1016/j.conengprac.2013.06.012}},
volume = {{21}},
year = {{2013}},
}