A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

Minh Dang Doan; Pontus Giselsson; Tamás Keviczky; Bart De Schutter; Anders Rantzer

doi:10.1016/j.conengprac.2013.06.012

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

Minh Dang Doan, Pontus Giselsson, Tamás Keviczky, Bart De Schutter, Anders Rantzer

Research output: Contribution to journal › Article › peer-review

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 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.

Original language	English
Pages (from-to)	1594-1605
Journal	Control Engineering Practice
Volume	21
Issue number	11
DOIs	https://doi.org/10.1016/j.conengprac.2013.06.012
Publication status	Published - 2013

Subject classification (UKÄ)

Control Engineering

Free keywords

Distributed optimization
Hydro power control
Accelerated gradient algorithm
Distributed model predictive control
Model predictive control

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10.1016/j.conengprac.2013.06.012

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Holmqvist, A. (Researcher), Andersson, N. (Researcher), Cervin, A. (Researcher), Mannesson, A. (Researcher), Gattami, A. (Researcher), Ghulchak, A. (Researcher), Papadopoulos, A. V. (Researcher), Rantzer, A. (Researcher), Robertsson, A. (Researcher), Sootla, A. (Researcher), THEORIN, A. (Researcher), Bernhardsson, B. (Researcher), Olofsson, B. (Researcher), Wittenmark, B. (Researcher), Grussler, C. (Researcher), Johnsson, C. (Researcher), MADJIDIAN, D. (Researcher), Johannesson, E. (Researcher), Magnusson, F. (Researcher), Ståhl, F. (Researcher), Como, G. (Researcher), Chasparis, G. (Researcher), Turesson, G. (Researcher), Dressler, I. (Researcher), Åkesson, J. (Researcher), Cho, J. H. (Researcher), Årzén, K.-E. (Researcher), Åström, K. J. (Researcher), Sou, K. C. (Researcher), Mårtensson, K. (Researcher), Berntorp, K. (Researcher), Soltesz, K. (Researcher), Lessard, L. (Researcher), Hast, M. (Researcher), Rönn, M. (Researcher), Ansbjerg Kjær, M. (Researcher), Maggio, M. (Researcher), Kristalny, M. (Researcher), Garpinger, O. (Researcher), From, P. J. (Researcher), Larsson, P.-O. (Researcher), Giselsson, P. (Researcher), Johansson, R. (Researcher), Hägglund, T. (Researcher), Vladimerou, V. (Researcher), Romero Segovia, V. (Researcher), Aurelius, A. (Researcher), Cedersjö, G. (Researcher), Bür, K. (Researcher), Dellkrantz, M. (Researcher), Du, M. (Researcher), Amani, P. (Researcher), Larsson, R. (Researcher), Tärneberg, W. (Research student), Li, Z. (Researcher), Yin, L. (Researcher), Tufvesson, F. (Researcher), Höst, S. (Researcher), Nilsson, B. (Researcher), Stenström, S. (Researcher), Andersson, J. A. (Researcher), Diehl, S. (Researcher), Dürango, J. (Researcher), Ghazaei Ardakani, M. (Researcher), Forsberg, P.-O. (Researcher), Bengtsson, F. (Researcher), Jörntell, H. (Researcher), Arévalo, C. (Researcher), Führer, C. (Researcher), Andersson, C. (Researcher), Mohammadi, F. (Researcher), Ödling, P. (Researcher), Andersson, M. (Researcher), Kihl, M. (Researcher) & Tunestål, P. (Researcher)
2008/07/01 → 2018/06/30
Project: Research

Cite this

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title = "A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley",

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 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.",

keywords = "Distributed optimization, Hydro power control, Accelerated gradient algorithm, Distributed model predictive control, Model predictive control",

author = "Doan, {Minh Dang} and Pontus Giselsson and Tam{\'a}s Keviczky and {De Schutter}, Bart and Anders Rantzer",

year = "2013",

doi = "10.1016/j.conengprac.2013.06.012",

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T1 - A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

AU - Doan, Minh Dang

AU - Giselsson, Pontus

AU - Keviczky, Tamás

AU - De Schutter, Bart

AU - Rantzer, Anders

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Distributed optimization

KW - Hydro power control

KW - Accelerated gradient algorithm

KW - Distributed model predictive control

KW - Model predictive control

U2 - 10.1016/j.conengprac.2013.06.012

DO - 10.1016/j.conengprac.2013.06.012

M3 - Article

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SP - 1594

EP - 1605

JO - Control Engineering Practice

JF - Control Engineering Practice

IS - 11

ER -

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

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