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

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskriftPeer review

Sammanfattning

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.
Originalspråkengelska
Sidor (från-till)1594-1605
TidskriftControl Engineering Practice
Volym21
Nummer11
DOI
StatusPublished - 2013

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