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

In English

Författare

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.

Publiceringsår

2013

Språk

Engelska

Sidor

1594-1605

Publikation/Tidskrift/Serie

Control Engineering Practice

Volym

21

Issue

11

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Control Engineering

Nyckelord

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

Status

Published

Projekt

  • LCCC

Forskningsgrupp

  • LCCC

ISBN/ISSN/Övrigt

  • ISSN: 0967-0661