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, nonsmoothness 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.
Cite
@article{arxiv.1302.2093,
title = {A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley},
author = {Minh Dang Doan and Pontus Giselsson and Tamás Keviczky and Bart De Schutter and Anders Rantzer},
journal= {arXiv preprint arXiv:1302.2093},
year = {2013}
}