We propose a distributed optimization algorithm for mixed L1/L2-norm optimization based on accelerated gradient methods using dual decomposition. The algorithm achieves convergence rate O(1 k 2), where k is the iteration number, which significantly improves the convergence rates of existing duality-based distributed optimization algorithms that achieve O(1 k). The performance of the developed algorithm is evaluated on randomly generated optimization problems arising in distributed model predictive control (DMPC). The evaluation shows that, when the problem data is sparse and large-scale, our algorithm can outperform current state-of-the-art optimization software CPLEX and MOSEK.

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SUMMARYIn this paper, we analyze and compare five distributed model predictive control (DMPC) schemes using a hydro-power plant benchmark. Besides being one of the most important sources of renewable power, hydropower plants present very interesting control challenges. The operation of a hydro-power valley involves the coordination of several subsystems over a large geographical area in order to produce the demanded energy while satisfying constraints on water levels and flows. In particular, we test the different DMPC algorithms using a 24-h power tracking scenario in which the hydro-power plant is simulated with an accurate nonlinear model. In this way, it is possible to provide qualitative and quantitative comparisons between different DMPC schemes implemented on a common benchmark, which is a type of assessment rare in the literature.

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