Title data
Braun, Philipp ; Grüne, Lars ; Kellett, Christopher M. ; Weller, Steven R. ; Worthmann, Karl:
A distributed optimization algorithm for the predictive control of smart grids.
In: IEEE Transactions on Automatic Control.
Vol. 61
(2016)
Issue 12
.
 pp. 38983911.
ISSN 15582523
DOI: https://doi.org/10.1109/TAC.2016.2525808
This is the latest version of this item.
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Project information
Project title: 
Project's official title Project's id DFG Priority Program 1305 "Control theory for digitally networked dynamical systems", Project "Performance Analysis for Distributed and Multiobjective Model Predictive Control" GR1569/131 ARC Future Fellowship FT1101000746 

Project financing: 
Deutsche Forschungsgemeinschaft 
Abstract in another language
In this paper, we present a hierarchical, iterative distributed optimization algorithm and show that the algorithm converges to the global solution of a particular optimization problem. The motivation for the distributed optimization problem is the predictive control of a smart grid, in which the states of charge of a network of residentialscale batteries are optimally coordinated so as to minimize variability in the aggregated power supplied to/from the grid by the residential network. The distributed algorithm developed in this paper calls for communication between a central entity and an optimizing agent associated with each battery, but does not require communication between agents. The distributed algorithm is shown to achieve the performance of a largescale centralized optimization algorithm, but with greatly reduced communication overhead and computational burden. A numerical case study using data from an Australian electricity distribution network is presented to demonstrate the performance of the distributed optimization algorithm.
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A distributed optimization algorithm for the predictive control of smart grids. (deposited 27 Jun 2015 21:00)
 A distributed optimization algorithm for the predictive control of smart grids. (deposited 03 Mar 2016 10:17) [Currently Displayed]