A distributed optimization algorithm for the predictive control of smart grids

URN to cite this document: urn:nbn:de:bvb:703-epub-2081-1

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.
Department of Mathematics, University of Bayreuth, School of Electrical Engineering and Computer Science, University of Newcastle, Australia, Technische Universität Ilmenau
Bayreuth , 2015 . - 12 S.

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Name:	Braun_Gruene_et_al_DistributedOptimizationAlgorithm_2015.pdf
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Available under License	German copyright law. This contribution is freely accessible with the consent of the copyright holder on the basis of an (DFG-funded) alliance or national license. The document may be used free of charge for personal use. In addition, the reproduction, editing, distribution and any kind of exploitation require the written consent of the respective rights holder.

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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"	GR1569/13-1
ARC Future Fellowship	FT1101000746

Project financing:

Deutsche Forschungsgemeinschaft

Abstract

In this paper, we present a hierarchical, iterative distributed optimization algorithm, and show that the algorithm converges to the solution of a particular global 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 residential-scale 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 large-scale 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.

Further data

Item Type:	Preprint, postprint
Additional notes (visible to public):	erschienen in: IEEE Transactions on Automatic Control. Bd. 61 (2016) Heft 12 . - S. 3898-3911. DOI: https://doi.org/10.1109/TAC.2016.2525808
Keywords:	distributed optimization; model predictive control; smart grid
DDC Subjects:	500 Science > 510 Mathematics
Institutions of the University:	Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics) Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics) > Chair Mathematics V (Applied Mathematics) - Univ.-Prof. Dr. Lars Grüne Profile Fields Profile Fields > Advanced Fields Profile Fields > Advanced Fields > Nonlinear Dynamics
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-2081-1
Date Deposited:	23 Jun 2015 07:37
Last Modified:	01 Jun 2021 06:08
URI:	https://epub.uni-bayreuth.de/id/eprint/2081

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