Predictive control of a smart grid: a distributed optimization algorithm with centralized performance properties

URN to cite this document: urn:nbn:de:bvb:703-epub-1988-9

Title data

Braun, Philipp ; Grüne, Lars ; Kellett, Christopher M. ; Weller, Steven R. ; Worthmann, Karl:
Predictive control of a smart grid: a distributed optimization algorithm with centralized performance properties.
Department of Mathematics, University of Bayreuth
Bayreuth , 2015 . - 8 S.

Format:	PDF
Name:	pred_cont_of_smart_grid_dits_opt_alg_with_cent_perf_prop2015braun_gruene_kellett_weller_worthmann.pdf
Version:	Accepted Version
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.

Download (506kB)

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
Australian Research Council

Abstract

The authors recently proposed several model predictive control (MPC) approaches to managing residential level energy generation and storage, including centralized, distributed, and decentralized schemes. As expected, the distributed and decentralized schemes result in a loss of performance but are scalable and more flexible with regards to network topology. In this paper we present a distributed optimization approach which asymptotically recovers the performance of the centralized optimization problem performed in MPC at each time step. Simulations using data from an Australian electricity distribution company, Ausgrid, are provided showing the benefit of a variable step size in the algorithm and the impact of an increasing number of participating residential energy systems. Furthermore, when used in a receding horizon scheme, simulations indicate that terminating the iterative distributed optimization algorithm before convergence does not result in a significant loss of performance.

Further data

Item Type:	Preprint, postprint
Additional notes (visible to public):	erschienen in: Proceedings of the 54th IEEE Conference on Decision and Control (CDC 2015). - Piscataway, NJ : IEEE , 2015 . - S. 5593-5598 DOI: https://doi.org/10.1109/CDC.2015.7403096
Keywords:	distributed control; smart grid; hierarchical control
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-1988-9
Date Deposited:	02 Apr 2015 09:22
Last Modified:	01 Jun 2021 05:14
URI:	https://epub.uni-bayreuth.de/id/eprint/1988

Download Statistics

Downloads

Downloads per month over past year