Distributed and decentralized control of residential energy systems incorporating battery storage

URN to cite this document: urn:nbn:de:bvb:703-epub-1986-8

Title data

Worthmann, Karl ; Kellett, Christopher M. ; Braun, Philipp ; Grüne, Lars ; Weller, Steven R.:
Distributed and decentralized control of residential energy systems incorporating battery storage.
Department of Mathematics, University of Bayreuth
Bayreuth , 2014 . - 8 S.

Format:	PDF
Name:	dist_decent_control_of_RES_incorporating_battery2014worthmann_kellett_braun_gruene_weller.pdf
Version:	Preprint
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
Marie-Curie Initial Training Network "Sensitivity Analysis for Deterministic Controller Design" (SADCO)	264735-SADCO
DFG Priority Program 1305 "Control theory for digitally networked dynamical systems", Project "Development of decentralized asynchronous predictive control methods for digitally networked systems"	GR1569/12-2
ARC Future Fellowship	FT1101000746

Project financing:

7. Forschungsrahmenprogramm für Forschung, technologische Entwicklung und Demonstration der Europäischen Union
Deutsche Forschungsgemeinschaft
Australian Research Council

Abstract

The recent rapid uptake of residential solar photovoltaic (PV) installations provides many challenges for electricity distribution networks designed for one-way power flow from the distribution company to the residential customer. For gridconnected installations, intermittent generation as well as large amounts of generation during low load periods can lead to a degradation of power quality and even outages due to overvoltage conditions. In this paper we present four control methodologies to mitigate these difficulties using small-scale distributed battery storage. These four approaches represent three different control architectures: centralized, decentralized, and distributed control. These approaches are validated and compared using data on load and generation profiles from customers in an Australian electricity distribution network.

Further data

Item Type:	Preprint, postprint
Additional notes (visible to public):	erschienen in: IEEE Transactions on Smart Grid. Bd. 6 (2015) Heft 4 . - S. 1914-1923. DOI: https://doi.org/10.1109/TSG.2015.2392081
Keywords:	distributed generation; energy storage; model predictive control (MPC)
DDC Subjects:	500 Science > 510 Mathematics
Institutions of the University:	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 > Advanced Fields > Nonlinear Dynamics 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) Profile Fields Profile Fields > Advanced Fields
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-1986-8
Date Deposited:	17 Apr 2015 07:06
Last Modified:	01 Jun 2021 10:15
URI:	https://epub.uni-bayreuth.de/id/eprint/1986

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