A real-time pricing scheme for residential energy systems using a market maker

URN to cite this document: urn:nbn:de:bvb:703-epub-2078-4

Title data

Braun, Philipp ; Grüne, Lars ; Kellett, Christopher M. ; Weller, Steven R. ; Worthmann, Karl:
A real-time pricing scheme for residential energy systems using a market maker.
Department of Mathematics, University of Bayreuth, School of Electrical Engineering and Computer Science, University of Newcastle, Australia, Technische Universität Ilmenau
Bayreuth , 2015 . - 4 S.

Format:	PDF
Name:	Braun_Gruene_et_al_real_time_pricing_scheme_for_res_2015.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
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

Voltage rise is an undesirable side-effect of solar photovoltaic (PV) generation, arising from the flow of surplus electrical power back into the grid when PV generation exceeds local demand. Customers deploying residential-scale battery storage are likely to further exacerbate voltage rise problems for electrical utilities unless the charge/discharge schedules of batteries is appropriately coordinated. In this paper, we present a real-time pricing mechanism for use in a network of distributed residential energy systems (RESs), each employing solar PV generation and battery storage. The pricing mechanism proposed in this paper is based on a Market Maker algorithm in which predicted power profiles and real-time pricing information is iteratively exchanged between a central entity and each of the RESs. The Market Maker formulation presented in this paper is shown via simulation studies to converge to a fixed price vector, thereby reducing the price volatility observed in an earlier formulation, while achieving the same reduction in power usage variability as a centralised model predictive control (MPC) scheme presented previously.

Further data

Item Type:	Preprint, postprint
Keywords:	noncooperative distributed optimization; smart grid; model predictive 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-2078-4
Date Deposited:	23 Jun 2015 07:40
Last Modified:	28 Mar 2019 15:16
URI:	https://epub.uni-bayreuth.de/id/eprint/2078

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