Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

Morstyn, T; Hredzak, B; Aguilera, RP; Agelidis, VG

Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems

Morstyn, T Hredzak, B Aguilera, RP

Agelidis, VG

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Control Systems Technology, 2018, 26 (3), pp. 1107 - 1114
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Morstyn, T	en_US
dc.contributor.author	Hredzak, B	en_US
dc.contributor.author	Aguilera, RP https://orcid.org/0000-0003-4166-8341	en_US
dc.contributor.author	Agelidis, VG	en_US
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	IEEE Transactions on Control Systems Technology, 2018, 26 (3), pp. 1107 - 1114	en_US
dc.identifier.issn	1063-6536	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123072
dc.description.abstract	© 2017 IEEE. This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.	en_US
dc.relation.ispartof	IEEE Transactions on Control Systems Technology	en_US
dc.relation.isbasedon	10.1109/TCST.2017.2699159	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Model Predictive Control for Distributed Microgrid Battery Energy Storage Systems	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	26	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0102 Applied Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	26	en_US

Abstract:

© 2017 IEEE. This brief proposes a new convex model predictive control (MPC) strategy for dynamic optimal power flow between battery energy storage (ES) systems distributed in an ac microgrid. The proposed control strategy uses a new problem formulation, based on a linear d-q reference frame voltage-current model and linearized power flow approximations. This allows the optimal power flows to be solved as a convex optimization problem, for which fast and robust solvers exist. The proposed method does not assume that real and reactive power flows are decoupled, allowing line losses, voltage constraints, and converter current constraints to be addressed. In addition, nonlinear variations in the charge and discharge efficiencies of lithium ion batteries are analyzed and included in the control strategy. Real-time digital simulations were carried out for an islanded microgrid based on the IEEE 13 bus prototypical feeder, with distributed battery ES systems and intermittent photovoltaic generation. It is shown that the proposed control strategy approaches the performance of a strategy based on nonconvex optimization, while reducing the required computation time by a factor of 1000, making it suitable for a real-time MPC implementation.

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http://hdl.handle.net/10453/123072