A Model Predictive Control for Cotton Farm Microgrid Systems in Australia

Lin, Y; Zhang, J; Li, L

A Model Predictive Control for Cotton Farm Microgrid Systems in Australia

Lin, Y Zhang, J

Li, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021, 00, pp. 1-6
Issue Date:: 2021-11-16

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Field	Value	Language
dc.contributor.author	Lin, Y
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.date	2021-09-26
dc.date.accessioned	2022-06-03T00:52:00Z
dc.date.available	2022-06-03T00:52:00Z
dc.date.issued	2021-11-16
dc.identifier.citation	2021 31st Australasian Universities Power Engineering Conference (AUPEC), 2021, 00, pp. 1-6
dc.identifier.isbn	9781665434515
dc.identifier.uri	http://hdl.handle.net/10453/157892
dc.description.abstract	This paper presents a model predictive control (MPC) approach to a microgrid at a cotton farm so as to minimize the water pumping operational cost while taking full advantage of renewable energy sources. The reason for using MPC is its ability in handling noise, disturbance, and real-time parameter changes. In this paper, the MPC models of grid-connected are established; moreover, the effectiveness and robustness of the MPC models are analyzed by cotton farm case studies. Simulation results show that the optimal MPC solutions for grid-connected microgrid of a farm are AU$8.4/ML less than a manual control-based strategy. In addition, the MPC solution shows outstanding robustness in controlling the water reservoir level. When the disturbance data of the rainy season in 2016 are added, the system saves 34.5% of the operating cost compared with the baseline. When the rainy season disturbance is added together, the system saves 11.74% of operating costs compared to the baseline.
dc.language	en
dc.publisher	IEEE
dc.relation	Cotton RDCCotton Research and Development Corporation
dc.relation.ispartof	2021 31st Australasian Universities Power Engineering Conference (AUPEC)
dc.relation.ispartof	2021 31st Australasian Universities Power Engineering Conference
dc.relation.isbasedon	10.1109/aupec52110.2021.9597774
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A Model Predictive Control for Cotton Farm Microgrid Systems in Australia
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Perth, Australia
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	*
dc.date.updated	2022-06-03T00:51:58Z
pubs.finish-date	2021-09-30
pubs.publication-status	Published
pubs.start-date	2021-09-26
pubs.volume	00

Abstract:

This paper presents a model predictive control (MPC) approach to a microgrid at a cotton farm so as to minimize the water pumping operational cost while taking full advantage of renewable energy sources. The reason for using MPC is its ability in handling noise, disturbance, and real-time parameter changes. In this paper, the MPC models of grid-connected are established; moreover, the effectiveness and robustness of the MPC models are analyzed by cotton farm case studies. Simulation results show that the optimal MPC solutions for grid-connected microgrid of a farm are AU$8.4/ML less than a manual control-based strategy. In addition, the MPC solution shows outstanding robustness in controlling the water reservoir level. When the disturbance data of the rainy season in 2016 are added, the system saves 34.5% of the operating cost compared with the baseline. When the rainy season disturbance is added together, the system saves 11.74% of operating costs compared to the baseline.

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http://hdl.handle.net/10453/157892