A model predictive control approach for cotton farm microgrid operation under uncertainties

Lin, Y; Li, L; Zhang, J; Wang, J

A model predictive control approach for cotton farm microgrid operation under uncertainties

Lin, Y Li, L

Zhang, J Wang, J

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2023, 00
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Lin, Y
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.contributor.author	Zhang, J
dc.contributor.author	Wang, J
dc.date	2022-09-26
dc.date.accessioned	2024-05-03T04:12:06Z
dc.date.available	2024-05-03T04:12:06Z
dc.date.issued	2023-01-01
dc.identifier.citation	2022 32nd Australasian Universities Power Engineering Conference (AUPEC), 2023, 00
dc.identifier.isbn	9798350339567
dc.identifier.uri	http://hdl.handle.net/10453/178603
dc.description.abstract	This study addresses the problem of optimizing cotton farm operating costs under uncertainties A model predictive control MPC optimization approach is adopted to maximize the usage of renewable energy and minimize the overall water pumping cost during the cotton growth and irrigation period To deal with the uncertainties in renewable generation water demand precipitation and evaporation this paper formulates the operation problem of the cotton farm pumping system as a stochastic programming problem and then an MPC approach is adopted to solve the stochastic programming iteratively to cater to real time changes in uncertain weather conditions and irrigation demand Scenario generation and reduction techniques are applied to obtain typical scenarios with their probability Then they will be used to facilitate the obtained stochastic optimization problem in all the MPC iteration steps A case study is conducted using the historical irrigation data and local meteorological data of an actual cotton farm The results show that MPC under uncertainty to optimize water pump operation can obtain the expected value of AU 36 386 which saves A 3 193 compared with the manual mode of operation
dc.language	en
dc.publisher	IEEE
dc.relation	Cotton RDCCotton Research and Development Corporation
dc.relation.ispartof	2022 32nd Australasian Universities Power Engineering Conference (AUPEC)
dc.relation.ispartof	2022 32nd Australasian Universities Power Engineering Conference
dc.relation.ispartofseries	Australasian Universities Power Engineering Conference
dc.relation.isbasedon	10.1109/aupec58309.2022.10215528
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	A model predictive control approach for cotton farm microgrid operation under uncertainties
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Adelaide, 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	embargoed	*
utslib.copyright.embargo	2025-08-21T00:00:00+1000Z
dc.date.updated	2024-05-03T04:12:05Z
pubs.finish-date	2022-09-28
pubs.publication-status	Published
pubs.start-date	2022-09-26
pubs.volume	00

Abstract:

This study addresses the problem of optimizing cotton farm operating costs under uncertainties A model predictive control MPC optimization approach is adopted to maximize the usage of renewable energy and minimize the overall water pumping cost during the cotton growth and irrigation period To deal with the uncertainties in renewable generation water demand precipitation and evaporation this paper formulates the operation problem of the cotton farm pumping system as a stochastic programming problem and then an MPC approach is adopted to solve the stochastic programming iteratively to cater to real time changes in uncertain weather conditions and irrigation demand Scenario generation and reduction techniques are applied to obtain typical scenarios with their probability Then they will be used to facilitate the obtained stochastic optimization problem in all the MPC iteration steps A case study is conducted using the historical irrigation data and local meteorological data of an actual cotton farm The results show that MPC under uncertainty to optimize water pump operation can obtain the expected value of AU 36 386 which saves A 3 193 compared with the manual mode of operation

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http://hdl.handle.net/10453/178603