Optimal selection of small-scale hybrid PV-battery systems to maximize economic benefit based on temporal load data

Every, J; Li, L; Dorrell, DG

Optimal selection of small-scale hybrid PV-battery systems to maximize economic benefit based on temporal load data

Every, J

Li, L

Dorrell, DG

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 471 - 476
Issue Date:: 2018-02-05

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Field	Value	Language
dc.contributor.author	Every, J https://orcid.org/0000-0002-4695-2619	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Dorrell, DG https://orcid.org/0000-0001-8060-3386	en_US
dc.date.issued	2018-02-05	en_US
dc.identifier.citation	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017, 2018, 2018-February pp. 471 - 476	en_US
dc.identifier.isbn	9781538621035	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127470
dc.description.abstract	© 2017 IEEE. Continued advances in PV and battery energy storage technologies have made hybrid PV-battery systems an attractive prospect for residential energy consumers. However the process to select an appropriate system is complicated by the relatively high cost of batteries, a multitude of available retail electricity plans and the removal of PV installation incentive schemes. In this paper, an optimization strategy based on an individual customer's temporal load profile is established to maximize electricity cost savings through optimal selection of PV-battery system size, orientation and retail electricity plan. Quantum-behaved particle swarm optimization is applied as the underlying algorithm given its well-suited application to problems involving hybrid energy system specification. The optimization strategy is tested using real-world residential consumption data, current system pricing and available retail electricity plans to establish the efficacy of a hybrid PV-battery solution.	en_US
dc.relation.ispartof	Proceedings of the 2017 12th IEEE Conference on Industrial Electronics and Applications, ICIEA 2017	en_US
dc.relation.isbasedon	10.1109/ICIEA.2017.8282891	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Optimal selection of small-scale hybrid PV-battery systems to maximize economic benefit based on temporal load data	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-February	en_US
utslib.for	0906 Electrical and Electronic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-February	en_US

Abstract:

© 2017 IEEE. Continued advances in PV and battery energy storage technologies have made hybrid PV-battery systems an attractive prospect for residential energy consumers. However the process to select an appropriate system is complicated by the relatively high cost of batteries, a multitude of available retail electricity plans and the removal of PV installation incentive schemes. In this paper, an optimization strategy based on an individual customer's temporal load profile is established to maximize electricity cost savings through optimal selection of PV-battery system size, orientation and retail electricity plan. Quantum-behaved particle swarm optimization is applied as the underlying algorithm given its well-suited application to problems involving hybrid energy system specification. The optimization strategy is tested using real-world residential consumption data, current system pricing and available retail electricity plans to establish the efficacy of a hybrid PV-battery solution.

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http://hdl.handle.net/10453/127470