Maximizing investment value of small-scale PV in a smart grid environment

Every, J; Li, L; Guo, YG; Dorrell, DG

Maximizing investment value of small-scale PV in a smart grid environment

Every, J

Li, L

Guo, YG

Dorrell, DG

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Publication Type:: Conference Proceeding
Citation:: 2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016, 2016, pp. 385 - 390
Issue Date:: 2016-01-01

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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	Guo, YG https://orcid.org/0000-0001-6182-0684	en_US
dc.contributor.author	Dorrell, DG https://orcid.org/0000-0001-8060-3386	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016, 2016, pp. 385 - 390	en_US
dc.identifier.isbn	9781509033881	en_US
dc.identifier.uri	http://hdl.handle.net/10453/108918
dc.description.abstract	© 2016 IEEE. Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.	en_US
dc.relation.ispartof	2016 IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2016	en_US
dc.relation.isbasedon	10.1109/ICRERA.2016.7884366	en_US
dc.title	Maximizing investment value of small-scale PV in a smart grid environment	en_US
dc.type	Conference Proceeding
utslib.for	0907 Environmental Engineering	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	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.

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http://hdl.handle.net/10453/108918