Planning and operation scheduling of PV-battery systems: A novel methodology

Khalilpour, R; Vassallo, A

Planning and operation scheduling of PV-battery systems: A novel methodology

Khalilpour, R Vassallo, A

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Renewable and Sustainable Energy Reviews, 2016, 53, (C), pp. 194-208
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Khalilpour, R
dc.contributor.author	Vassallo, A
dc.date.accessioned	2022-08-14T04:09:27Z
dc.date.available	2022-08-14T04:09:27Z
dc.date.issued	2016-01-01
dc.identifier.citation	Renewable and Sustainable Energy Reviews, 2016, 53, (C), pp. 194-208
dc.identifier.issn	1364-0321
dc.identifier.issn	1879-0690
dc.identifier.uri	http://hdl.handle.net/10453/160123
dc.description.abstract	The aim of this paper is to develop a decision support tool for investment decision making, optimal sizing, and operation scheduling of grid-connected PV/battery system with respect to dynamics of periodical weather data, electricity price, PV/battery system cost, PV/battery specifications, desired reliability, and other critical design and operational parameters. We have reviewed the literature on historical development of models for PV-battery systems sizing. A multi-period mixed-integer linear program (MILP) is then introduced with the objective of maximizing the net present value of cash flow (for investment analysis) or the savings in electricity bill (for operation scheduling). For investment decision analysis, the model is capable to identify whether it is economical to invest in PV and/or battery systems, and if positive it can find the best PV and/or battery systems from the mix of available options. Furthermore, the model defines the optimal size of selected PV and/or battery systems. The model will select one or a combination of a few systems if feasible. All these decision variables are identified concurrently with finding the optimal operation schedule of the PV and/or battery systems at each period over the planning horizon. These variables include power flows of grid-to-load, PV-to-load, battery-to-load, battery-to-grid, grid-to-battery, PV-to-battery, PV-to-grid as well as battery state-of charge. This decision support program enables the consumer (ranging from a small house to large-scale industrial plants) to implement the most efficient electricity management strategy while achieving the goal of minimizing the electricity bill.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartof	Renewable and Sustainable Energy Reviews
dc.relation.isbasedon	10.1016/j.rser.2015.08.015
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	09 Engineering
dc.subject.classification	Energy
dc.title	Planning and operation scheduling of PV-battery systems: A novel methodology
dc.type	Journal Article
utslib.citation.volume	53
utslib.for	09 Engineering
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 Professional Practice and Leadership
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-14T04:09:25Z
pubs.issue	C
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	C

Abstract:

The aim of this paper is to develop a decision support tool for investment decision making, optimal sizing, and operation scheduling of grid-connected PV/battery system with respect to dynamics of periodical weather data, electricity price, PV/battery system cost, PV/battery specifications, desired reliability, and other critical design and operational parameters. We have reviewed the literature on historical development of models for PV-battery systems sizing. A multi-period mixed-integer linear program (MILP) is then introduced with the objective of maximizing the net present value of cash flow (for investment analysis) or the savings in electricity bill (for operation scheduling). For investment decision analysis, the model is capable to identify whether it is economical to invest in PV and/or battery systems, and if positive it can find the best PV and/or battery systems from the mix of available options. Furthermore, the model defines the optimal size of selected PV and/or battery systems. The model will select one or a combination of a few systems if feasible. All these decision variables are identified concurrently with finding the optimal operation schedule of the PV and/or battery systems at each period over the planning horizon. These variables include power flows of grid-to-load, PV-to-load, battery-to-load, battery-to-grid, grid-to-battery, PV-to-battery, PV-to-grid as well as battery state-of charge. This decision support program enables the consumer (ranging from a small house to large-scale industrial plants) to implement the most efficient electricity management strategy while achieving the goal of minimizing the electricity bill.

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http://hdl.handle.net/10453/160123