Deep learning based optimal energy management framework for community energy storage system

Alam, MM; Jang, YM

Deep learning based optimal energy management framework for community energy storage system

Alam, MM Jang, YM

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: ICT Express, 2023, 9, (3), pp. 333-340
Issue Date:: 2023-06-01

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Field	Value	Language
dc.contributor.author	Alam, MM
dc.contributor.author	Jang, YM
dc.date.accessioned	2024-04-25T00:36:04Z
dc.date.available	2024-04-25T00:36:04Z
dc.date.issued	2023-06-01
dc.identifier.citation	ICT Express, 2023, 9, (3), pp. 333-340
dc.identifier.issn	2405-9595
dc.identifier.issn	2405-9595
dc.identifier.uri	http://hdl.handle.net/10453/178353
dc.description.abstract	This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The corresponding energy generation and consumption problems are formulated by a long short-term memory algorithm is combined with an optimization algorithm to produce an optimal solution. In this study, a PV-community energy storage system (CESS) integrated is considered where the scheduling decision of the CESS and utility grid can be subsequently achieved through formulated constraints. The test results demonstrate the efficacy and robustness of the proposed system that achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	ICT Express
dc.relation.isbasedon	10.1016/j.icte.2022.05.007
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Deep learning based optimal energy management framework for community energy storage system
dc.type	Journal Article
utslib.citation.volume	9
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	2024-04-25T00:36:00Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	9
utslib.citation.issue	3

Abstract:

This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The corresponding energy generation and consumption problems are formulated by a long short-term memory algorithm is combined with an optimization algorithm to produce an optimal solution. In this study, a PV-community energy storage system (CESS) integrated is considered where the scheduling decision of the CESS and utility grid can be subsequently achieved through formulated constraints. The test results demonstrate the efficacy and robustness of the proposed system that achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment.

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http://hdl.handle.net/10453/178353