Data Driven Approach for User-owned Renewable Energy Sources Allocation in Community Microgrid

Alam, MM; Hossain, J; Mofidul, RB

Data Driven Approach for User-owned Renewable Energy Sources Allocation in Community Microgrid

Alam, MM Hossain, J

Mofidul, RB

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024, 00, pp. 1-6
Issue Date:: 2024-12-25

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Field	Value	Language
dc.contributor.author	Alam, MM
dc.contributor.author	Hossain, J https://orcid.org/0000-0001-7602-3581
dc.contributor.author	Mofidul, RB
dc.date	2024-11-20
dc.date.accessioned	2025-02-27T22:06:58Z
dc.date.available	2025-02-27T22:06:58Z
dc.date.issued	2024-12-25
dc.identifier.citation	2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC), 2024, 00, pp. 1-6
dc.identifier.isbn	979-8-3503-7795-8
dc.identifier.issn	2474-1493
dc.identifier.uri	http://hdl.handle.net/10453/185411
dc.description.abstract	Massive integration of photovoltaic PV and energy storage systems ESS in the energy system increases the utilization of renewable energy and system complexity Optimal and centralized scheduling and management of these sources can make them more effective and acceptable in the energy community In this regard this paper proposes a virtual scheduling of the PV ESS system based on a deep learning based data driven approach The proposed system divides the time span into two segments based on the availability of PV generation A deep learning model is applied to predict the day ahead PV generation and demand during the two time segments An optimization algorithm is then applied to schedule the PV and ESS based on the predictive outcomes For validating the proposed system a community microgrid framework is considered where multiple PV and ESS systems are connected In addition the user owned PV ESS systems are controlled by the microgrid The simulation results demonstrate the proposed system s performance in terms of reliability and sustainability
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2024 IEEE 34th Australasian Universities Power Engineering Conference (AUPEC)
dc.relation.ispartof	2024 IEEE 34th Australasian Universities Power Engineering Conference
dc.relation.isbasedon	10.1109/aupec62273.2024.10807569
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Data Driven Approach for User-owned Renewable Energy Sources Allocation in Community Microgrid
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sydney, 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	in_progress	*
dc.date.updated	2025-02-27T22:06:56Z
pubs.finish-date	2024-11-22
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2024-11-20
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Massive integration of photovoltaic PV and energy storage systems ESS in the energy system increases the utilization of renewable energy and system complexity Optimal and centralized scheduling and management of these sources can make them more effective and acceptable in the energy community In this regard this paper proposes a virtual scheduling of the PV ESS system based on a deep learning based data driven approach The proposed system divides the time span into two segments based on the availability of PV generation A deep learning model is applied to predict the day ahead PV generation and demand during the two time segments An optimization algorithm is then applied to schedule the PV and ESS based on the predictive outcomes For validating the proposed system a community microgrid framework is considered where multiple PV and ESS systems are connected In addition the user owned PV ESS systems are controlled by the microgrid The simulation results demonstrate the proposed system s performance in terms of reliability and sustainability

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http://hdl.handle.net/10453/185411