Design and operation of future low-voltage community microgrids: An AI-based approach with real case study

Alam, MM; Hossain, MJ; Zamee, MA; Al-Durra, A

Design and operation of future low-voltage community microgrids: An AI-based approach with real case study

Alam, MM Hossain, MJ Zamee, MA Al-Durra, A

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Energy, 2025, 377, pp. 124523
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Alam, MM
dc.contributor.author	Hossain, MJ
dc.contributor.author	Zamee, MA
dc.contributor.author	Al-Durra, A
dc.date.accessioned	2024-10-16T09:09:31Z
dc.date.available	2024-10-16T09:09:31Z
dc.date.issued	2025-01-01
dc.identifier.citation	Applied Energy, 2025, 377, pp. 124523
dc.identifier.issn	0306-2619
dc.identifier.uri	http://hdl.handle.net/10453/181425
dc.description.abstract	The utilization of artificial intelligence in the design and operation of a microgrid (MG) can contribute to improve its energy efficiency, resiliency, and cost of energy supply. This research proposes a new approach to conduct a comprehensive analysis for transforming existing low-voltage networks into MGs to achieve the net-zero goal by 2050. A data-driven machine learning-based clustering and profiling approach is designed and implemented to extract the data, constraints, and dependencies from the historical data. Furthermore, the constraints and dependencies are utilized for determining the renewable energy sources’ capacity. A Bi-level optimization technique is developed to ensure appropriate coordination of cost and renewable energy source (RES) capacity. A comprehensive analysis is carried out utilizing real historical demand and generation data of an energy community in Australia. Based on the clustered analysis, the consecutive day's data are considered for the analysis. The findings reveal that the proposed microgrids achieve higher renewable RES utilization and lower electricity costs compared to grid-connected systems, with the potential to reduce carbon emissions by up to 98.23% when transitioning from coal-based grid systems to the proposed microgrid system. Additionally, a transformation from a grid time-of-use tariff-based system to the proposed microgrid setup can lead to a cost reduction of 65.45%. These case studies will also assist the researcher in identifying new, potential ideas and industries to accelerate the implementation of remote community microgrids.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2024.124523
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	09 Engineering, 14 Economics
dc.subject.classification	Energy
dc.subject.classification	33 Built environment and design
dc.subject.classification	38 Economics
dc.subject.classification	40 Engineering
dc.title	Design and operation of future low-voltage community microgrids: An AI-based approach with real case study
dc.type	Journal Article
utslib.citation.volume	377
utslib.for	09 Engineering
utslib.for	14 Economics
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.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-10-16T09:09:27Z
pubs.publication-status	Accepted
pubs.volume	377

Abstract:

The utilization of artificial intelligence in the design and operation of a microgrid (MG) can contribute to improve its energy efficiency, resiliency, and cost of energy supply. This research proposes a new approach to conduct a comprehensive analysis for transforming existing low-voltage networks into MGs to achieve the net-zero goal by 2050. A data-driven machine learning-based clustering and profiling approach is designed and implemented to extract the data, constraints, and dependencies from the historical data. Furthermore, the constraints and dependencies are utilized for determining the renewable energy sources’ capacity. A Bi-level optimization technique is developed to ensure appropriate coordination of cost and renewable energy source (RES) capacity. A comprehensive analysis is carried out utilizing real historical demand and generation data of an energy community in Australia. Based on the clustered analysis, the consecutive day's data are considered for the analysis. The findings reveal that the proposed microgrids achieve higher renewable RES utilization and lower electricity costs compared to grid-connected systems, with the potential to reduce carbon emissions by up to 98.23% when transitioning from coal-based grid systems to the proposed microgrid system. Additionally, a transformation from a grid time-of-use tariff-based system to the proposed microgrid setup can lead to a cost reduction of 65.45%. These case studies will also assist the researcher in identifying new, potential ideas and industries to accelerate the implementation of remote community microgrids.

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http://hdl.handle.net/10453/181425