Energy Management Under Uncertainty for Hybrid Microgrids: From Data to Decision-Making

Moazzen, F; Hossain, MJ; Li, L; Mohammadi-Ivatloo, B

Energy Management Under Uncertainty for Hybrid Microgrids: From Data to Decision-Making

Moazzen, F

Hossain, MJ Li, L Mohammadi-Ivatloo, B

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Publisher:: INST ENGINEERING TECHNOLOGY-IET
Publication Type:: Journal Article
Citation:: IET RENEWABLE POWER GENERATION, 2025, 19, (1)
Issue Date:: 2025-01

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Field	Value	Language
dc.contributor.author	Moazzen, F https://orcid.org/0000-0002-1834-662X
dc.contributor.author	Hossain, MJ
dc.contributor.author	Li, L
dc.contributor.author	Mohammadi-Ivatloo, B
dc.date.accessioned	2026-01-05T02:56:38Z
dc.date.available	2026-01-05T02:56:38Z
dc.date.issued	2025-01
dc.identifier.citation	IET RENEWABLE POWER GENERATION, 2025, 19, (1)
dc.identifier.issn	1752-1416
dc.identifier.issn	1752-1424
dc.identifier.uri	http://hdl.handle.net/10453/191203
dc.description.abstract	ABSTRACT The increasing adoption of distributed energy resources has greatly amplified interest in microgrids, whose effective, reliable and resilient operation relies on the performance of their energy management systems (EMS). These systems ensure the economic operation and maintain load‐generation balance. A practical microgrid EMS (M‐EMS) incorporates data monitoring, variable forecasting, resource allocation and online supervision to optimise the system while interacting with electricity markets. However, in the inherently uncertain environment of both stand‐alone and grid‐connected microgrids, variations in key variables can significantly impact the decision‐making outcomes of M‐EMS. This review paper explores various sources of uncertainties within microgrids, including forecast errors and uncertainties arising from modelling approximations or monitoring inaccuracies. It also provides insights into handling these uncertainties by thoroughly reviewing the pertinent literature and exploring strategies such as analytical methods and AI‐based approaches for capturing them. The eventual goal of handling the uncertainties is to enhance system reliability and security through robust energy management solutions. Furthermore, practical measures to mitigate uncertainties are discussed. The practical implementation of these concepts is illustrated through a review of commercially available M‐EMS solutions and real‐world projects demonstrating their effectiveness in managing energy resources. This paper aims to help both researchers and industry professionals perceive the uncertainties within M‐EMS and how to handle them to achieve accurate, optimal solutions and avoid unexpected costs. Emerging trends and future research directions are also outlined.
dc.language	English
dc.publisher	INST ENGINEERING TECHNOLOGY-IET
dc.relation	http://purl.org/au-research/grants/arc/LP190101251
dc.relation.ispartof	IET RENEWABLE POWER GENERATION
dc.relation.isbasedon	10.1049/rpg2.70174
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Energy
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.subject.classification	4011 Environmental engineering
dc.title	Energy Management Under Uncertainty for Hybrid Microgrids: From Data to Decision-Making
dc.type	Journal Article
utslib.citation.volume	19
utslib.for	0906 Electrical and Electronic 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 Electrical and Data Engineering
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/Engineering and IT Related HDR Students
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	2026-01-05T02:56:35Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	19
utslib.citation.issue	1

Abstract:

ABSTRACT The increasing adoption of distributed energy resources has greatly amplified interest in microgrids, whose effective, reliable and resilient operation relies on the performance of their energy management systems (EMS). These systems ensure the economic operation and maintain load‐generation balance. A practical microgrid EMS (M‐EMS) incorporates data monitoring, variable forecasting, resource allocation and online supervision to optimise the system while interacting with electricity markets. However, in the inherently uncertain environment of both stand‐alone and grid‐connected microgrids, variations in key variables can significantly impact the decision‐making outcomes of M‐EMS. This review paper explores various sources of uncertainties within microgrids, including forecast errors and uncertainties arising from modelling approximations or monitoring inaccuracies. It also provides insights into handling these uncertainties by thoroughly reviewing the pertinent literature and exploring strategies such as analytical methods and AI‐based approaches for capturing them. The eventual goal of handling the uncertainties is to enhance system reliability and security through robust energy management solutions. Furthermore, practical measures to mitigate uncertainties are discussed. The practical implementation of these concepts is illustrated through a review of commercially available M‐EMS solutions and real‐world projects demonstrating their effectiveness in managing energy resources. This paper aims to help both researchers and industry professionals perceive the uncertainties within M‐EMS and how to handle them to achieve accurate, optimal solutions and avoid unexpected costs. Emerging trends and future research directions are also outlined.

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http://hdl.handle.net/10453/191203