Energy data security and pricing model in local energy markets using artificial intelligence

Tabassum, F; Azim, MI; Islam, MR; Rahman, MA; Ali, L; Rahman, MM; Hossain, MJ

Energy data security and pricing model in local energy markets using artificial intelligence

Tabassum, F Azim, MI Islam, MR Rahman, MA Ali, L Rahman, MM Hossain, MJ

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Applied Energy, 2025, 401, pp. 126737
Issue Date:: 2025-12-15

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Field	Value	Language
dc.contributor.author	Tabassum, F
dc.contributor.author	Azim, MI
dc.contributor.author	Islam, MR
dc.contributor.author	Rahman, MA
dc.contributor.author	Ali, L
dc.contributor.author	Rahman, MM
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2026-02-18T04:01:40Z
dc.date.available	2026-02-18T04:01:40Z
dc.date.issued	2025-12-15
dc.identifier.citation	Applied Energy, 2025, 401, pp. 126737
dc.identifier.issn	0306-2619
dc.identifier.uri	http://hdl.handle.net/10453/193575
dc.description.abstract	The increasing adoption of local energy markets has introduced new opportunities for decentralized energy trading but has rendered these systems vulnerable to significant cyberthreats. For local energy markets to remain trustworthy and reliable for efficient energy trading, data availability and integrity must be guaranteed. However, due to the use of contemporary information and communication technologies, these systems are becoming more susceptible to cyberthreats, such as distributed denial of service and false data injection attacks, which can interfere with regular business operations and jeopardize the fairness of trading. This article presents a comprehensive framework utilizing artificial intelligence to ensure a secure bilateral trading environment by identifying corrupted trading data, preventing customers from reacting to it, and mitigating threats’ impact on it. In addition, the proposed framework suggests a new real-time optimal trading price-giving model based on artificial intelligence to improve the financial benefits for both sellers and buyers. The framework's effectiveness in maintaining trading data security and operational resilience is demonstrated through a thorough analysis. The simulation results testify that the designed trading price-giving approach benefits both sellers and buyers more than business-as-usual. Moreover, how the secured trading data sharing environment helps in maintaining financial benefits among customers during attack scenarios is also investigated. This work not only enhances the security and dependability of local energy markets but also emphasizes the financial benefits of implementing artificial intelligence-based schemes in energy trading systems.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Applied Energy
dc.relation.isbasedon	10.1016/j.apenergy.2025.126737
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	Energy data security and pricing model in local energy markets using artificial intelligence
dc.type	Journal Article
utslib.citation.volume	401
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
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
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	2026-02-18T04:01:31Z
pubs.publication-status	Published
pubs.volume	401

Abstract:

The increasing adoption of local energy markets has introduced new opportunities for decentralized energy trading but has rendered these systems vulnerable to significant cyberthreats. For local energy markets to remain trustworthy and reliable for efficient energy trading, data availability and integrity must be guaranteed. However, due to the use of contemporary information and communication technologies, these systems are becoming more susceptible to cyberthreats, such as distributed denial of service and false data injection attacks, which can interfere with regular business operations and jeopardize the fairness of trading. This article presents a comprehensive framework utilizing artificial intelligence to ensure a secure bilateral trading environment by identifying corrupted trading data, preventing customers from reacting to it, and mitigating threats’ impact on it. In addition, the proposed framework suggests a new real-time optimal trading price-giving model based on artificial intelligence to improve the financial benefits for both sellers and buyers. The framework's effectiveness in maintaining trading data security and operational resilience is demonstrated through a thorough analysis. The simulation results testify that the designed trading price-giving approach benefits both sellers and buyers more than business-as-usual. Moreover, how the secured trading data sharing environment helps in maintaining financial benefits among customers during attack scenarios is also investigated. This work not only enhances the security and dependability of local energy markets but also emphasizes the financial benefits of implementing artificial intelligence-based schemes in energy trading systems.

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http://hdl.handle.net/10453/193575