Smart Grid, Smart FiT: A data-driven approach to optimize microgrid energy market

Habib, MA; Hossain, MJ

Smart Grid, Smart FiT: A data-driven approach to optimize microgrid energy market

Habib, MA Hossain, MJ

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Energy Policy, 2025, 203
Issue Date:: 2025-08-01

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Field	Value	Language
dc.contributor.author	Habib, MA
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2026-02-13T01:23:41Z
dc.date.available	2026-02-13T01:23:41Z
dc.date.issued	2025-08-01
dc.identifier.citation	Energy Policy, 2025, 203
dc.identifier.issn	0301-4215
dc.identifier.issn	1873-6777
dc.identifier.uri	http://hdl.handle.net/10453/193454
dc.description.abstract	The dynamic nature of renewable energy production and customer demand necessitates a flexible approach for designing Feed-in Tariff (FiT) schemes to ensure equity and fairness. This research presents a comprehensive data-driven framework for determining FiT rates by analyzing trends in demand, renewable energy generation, and temperature over time. The proposed method calculates FiT rates that adapt dynamically to evolving scenarios by incorporating both historical and projected trends. To optimize FiT values and offer affordable tariffs beneficial to both energy providers and customers, the proposed approach employs Sequential Model-Based Optimization (SMBO). Case studies using real-world microgrid data showcase the model's adaptability and confirm its reliability by ensuring that the optimized FiT values remain within Australian government-set tariff limits. The SMBO method can decrease computational time by as much as 90%, achieving a Root Mean Square Error of 2.839. Additionally, the dynamic FiT model enhances financial sustainability by shortening the payback period for various prosumers by 17%–22% compared to a fixed FiT. The dynamic FiT adjusts rates based on previous historical and projected trends, incentivizing prosumers to export energy during peak demand. This method supports sustainable energy usage and offers a flexible, efficient pricing mechanism that adapts to the changing energy landscape.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Energy Policy
dc.relation.isbasedon	10.1016/j.enpol.2025.114618
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Energy
dc.subject.classification	3304 Urban and regional planning
dc.subject.classification	4407 Policy and administration
dc.subject.classification	4802 Environmental and resources law
dc.title	Smart Grid, Smart FiT: A data-driven approach to optimize microgrid energy market
dc.type	Journal Article
utslib.citation.volume	203
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	2026-02-13T01:23:36Z
pubs.publication-status	Published
pubs.volume	203

Abstract:

The dynamic nature of renewable energy production and customer demand necessitates a flexible approach for designing Feed-in Tariff (FiT) schemes to ensure equity and fairness. This research presents a comprehensive data-driven framework for determining FiT rates by analyzing trends in demand, renewable energy generation, and temperature over time. The proposed method calculates FiT rates that adapt dynamically to evolving scenarios by incorporating both historical and projected trends. To optimize FiT values and offer affordable tariffs beneficial to both energy providers and customers, the proposed approach employs Sequential Model-Based Optimization (SMBO). Case studies using real-world microgrid data showcase the model's adaptability and confirm its reliability by ensuring that the optimized FiT values remain within Australian government-set tariff limits. The SMBO method can decrease computational time by as much as 90%, achieving a Root Mean Square Error of 2.839. Additionally, the dynamic FiT model enhances financial sustainability by shortening the payback period for various prosumers by 17%–22% compared to a fixed FiT. The dynamic FiT adjusts rates based on previous historical and projected trends, incentivizing prosumers to export energy during peak demand. This method supports sustainable energy usage and offers a flexible, efficient pricing mechanism that adapts to the changing energy landscape.

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http://hdl.handle.net/10453/193454