Intelligent Blockchain Group-based Methodology for Renewable Energy Trading for Local Communities

Alhazmi, Rania Masad

Intelligent Blockchain Group-based Methodology for Renewable Energy Trading for Local Communities

Alhazmi, Rania Masad

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Publication Type:: Thesis
Issue Date:: 2023

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Field	Value	Language
dc.contributor.author	Alhazmi, Rania Masad
dc.date.accessioned	2023-10-17T03:42:46Z
dc.date.available	2023-10-17T03:42:46Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10453/172726
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The integration of renewable energy resources (RES) into energy systems has led to the emergence of the local energy market (LEM), facilitating the exchange and balancing of RES among consumers and prosumers. However, the long-term viability of the LEM necessitates innovative and secure decentralized technologies such as blockchain. While several blockchain-based energy trading platforms exist, they overlook empowering small-scale prosumers and bridging the gap between them and industry giants, failing to adopt sustainable group methodologies. Furthermore, the absence of research on assisting consumers in energy trading decisions based on their preferences is evident. This research addresses these challenges by proposing a novel methodology for community-based renewable energy trading. Leveraging blockchain for marketplace security, and integrating artificial intelligence (AI) techniques for intelligent group trading and decision-making support, we present the Intelligent Blockchain Group Energy Trading (IBGT) platform. A proof of concept prototype was developed, establishing a sustainable, decentralized, and intelligent group-based framework for local energy communities. The IBGT platform comprises three key frameworks: the intelligent renewable energy selling group (RESG) framework, the reputation-based prosumer assessment framework, and the renewable energy surplus selection and trading framework. The intelligent RESG framework consists of an intelligent reputation-based RESG formation system, an intelligent RESG membership system, and an intelligent RESG selling offer management system, collectively managing the life cycle of selling group formation. To ensure group viability, the reputation-based prosumer assessment framework evaluates prosumers based on predetermined criteria. The renewable energy surplus selection and trading framework provides consumers with an AI-driven decision support system for efficient matching and trading. Findings underscore the significance of reputation value in energy trading and highlight the potential of the proposed blockchain-based architecture. Results demonstrate that integrating reputation value and blockchain enhances energy trading efficiency, yielding increased financial gains and overall market efficiency. These insights strongly advocate for the adoption of the proposed formation, assessment, and trading techniques in future energy trading platforms. In conclusion, this research contributes a pioneering framework for sustainable and intelligent community-based renewable energy trading, bridging the gap between small-scale prosumers and industry players. The IBGT platform showcases the transformative potential of blockchain and AI in revolutionizing energy markets. The presented methodologies offer a compelling case for their integration into future energy trading systems, advancing the development of renewable energy integration and fostering a more resilient and efficient energy ecosystem.	en_US.UTF-8
dc.format	Thesis (PhD)
dc.language.iso	en_US	en_US.UTF-8
dc.relation	https://opus.lib.uts.edu.au/bitstream/10453/172726/1/thesis.pdf
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	The author owns the copyright in this thesis including all reproduction and reuse rights for the work. The work may not be altered without the permission of the copyright owner. Attribution is essential when quoting or paraphrasing from this thesis.
dc.rights	© 2023 Rania Masad Alhazmi
dc.rights	au.edu.uts.lib/nph
dc.title	Intelligent Blockchain Group-based Methodology for Renewable Energy Trading for Local Communities	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2025-08-01T00:00:00+1000

Abstract:

The integration of renewable energy resources (RES) into energy systems has led to the emergence of the local energy market (LEM), facilitating the exchange and balancing of RES among consumers and prosumers. However, the long-term viability of the LEM necessitates innovative and secure decentralized technologies such as blockchain. While several blockchain-based energy trading platforms exist, they overlook empowering small-scale prosumers and bridging the gap between them and industry giants, failing to adopt sustainable group methodologies. Furthermore, the absence of research on assisting consumers in energy trading decisions based on their preferences is evident. This research addresses these challenges by proposing a novel methodology for community-based renewable energy trading. Leveraging blockchain for marketplace security, and integrating artificial intelligence (AI) techniques for intelligent group trading and decision-making support, we present the Intelligent Blockchain Group Energy Trading (IBGT) platform. A proof of concept prototype was developed, establishing a sustainable, decentralized, and intelligent group-based framework for local energy communities. The IBGT platform comprises three key frameworks: the intelligent renewable energy selling group (RESG) framework, the reputation-based prosumer assessment framework, and the renewable energy surplus selection and trading framework. The intelligent RESG framework consists of an intelligent reputation-based RESG formation system, an intelligent RESG membership system, and an intelligent RESG selling offer management system, collectively managing the life cycle of selling group formation. To ensure group viability, the reputation-based prosumer assessment framework evaluates prosumers based on predetermined criteria. The renewable energy surplus selection and trading framework provides consumers with an AI-driven decision support system for efficient matching and trading. Findings underscore the significance of reputation value in energy trading and highlight the potential of the proposed blockchain-based architecture. Results demonstrate that integrating reputation value and blockchain enhances energy trading efficiency, yielding increased financial gains and overall market efficiency. These insights strongly advocate for the adoption of the proposed formation, assessment, and trading techniques in future energy trading platforms. In conclusion, this research contributes a pioneering framework for sustainable and intelligent community-based renewable energy trading, bridging the gap between small-scale prosumers and industry players. The IBGT platform showcases the transformative potential of blockchain and AI in revolutionizing energy markets. The presented methodologies offer a compelling case for their integration into future energy trading systems, advancing the development of renewable energy integration and fostering a more resilient and efficient energy ecosystem.

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