Small-Scale DC Power Plants Supported by Blockchain, AI, and IoT Models: a Faster Transition to Sustainability

Pereira Dos Santos, Antonio

Small-Scale DC Power Plants Supported by Blockchain, AI, and IoT Models: a Faster Transition to Sustainability

Pereira Dos Santos, Antonio

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Publication Type:: Thesis
Issue Date:: 2022

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Field	Value	Language
dc.contributor.author	Pereira Dos Santos, Antonio
dc.date.accessioned	2023-07-14T03:58:11Z
dc.date.available	2023-07-14T03:58:11Z
dc.date.issued	2022
dc.identifier.uri	http://hdl.handle.net/10453/171508
dc.description	University of Technology Sydney. Faculty of Engineering and Information Technology.	en_US.UTF-8
dc.description.abstract	The electricity industry has become the major greenhouse gas (GHG) emitter on Earth since every economic activity relies on it. Wealthy nations headquarter the largest transnational corporations, and to remain competitive, they are compelled to stimulate higher consumption levels, and higher productivity at lower costs. How can global GHG emissions be reduced when political economy and law keep rewarding institutions that directly or not, contributes to the release of emissions? How to reduce emissions when technology has mostly been used to magnify production, and boost indiscriminate consumption? The transition to renewable energy sources and carbon offset have been the flagship strategies to lower emissions, sponsored by the United Nations, and eagerly adopted by affluent nations. Conversely, the assumption that an eventual transitioning to renewable sources in a localized region, could yield any substantial contribution to climate change in a global scale is rather weak-willed. It may provide a palliative comfort for the wealthy nations to mask environmental liabilities. However, it fails on addressing the emissions’ root causes, allowing the perpetuation of the problem. This research addresses the emissions dilemma within the electricity industry. It features an in-depth study on global emissions, covering causes, sources, drivers, root causes, and providing specifics why present mitigation strategies have failed. Then, it introduces the ADCx model, a small-scale autonomous DC power plant aiming to provide an alternative root for consumers to become sustainable, away from the large-centralised-polluted AC grid. It prioritises cleaner transformation methods and autonomy, irrespective of the type of power sources. Next, it proposes the BAIoT system, where Blockchain, Artificial Intelligence and IoT work together to provide additional features to the ADCx. While ADCx focuses on the network infrastructure, BAIoT builds upon user re-education, network intelligence, rationalising energy consumption, energy trading, and leveraging power demand and supply. Next, it presents the BAIoTAG framework that establishes the 12 fundamental principles leading to sustainability. It has been strategically conceived to cause minimal impact on the existing AC system, reducing legal barriers, and facilitating cross-country replications. Rather waiting for an effective solution from government, this framework enables citizens to spearhead a local solution, and the formation of off-grid communities. Lastly, this study presents a comparative case study showing how AI/ML can support small-scale power plants in reaching sustainability. The greater the data granularity, the larger the opportunities for superior predictions, maximise network performance and increasing users’ awareness on their local emissions.	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/10453/171508/2/02whole.pdf
dc.rights	info:eu-repo/semantics/openAccess
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	© 2022 Antonio Pereira Dos Santos
dc.rights	au.edu.uts.lib/cph
dc.title	Small-Scale DC Power Plants Supported by Blockchain, AI, and IoT Models: a Faster Transition to Sustainability	en_US.UTF-8
dc.type	Thesis
utslib.copyright.status	open_access	*

Abstract:

The electricity industry has become the major greenhouse gas (GHG) emitter on Earth since every economic activity relies on it. Wealthy nations headquarter the largest transnational corporations, and to remain competitive, they are compelled to stimulate higher consumption levels, and higher productivity at lower costs. How can global GHG emissions be reduced when political economy and law keep rewarding institutions that directly or not, contributes to the release of emissions? How to reduce emissions when technology has mostly been used to magnify production, and boost indiscriminate consumption? The transition to renewable energy sources and carbon offset have been the flagship strategies to lower emissions, sponsored by the United Nations, and eagerly adopted by affluent nations. Conversely, the assumption that an eventual transitioning to renewable sources in a localized region, could yield any substantial contribution to climate change in a global scale is rather weak-willed. It may provide a palliative comfort for the wealthy nations to mask environmental liabilities. However, it fails on addressing the emissions’ root causes, allowing the perpetuation of the problem. This research addresses the emissions dilemma within the electricity industry. It features an in-depth study on global emissions, covering causes, sources, drivers, root causes, and providing specifics why present mitigation strategies have failed. Then, it introduces the ADCx model, a small-scale autonomous DC power plant aiming to provide an alternative root for consumers to become sustainable, away from the large-centralised-polluted AC grid. It prioritises cleaner transformation methods and autonomy, irrespective of the type of power sources. Next, it proposes the BAIoT system, where Blockchain, Artificial Intelligence and IoT work together to provide additional features to the ADCx. While ADCx focuses on the network infrastructure, BAIoT builds upon user re-education, network intelligence, rationalising energy consumption, energy trading, and leveraging power demand and supply. Next, it presents the BAIoTAG framework that establishes the 12 fundamental principles leading to sustainability. It has been strategically conceived to cause minimal impact on the existing AC system, reducing legal barriers, and facilitating cross-country replications. Rather waiting for an effective solution from government, this framework enables citizens to spearhead a local solution, and the formation of off-grid communities. Lastly, this study presents a comparative case study showing how AI/ML can support small-scale power plants in reaching sustainability. The greater the data granularity, the larger the opportunities for superior predictions, maximise network performance and increasing users’ awareness on their local emissions.

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