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  5. Towards Imposing Load Shedding Hierarchies based on Tokenised Self Assessed Licenses
 
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Towards Imposing Load Shedding Hierarchies based on Tokenised Self Assessed Licenses

Author(s)
Cuffe, Paul  
de Villiers, Almero  
Uri
http://hdl.handle.net/10197/25705
Date Issued
2021-07-02
Date Available
2024-04-23T08:52:11Z
Abstract
This piece serves as an exploratory study into the combination of blockchain-aware smart meters and radical economic paradigms. Load shedding is examined through this novel lens. Load shedding is an established means of preventing system collapse under emergency conditions. An auctioned load shedding
hierarchy method is proposed whereby participating parties bid on tokens (SALtoks) guaranteeing access to supply during load shedding. This method is based on the recently developed Self Assessed Licenses Sold via Auction paradigm developed. Under this paradigm, SALtoks are subject to taxes based on valuations,
while also being subject to continuous auction. A case study is performed on a hypothetical island power system. Higher ranking SALtoks are held by larger consumers. The method is found to mitigate economic impacts by allowing industry to maintain supply during load shedding. Smaller consumers, while subject to
more frequent interruptions, gain the public benefit of increased income from taxation.
Other Sponsorship
Sustainable Energy Authority of Ireland (SEAI)
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2021 IEEE
Subjects

Load shedding

Blockchain

Smart energy

Tokenised energy

DOI
10.1109/PowerTech46648.2021.9494957
Web versions
https://www.powertech2021.com/
Language
English
Status of Item
Peer reviewed
Journal
2021 IEEE Madrid PowerTech
Conference Details
PowerTech 2021, Madrid, Spain (held online due to Coronavirus outbreak), 28 June - 2 July 2021
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Electrical and Electronic Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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