Myles J. Thompson
Blockchain-Based Peer-to-Peer Energy Trading Method
Thompson, Myles J.; Sun, Hongjian; Jiang, Jing
Abstract
Blockchain-enabled peer-to-peer energy trading provides a method for neighbours and communities to trade energy generated from local and distributed renewable energy sources. Effective matching can facilitate greater energy efficiency during transmission, increases user welfare through preference and improves power quality. The proposed algorithm builds upon work to develop a system of scoring an energy transaction. It uses a McAfee-priced double auction, and scores based upon preference of price, locality, and energy generation type, alongside the quantity of energy being traded. The algorithm pre-evaluates transactions to determine the optimal transactional pathway. The transaction carried out is that leading to the greatest cumulative score. Simulated over a range of scenarios, the proposed algorithm provides an average increase in user welfare of 75%. Commercially, the algorithm may be deployed in small to large settlements whilst remaining stable. By reducing power loss, the algorithm allows consumers to save 25% on their cost of energy, whilst providing a 50% increase in the revenue earned by prosumers.
Citation
Thompson, M. J., Sun, H., & Jiang, J. (2022). Blockchain-Based Peer-to-Peer Energy Trading Method. CSEE journal of power and energy systems, 8(5), 1318-1326. https://doi.org/10.17775/cseejpes.2021.00010
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 15, 2021 |
Online Publication Date | Sep 10, 2021 |
Publication Date | 2022-09 |
Deposit Date | Jul 20, 2021 |
Publicly Available Date | Jul 20, 2021 |
Journal | CSEE Journal of Power and Energy Systems |
Print ISSN | 2096-0042 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 8 |
Issue | 5 |
Pages | 1318-1326 |
DOI | https://doi.org/10.17775/cseejpes.2021.00010 |
Public URL | https://durham-repository.worktribe.com/output/1245349 |
Files
Published Journal Article
(1.2 Mb)
PDF
Accepted Journal Article
(946 Kb)
PDF
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
You might also like
Energy-based Predictive Root Cause Analysis for Real-Time Anomaly Detection in Big Data Systems
(2025)
Presentation / Conference Contribution
Integrated Sensing and Communications With Mixed Fields Using Transmit Beamforming
(2024)
Journal Article
Optimal Energy Scheduling of Digital Twins Based Integrated Energy System
(2024)
Presentation / Conference Contribution
Decarbonising Heating with Power-Hydrogen Optimisation
(2024)
Presentation / Conference Contribution
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search