A convergence predictor model for consensus-based decentralised energy markets
This paper introduces a convergence prediction model (CPM) for decentralized market clearing mechanisms. The CPM serves as a tool to detect potential cyber-attacks that affect the convergence of the consensus mechanism during ongoing market clearing operations. In this study, we propose a success...
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sg-ntu-dr.10356-1785682024-06-28T15:38:57Z A convergence predictor model for consensus-based decentralised energy markets Pareek, Parikshit Sampath, Lahanda Purage Mohasha Isuru Nguyen, Hung D. Foo, Eddy Yi Shyh School of Electrical and Electronic Engineering 15th ACM International Conference on Future and Sustainable Energy Systems (E-Energy ’24) Engineering Convergence prediction model Energy This paper introduces a convergence prediction model (CPM) for decentralized market clearing mechanisms. The CPM serves as a tool to detect potential cyber-attacks that affect the convergence of the consensus mechanism during ongoing market clearing operations. In this study, we propose a successively elongating Bayesian logistic regression approach to model the probability of convergence of real-time market mechanisms. The CPM utilizes net-power balance among all the prosumers/market participants as a feature for convergence prediction, enabling a low-dimensional model to operate efficiently for all the prosumers concurrently. The results highlight that the proposed CPM has achieved a net false rate of less than 0.01% for a stressed dataset. Published version 2024-06-26T04:53:51Z 2024-06-26T04:53:51Z 2024 Conference Paper Pareek, P., Sampath, L. P. M. I., Nguyen, H. D. & Foo, E. Y. S. (2024). A convergence predictor model for consensus-based decentralised energy markets. 15th ACM International Conference on Future and Sustainable Energy Systems (E-Energy ’24), 606-609. https://dx.doi.org/10.1145/3632775.3661987 979-8-4007-0480-2/24/06 https://hdl.handle.net/10356/178568 10.1145/3632775.3661987 606 609 en © 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License. application/pdf |
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Engineering Convergence prediction model Energy Pareek, Parikshit Sampath, Lahanda Purage Mohasha Isuru Nguyen, Hung D. Foo, Eddy Yi Shyh A convergence predictor model for consensus-based decentralised energy markets |
description |
This paper introduces a convergence prediction model (CPM) for
decentralized market clearing mechanisms. The CPM serves as a
tool to detect potential cyber-attacks that affect the convergence of
the consensus mechanism during ongoing market clearing operations. In this study, we propose a successively elongating Bayesian
logistic regression approach to model the probability of convergence of real-time market mechanisms. The CPM utilizes net-power
balance among all the prosumers/market participants as a feature
for convergence prediction, enabling a low-dimensional model to
operate efficiently for all the prosumers concurrently. The results
highlight that the proposed CPM has achieved a net false rate of
less than 0.01% for a stressed dataset. |
author2 |
School of Electrical and Electronic Engineering |
author_facet |
School of Electrical and Electronic Engineering Pareek, Parikshit Sampath, Lahanda Purage Mohasha Isuru Nguyen, Hung D. Foo, Eddy Yi Shyh |
format |
Conference or Workshop Item |
author |
Pareek, Parikshit Sampath, Lahanda Purage Mohasha Isuru Nguyen, Hung D. Foo, Eddy Yi Shyh |
author_sort |
Pareek, Parikshit |
title |
A convergence predictor model for consensus-based decentralised energy markets |
title_short |
A convergence predictor model for consensus-based decentralised energy markets |
title_full |
A convergence predictor model for consensus-based decentralised energy markets |
title_fullStr |
A convergence predictor model for consensus-based decentralised energy markets |
title_full_unstemmed |
A convergence predictor model for consensus-based decentralised energy markets |
title_sort |
convergence predictor model for consensus-based decentralised energy markets |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/178568 |
_version_ |
1806059756643680256 |