This letter 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.
@article{arxiv.2308.09603,
title = {A Convergence Predictor Model for Consensus-based Decentralised Energy Markets},
author = {Parikshit Pareek and L. P. Mohasha Isuru Sampath and Hung D. Nguyen and Eddy Y. S. Foo},
journal= {arXiv preprint arXiv:2308.09603},
year = {2023}
}