A convergence predictor model for consensus-based decentralised energy markets

• المؤلفون الرئيسيون: Pareek, Parikshit, Sampath, Lahanda Purage Mohasha Isuru, Nguyen, Hung D., Foo, Eddy Yi Shyh
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2024
• الموضوعات: Engineering; Convergence prediction model; Energy
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/178568

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.