Isolation forest-based anomaly detection through generation offers and optimal dispatch data

Abstract

Firms in the electrical marketplace expect each other to engage in Fair and Free Trade. Identifying anomalies in the electricity marketplace ensures that pricing procedures are reliable and secure. This paper aims to assess the isolation forest method in the machine learning paradigm for detecting anomalies from data simulated in the AMES software. The major limitation of traditional anomaly detection approaches is that statistical and rule-based approaches are less effective in working with big data in the context of the high dimensionality of the modern power market. The paper analyzes the bidding trend of five generation firms (Genco's) within 52 days. The isolation Forest method highlights the presence of abnormalities in Genco parameters (Genco dispatch &amp; reported supply offers) of power market data using Python coding under the Jupyter environment. Anomaly results are validated by making the cyber attack scenarios of gencos from the most common anomalous days in both parameters. Novel contributions of this paper are: 1) Filling the gap in the literature regarding anomaly detection in power markets targeting specific power generating companies' coefficients. 2) Using an accurate and concise Isolation Forest Model of machine learning with AMES software, presenting a clear state of bids &amp; offers, the power market operator, and anomalous detection. 3) Simultaneous anomaly detection in Genco dispatch and Genco supply offers. This paper enhances the anomaly detection process and indicates the utilization of advanced machine learning methodologies for monitoring the power markets comprehensively.</p