Detection of Potential Manipulations in Electricity Market using Machine Learning Approaches

Abstract

Detecting potential manipulations by monitoring trading activities in the electricity market is a time- consuming and challenging task despite the involvement of experienced market surveillance experts. This is due to the increasing complexity of the market structure, contributing to the increase of deceptive anomalous behaviours that can be considered as market abuses. In this paper, we present a novel methodology for detecting potential manipulations in the Nordic day-ahead electricity market by using bid curves data. We first develop a method for processing and reducing the dimensionality of the historical bid curves data using statistical techniques. Then, we train unsupervised machine learning-based models to detect outliers in the pre-processed data. Our methodology captures the sensitivity of the electricity prices resulting from the competitive bidding process and predicts anomalous market behaviours. The results of our experiments show that the proposed approach can compleme nt human experts in market monitoring, by pointing towards relevant cases of manipulation, demonstrating the applicability of the approach.