We aim to provide a tool for independent system operators to detect the collusion and identify the colluding firms by using day-ahead data. In this paper, an approach based on supervised machine learning is presented for collusion detection in electricity markets. The possible scenarios of the collusion among generation firms are firstly identified. Then, for each scenario and possible load demand, market equilibri‐ um is computed. Market equilibrium points under different col‐ lusions and their peripheral points are used to train the collu‐ sion detection machine using supervised learning approaches such as classification and regression tree (CART) and support vector machine (SVM) algorithms. By applying the proposed ap‐ proach to a four-firm and ten-generator test system, the accura‐ cy of the proposed approach is evaluated and the efficiency of SVM and CART algorithms in collusion detection are com‐ pared with other supervised learning and statistical techniques.

Keywords