A Machine Learning Approach for Collusion Detection in Electricity Markets Based on Nash Equilibrium Theory

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