DOI:10.35833/MPCE.2018.000566 A Machine Learning Approach for Collusion Detection in Electricity Markets Based on Nash Equilibrium Theory Page view: 0        Net amount: 91 Author: Peyman Razmi1,Majid Oloomi Buygi1,Mohammad Esmalifalak2 Author Affiliation: 1.Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, Iran;2.Predictive Modeling Department of Fiix Software, Toronto, Canada Foundation: Abstract: 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 equilibrium is computed. Market equilibrium points under different collusions and their peripheral points are used to train the collusion detection machine using supervised learning approaches such as classification and regression tree (CART) and support vector machine (SVM) algorithms. By applying the proposed approach to a four-firm and ten-generator test system, the accuracy of the proposed approach is evaluated and the efficiency of SVM and CART algorithms in collusion detection are compared with other supervised learning and statistical techniques. Keywords: Market power ; collusion detection ; machine learning ; support vector machine (SVM) ; classification and regression tree (CART) ; statistical method Received:August 27, 2018               Online Time:2021/01/22 View Full Text       Download reader