Assessment of Cyber Security in Renewable Electricity Market Considering System Reliability Using Machine Learning

The role of cyber security in facing risks and damages is an essential task. The aim of study is first to the effect of a sabotage sample in security on the power market, this is the unavailability of the production or the power outage. This network is a three-bus system consisting of three wind power generation units. This is achieved by running several scenarios in the system. The problem solution approach is considered in minimizing the costs after attacking in the system. The CPLEX solver solves the mixed integer linear programming (MILP) model. Also, the data observed from the output of the GAMS software is used as the input data of the Python software to use data science such as histogram chart analysis and machine learning algorithms for proper prediction and isolation in different attack situations. As a result, the peak load point shifts after the attack, and also between the used machine learning algorithms, the two-layer neural network has significant accuracy for the presented model. The results of this research show that by using this machine learning algorithm, the system's security level has improved in the face of these damages.