کنفرانس بین المللی فناوری و مدیریت انرژی

صفحه اصلی / نهمین کنفرانس بین المللی فناوری و مدیریت انرژی

Applying Data Science to Assess Cyber Security of Renewable Energy and Reserve Markets to Ensure System Reliability

نویسندگان :

Daryoush Tavangar Rizi¹ Mohammad Hassan Nazari² Seyed Hossein Hosseinian³ Gevork B Gharehpetian⁴ Maryam Fani⁵ Amir Khorsandi⁶

1- دانشگاه صنعتی امیرکبیر 2- Niroo Research Institute 3- Amirkabir University of Technology 4- Amirkabir University of Technology 5- Amirkabir University of Technology 6- Amirkabir University of Technology

کلمات کلیدی :

Renewable Energy،Cyber Security،energy market،machine learning،reliability،support vector machine،Reserve Markets

چکیده :

Cybersecurity plays a vital role in tackling threats and minimizing potential harm. The primary objective of the research is to investigate the impact of intervention on the energy market, specifically the unavailability of resources. This network serves as a small-scale representation of energy and reserve markets, encompassing both of the renewable recourse and lines. This is done by executing multiple situations in the network. After an attack in the system, minimizing costs is achieved through a problem solution approach. The CPLEX solver has used for solving MILP model. Furthermore, the gained optimal information is used for prediction and analysis, enabling the utilization of data science techniques, such as charts and machine learning methods, to accurately predict and identify various scenarios. The attack causes a shift in the peak and minimum of the load. Additionally, the examination of three machine learning algorithms has revealed the usage of support vector machine (SVM) in conjunction with neural networks such as RBF, a second-degree polynomial, and third-degree polynomial. According to the results, the highest performance accuracy of 86% is achieved by integrating SVM with a neural network structure.