Smart grid

Identification (biology)

Cascading failure

Electrical engineering

Physics

Psychological resilience

Critical infrastructure protection

Critical infrastructure

Mathematics

Psychotherapist

Biology

Gene

Distributed computing

Engineering

Political science

Interdependence

Psychology

Power (physics)

Vulnerability assessment

Chemistry

Grid

Geometry

Computer science

Botany

Quantum mechanics

Electric power system

Computer security

Biochemistry

Robustness (evolution)

Vulnerability (computing)

Most of today's infrastructure systems can be efficiently operated thanks to the intelligent power supply of the smart grids. However, smart grids are highly vulnerable to malicious attacks, that is, because of the interplay between the components in the smart grids, the failure of some critical components may result in the cascading failure and breakdown of the whole system. Therefore, the question of how to identify the most critical components to protect the smart grid system is the first challenge to operators. To enable the system's robustness, there has been a lot of effort aimed at the system analysis, designing new architectures, and proposing new algorithms. However, these works mainly introduce different ranking methods for link (transmission line) or node (station) identification and directly select most the highest degree nodes or common links as the critical ones. These methods fail to address the problem of interdependencies between components nor consider the role of users that is one of critical factors impacting on the smart grid vulnerability assessment. This motivates us to study a more general and practical problem in terms of smart grid vulnerability assessment, namely the Maximum-Impact through Critical-Line with Limited Budget (MICLLB) problem. The objective of this research is to provide an efficient method to identify critical components in the system by considering a realistic attack scenario.

Cyber Security of Smart Grid: Attacks and Defenses