Ebha Koley

The penetration of information and communication technologies (ICT) and the Internet of Things (IoT) in the smart grid (SG) power network paradigm has widened the landscape of cyber threats aiming at disrupting the grid operations. The wider exposure of the communication infrastructure increases the vulnerability of smart grids towards cyber intrusions. The most common impactful intrusions (commonly referred to as false data injection attack (FDIA)) involve falsification of sensor measurement while it is being transmitted to the control centre. Among FDIAs, the load redistribution(LR) attack causes the maximum disruption on the grid operations without being detected at the control centre. During the LR attack, the intruder attempts to virtually manipulate the sensor information at different locations. Analyzing the cyber vulnerability of power networks demands identifying possible LR attack scenarios that disrupt the grid operations to the maximum extent. In this regard, the present work proposes monitoring metrics, Cyber Resiliency Index (CRI), Cyber Severity Index (CSI) and Cyber Market Index (CMI) to quantify the impact of measurement falsification through LR attack scheme, executed with minimum budget and resources. The attack design task has been framed as a multi-objective optimization based on the trade-off between the objectives of maximum disruption and minimum attack cost. A set of solutions (Pareto-front), each pertaining to a bus location for LR attack, is derived post-convergence of the optimization problem. The proposed metrics have been validated for different attack scenarios in a real-time environment.

The increasing role of internet technology in the smart grid operation has raised its vulnerability to eavesdropping attacks, which aim at disrupting the electricity market operations. Preserving the integrity of electricity market operations demands securing the sensor information while it is transmitted through the communication channels. Encrypting sensor measurements enables retaining the authenticity of the electricity market during intrusion. The naive approach of encrypting the entire dataset has scalability issues and financial implications for larger networks. With the high-volume of data acquired from all the sensors in larger networks, the computational load and latency of the embedded devices used for the implementation of the encryption algorithms increases proportionately. In this regard, a scalable, lightweight approach based on graph theory has been proposed for securing the privacy of sensor information and hence maintaining the authenticity of the market management system (MMS) during cyber intrusions. The proposed graph-theory-based scheme allows for enhancing scalability by identifying critical nodes using maximum node connectivity and system observability. Selection of critical nodes reduces the volume of encrypted data since the sensor information acquired from the critical nodes is considered for encryption, thereby scaling the adoption of the approach for larger networks.The scheme mitigates the impact of false data injection attack (FDIA), aiming to manipulate the locational marginal price (LMP) and hence, induce profit/loss to a targeted utility/consumer. The validation on a low-cost, real-time co-simulation environment for IEEE 118 and 2383 bus Polish networks confirms the scalability of the scheme, for practical large-scale grid models.

The integration of information and communication technology tools in the smart grid network has made the electricity market operation vulnerable to cyberattacks. The attacks aim at compromising the integrity of market operations by causing profit/loss to a targeted utility/consumer. Injection of false data on sensor measurement allows for maliciously controlling the locational marginal price (LMP) through the state variable. In this work, we propose a false data injection attack (FDIA) on the market operation with the objectives of maximally deviating the LMP and minimizing the attack budget. Considering the conflict between the two objectives, a tradeoff approach based on multiobjective optimization has been proposed. The solution derived postconvergence of the optimization problem provides a set of solutions (Pareto-front), each pertaining to bus locations on which FDIA can be carried out. The solution allows an intruder to design an FDIA based on the priority assigned between market disruption and attack budget. For a given budget for launching an attack, the Pareto-front provides the optimal FDIA configuration for which the deviation in LMP is maximized. To validate the proposed design scheme in practical settings, a real-time testbed is developed involving the cosimulation of the power and communication networks.