Increasing resiliency against information vulnerability of renewable resources in the operation of smart multi-area microgrid

With the widespread use of information and communication technologies in smart grids, the vulnerability of these networks has increased significantly. In this paper, the operation of a smart electrical energy system is evaluated by considering the information vulnerability of renewable generators and their sensors. Hence, the false data injection process is modelled by the probability distribution function and different deviations to achieve real conditions. Since the attackers may have various information, an observation-action method is utilized to enhance their capability. Accordingly, an auxiliary variable is considered for real-time decisions and any modification which is required in the process. In return, to resilience the system and mitigate the impact of false data injection, a machine learning method, namely adaptive neuro fuzzy inference system, is used based on a threshold index. Implementing the method on a smart multi-area microgrid shows that if all data points are exposed to attack, the operation cost will be affected by about 8.52% and at least 70% of the false data into each sensor will be detectable. Moreover, sensitivity analysis validates that the wrong decision may be taken by attackers in real-time and, the percentage of detection will decrease if the threshold index increases. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper evaluates the operation of smart electrical energy system by considering the information vulnerability of renewable generators and their sensors. To prevent false data injection, an adaptive neuro fuzzy inference system is used. The study shows that attackers may make wrong decisions in real-time, and the detection rate decreases as the threshold index increases.