Threat from being Social: Vulnerability Analysis of Social Network Coupled Smart Grid

TL;DR

This paper analyzes the vulnerability of smart grids coupled with social networks to misinformation, proposing a model to identify critical nodes whose misinformation can cause severe grid failures, and suggests mitigation strategies.

Contribution

It introduces the MAPSS problem to identify critical social network nodes affecting smart grid stability and develops algorithms considering information diffusion and cascading failures.

Findings

Algorithms effectively identify critical nodes in test cases.

Misinformation propagation can significantly impact grid stability.

Controlled load shedding reduces cascading failure impact.

Abstract

Social Networks (SNs) have been gradually applied by utility companies as an addition to smart grid and are proved to be helpful in smoothing load curves and reducing energy usage. However, SNs also bring in new threats to smart grid: misinformation in SNs may cause smart grid users to alter their demand, resulting in transmission line overloading and in turn leading to catastrophic impact to the grid. In this paper, we discuss the interdependency in the social network coupled smart grid and focus on its vulnerability. That is, how much can the smart grid be damaged when misinformation related to it diffuses in SNs? To analytically study the problem, we propose the Misinformation Attack Problem in Social-Smart Grid (MAPSS) that identifies the top critical nodes in the SN, such that the smart grid can be greatly damaged when misinformation propagates from those nodes. This problem is challenging as we have to incorporate the complexity of the two networks concurrently. Nevertheless, we propose a technique that can explicitly take into account information diffusion in SN, power flow balance and cascading failure in smart grid integratedly when evaluating node criticality, based on which we propose various strategies in selecting the most critical nodes. Also, we introduce controlled load shedding as a protection strategy to reduce the impact of cascading failure. The effectiveness of our algorithms are demonstrated by experiments on IEEE bus test cases as well as the Pegase data set.