  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.
