  This paper studies the vulnerability of large-scale power systems to false
data injection (FDI) attacks through their physical consequences. Prior work
has shown that an attacker-defender bi-level linear program (ADBLP) can be used
to determine the worst-case consequences of FDI attacks aiming to maximize the
physical power flow on a target line. Understanding the consequences of these
attacks requires consideration of power system operations commonly used in
practice, specifically real-time contingency analysis (RTCA) and security
constrained economic dispatch (SCED). An ADBLP is formulated with detailed
assumptions on attacker's knowledge, and a modified Benders' decomposition
algorithm is introduced to solve such an ADBLP. The vulnerability analysis
results presented for the synthetic Texas system with 2000 buses show that
intelligent FDI attacks can cause post-contingency overflows.
