  Adversarial attacks on data-driven algorithms applied in the power system
will be a new type of threat to grid security. Literature has demonstrated that
the adversarial attack on the deep-neural network can significantly mislead the
load fore-cast of a power system. However, it is unclear how the new type of
attack impacts the operation of the grid system. In this research, we manifest
that the adversarial algorithm attack induces a significant cost-increase risk
which will be exacerbated by the growing penetration of intermittent renewable
energy. In Texas, a 5% adversarial attack can increase the total generation
cost by 17% in a quarter, which accounts for around $20 million. When
wind-energy penetration increases to over 40%, the 5% adversarial attack will
inflate the genera-tion cost by 23%. Our research discovers a novel approach to
defending against the adversarial attack: investing in the energy-storage
system. All current literature focuses on developing algorithms to defend
against adversarial attacks. We are the first research revealing the capability
of using the facility in a physical system to defend against the adversarial
algorithm attack in a system of the Internet of Things, such as a smart grid
system.
