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 20million.Whenwind−energypenetrationincreasestoover40inflatethegenera−tioncostby23defendingagainsttheadversarialattack:investingintheenergy−storagesystem.Allcurrentliteraturefocusesondevelopingalgorithmstodefendagainstadversarialattacks.WearethefirstresearchrevealingthecapabilityofusingthefacilityinaphysicalsystemtodefendagainsttheadversarialalgorithmattackinasystemoftheInternetofThings,suchasasmartgridsystem.