Network Cascade Vulnerability using Constrained Bayesian Optimization
This addresses power grid security by identifying vulnerabilities to cascading failures, which are a primary cause of large-scale blackouts, but the approach is incremental as it builds on existing optimization methods.
The paper tackles the problem of assessing power grid vulnerability by exploring adversarial attacks through modifications of transmission line protection settings to maximize network degradation due to cascading, using a Bayesian optimization method that is agnostic to the cascade simulator. Numerical experiments show that maximally misconfiguring all lines does not cause the most cascading, and even with limited misconfiguration, settings can produce cascades comparable to those with no constraints.
Measures of power grid vulnerability are often assessed by the amount of damage an adversary can exact on the network. However, the cascading impact of such attacks is often overlooked, even though cascades are one of the primary causes of large-scale blackouts. This paper explores modifications of transmission line protection settings as candidates for adversarial attacks, which can remain undetectable as long as the network equilibrium state remains unaltered. This forms the basis of a black-box function in a Bayesian optimization procedure, where the objective is to find protection settings that maximize network degradation due to cascading. Notably, our proposed method is agnostic to the choice of the cascade simulator and its underlying assumptions. Numerical experiments reveal that, against conventional wisdom, maximally misconfiguring the protection settings of all network lines does not cause the most cascading. More surprisingly, even when the degree of misconfiguration is limited due to resource constraints, it is still possible to find settings that produce cascades comparable in severity to instances where there are no resource constraints.