  Electricity systems are experiencing increased effects of randomness and
variability due to emerging stochastic assets. The increased effects introduce
new uncertainties into power systems that can impact system operability and
reliability. Existing steady-state methods for assessing system-level
operability and reliability are primarily deterministic, therefore, ill-suited
to capture randomness and variability. This work introduces a probabilistic
steady-state analysis inspired by statistical worst-case circuit analysis to
evaluate the risk of operational violations due to stochastic resources.
Compared to parallelized Monte Carlo analyses (MCS), we have seen up to 24x
improvement in runtime speed using our approach without significant loss of
probabilistic accuracy for a Texas7k low-wind day test system.
