Frequency violations from random disturbances: an MCMC approach
For power system operators, this method provides a tool to assess frequency stability risks under rare, large disturbances, addressing a critical need in systems with high renewable penetration.
The paper develops a novel MCMC method (ghost sampling) to efficiently sample rare power disturbances that cause RoCoF violations in power systems, enabling statistical analysis of violation probabilities and generator disconnection risks.
The frequency stability of power systems is increasingly challenged by various types of disturbances. In particular, the increasing penetration of renewable energy sources is increasing the variability of power generation and at the same time reducing system inertia against disturbances. In this paper we are particularly interested in understanding how rate of change of frequency (RoCoF) violations could arise from unusually large power disturbances. We devise a novel specialization, named ghost sampling, of the Metropolis-Hastings Markov Chain Monte Carlo method that is tailored to efficiently sample rare power disturbances leading to nodal frequency violations. Generating a representative random sample addresses important statistical questions such as "which generator is most likely to be disconnected due to a RoCoF violation?" or "what is the probability of having simultaneous RoCoF violations, given that a violation occurs?" Our method can perform conditional sampling from any joint distribution of power disturbances including, for instance, correlated and non-Gaussian disturbances, features which have both been recently shown to be significant in security analyses.