Frequency violations from random disturbances: an MCMC approach

TL;DR

This paper introduces a novel MCMC-based method called ghost sampling to efficiently analyze rare frequency violations in power systems caused by large disturbances, especially considering renewable energy variability.

Contribution

The paper develops a specialized ghost sampling technique for Markov Chain Monte Carlo to effectively sample rare events leading to frequency violations in power systems.

Findings

Efficient sampling of rare frequency violation events.

Ability to analyze correlated and non-Gaussian disturbances.

Insights into generator disconnection probabilities.

Abstract

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.