Assessing Risk from Cascading Blackouts Given Correlated Component Failures

Risk of Cascading Blackouts Given Correlated Component Outages

“…The Polish test case, examined in previous work on risk estimation [8], [9], [29], contains 2383 buses and 2896 branches at a peak winter load and is distributed with the MATPOWER simulation package [33]. The true spatial locations of branches and buses are not publicly available for this test case, so hypothetical locations were inferred based on a graph layout of the grid topology, assuming branches are straight lines between buses ( Fig.…”

“…For the results presented here, all independent outage rates were set equal to the mean outage rate of 0.9158 hours per year provided by the RTS-96 test case [40]. These independent outage rates were deliberately assumed identical for all branches in order to more clearly elucidate the impact of spatial correlations in outage rates, as assessed using (4) [6], [8], [9], [11], [29]. For the larger Western US model, the initial RC subset size a 1 was raised to 320 to increase the probability that the initial subset causes a blackout; thus, the Western US test case used the subset reduction scheme {320, 160, 80, 40, 20, 14, 10, 7, 5}.…”

“…A flexible and generalizable approach to risk estimation given correlated component outages was presented in [29] and used to estimate risk due to N − 2 malignancies in a 2383-bus model of the Polish grid. This paper extends that work in several significant ways including: (i) incorporating the effects of N − 3 malignancies, (ii) studying how the risk due to N − 3, relative to N − 2, malignancies changes as a function of correlation in outage probabilities, and (iii) applying the method to a much larger and more geographically realistic 10,000-bus test case, which necessitated (iv) development of new methods for estimating the total number of N − 3 malignancies.…”

Bayesian hierarchical methods for modeling electrical grid component failures

“…Some of these studies use prescriptive failure models to inform probabilistic models for assessing risk and weighing upgrade policies in simulation [1], [4], [5]. Other studies mine failure data from past events to characterize vulnerabilities in operational power networks, for example to cascading failures [6], hurricanes [7] and earthquakes [8].…”

“…From these component-level failure probabilities we generate a realization y of the number of failures in the system overall. The random variable Y follows the Poisson distribution specified in (6).…”