  The potential for cascading failure in power systems adds substantially to
overall reliability risk. Monte Carlo sampling can be used with a power system
model to estimate this impact, but doing so is computationally expensive. This
paper presents a new approach to estimating the risk of large cascading
blackouts triggered by multiple contingencies. The method uses a search
algorithm (Random Chemistry) to identify blackout-causing contingencies, and
then combines the results with outage probabilities to estimate overall risk.
Comparing this approach with Monte Carlo sampling for two test cases (the IEEE
RTS-96 and a 2383 bus model of the Polish system) illustrates that the new
approach is at least two orders of magnitude faster than Monte Carlo, without
introducing measurable bias. Moreover, the approach enables one to compute the
sensitivity of overall blackout risk to individual component-failure
probabilities in the initiating contingency, allowing one to quickly identify
low-cost strategies for reducing risk. By computing the sensitivity of risk to
individual initial outage probabilities for the Polish system, we found that
reducing three line-outage probabilities by 50% would reduce cascading failure
risk by 33%. Finally, we used the method to estimate changes in risk as a
function of load. Surprisingly, this calculation illustrates that risk can
sometimes decrease as load increases.
