Rapid Assessment, Visualization, and Mitigation of Cascading Failure Risk in Power Systems

This paper describes a new approach, using "Random Chemistry" sampling, to estimate the risk of large cascading blackouts triggered by multiple contingencies. On a 2383 bus test case the new approach finds the expected value of large-blackout sizes (a measure of risk) two orders of magnitude faster than Monte Carlo sampling, without introducing measurable bias. We also derive a method to compute the sensitivity of blackout risk to individual component-failure probabilities, allowing one to quickly identify low-cost strategies for reducing risk. For example, we show how a 1.9% increase in operational costs reduced the overall risk of cascading failure in a 2383-bus test case by 61%. An examination of how risk changes with load yielded a surprising decrease in cascading failure risk at the highest loadings, due to increased locality in generation and less long-distance transmission. Finally, this paper proposes new visualizations of spatio-temporal patterns in cascading failure risk that could provide valuable guidance to system planners and operators.