Risk Characterization of the Electric Grid to Cascading Blackouts
Testing of the electrical grid for robustness to blackouts is currently performed in which one outage is simulated at a time to determine whether the failure of one component might lead to a cascading blackout in so-called “N-1 contingency testing”. This testing ignores the scenario that leads to cascading blackouts in which multiple contingencies occur at approximately the same time. These N-k contingency scenarios (where k is the number of combined outages that combine to create a cascade) are not tested for values of k greater than one because of the combinatorial explosion that results from choosing k elements from N for large N. Thus the combinatorial search space for combinations of contingencies is too large to search in a reasonable amount of time to ensure robustness to these N-k contingencies of even a medium-sized electrical grid. In our work, we use the Random Chemistry algorithm to quickly identify a subset of all combinations of contingencies that lead to cascading blackouts in a cascading failure simulator. This algorithm gives us an unbiased sampling of branch combinations that cause cascading failure faster than if we were to employ a random search on the combinatorial space. We then use this subset of N-k contingencies to estimate the number of expected total number of outage subsets that would lead to cascading failure of the grid. Using this estimate, we develop a risk statistic that can be used to characterize the reliability of a given electrical grid configuration to cascading blackouts.