  Large-scale power failures are induced by nearly all natural disasters from
hurricanes to wild fires. A fundamental problem is whether and how recovery
guided by government policies is able to meet the challenge of a wide range of
disruptions. Prior research on this problem is scant due to lack of sharing
large-scale granular data at the operational energy grid, stigma of revealing
limitations of services, and complex recovery coupled with policies and
customers. As such, both quantification and firsthand information are lacking
on capabilities and fundamental limitation of energy services in response to
extreme events. Furthermore, government policies that guide recovery are often
sidelined by prior study. This work studies the fundamental problem through the
lens of recovery guided by two commonly adopted policies. We develop data
analysis on unsupervised learning from non-stationary data. The data span
failure events, from moderate to extreme, at the operational distribution grid
during the past nine years in two service regions at the state of New York and
Massachusetts. We show that under the prioritization policy favoring large
failures, recovery exhibits a surprising scaling property which counteracts
failure scaling on the infrastructure vulnerability. However, heterogeneous
recovery widens with the severity of failure events: large failures that cannot
be prioritized increase customer interruption time by 47 folds. And, prolonged
small failures dominate the entire temporal evolution of recovery.
