  In a grid with a significant share of renewable generation, operators will
need additional tools to evaluate the operational risk due to the increased
volatility in load and generation. The computational requirements of the
forward uncertainty propagation problem, which must solve numerous
security-constrained economic dispatch (SCED) optimizations, is a major barrier
for such real-time risk assessment. This paper proposes a Just-In-Time Risk
Assessment Learning Framework (JITRALF) as an alternative. JITRALF trains risk
surrogates, one for each hour in the day, using Machine Learning (ML) to
predict the quantities needed to estimate risk, without explicitly solving the
SCED problem. This significantly reduces the computational burden of the
forward uncertainty propagation and allows for fast, real-time risk estimation.
The paper also proposes a novel, asymmetric loss function and shows that models
trained using the asymmetric loss perform better than those using symmetric
loss functions. JITRALF is evaluated on the French transmission system for
assessing the risk of insufficient operating reserves, the risk of load
shedding, and the expected operating cost.
