  Uncertainty in renewable energy generation has the potential to adversely
impact the operation of electric networks. Numerous approaches to manage this
impact have been proposed, ranging from stochastic and chance-constrained
programming to robust optimization. However, these approaches either tend to be
conservative or leave the system vulnerable to low probability, high impact
uncertainty realizations. To address this issue, we propose a new formulation
for stochastic optimal power flow that explicitly distinguishes between "normal
operation", in which automatic generation control (AGC) is sufficient to
guarantee system security, and "adverse operation", in which the system
operator is required to take additional actions, e.g., manual reserve
deployment. The new formulation has been compared with the classical ones in a
case study on the IEEE-118 and IEEE-300 bus systems. We observe that our
consideration of extreme scenarios enables solutions that are more secure than
typical chance-constrained formulations, yet less costly than solutions that
guarantee robust feasibility with only AGC.
