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Uncertainty in renewable energy generation has the potential to adverselyimpact the operation of electric networks. Numerous approaches to manage thisimpact have been proposed, ranging from stochastic and chance-constrainedprogramming to robust optimization. However, these approaches either tend to beconservative or leave the system vulnerable to low probability, high impactuncertainty realizations. To address this issue, we propose a new formulationfor stochastic optimal power flow that explicitly distinguishes between "normaloperation", in which automatic generation control (AGC) is sufficient toguarantee system security, and "adverse operation", in which the systemoperator is required to take additional actions, e.g., manual reservedeployment. The new formulation has been compared with the classical ones in acase study on the IEEE-118 and IEEE-300 bus systems. We observe that ourconsideration of extreme scenarios enables solutions that are more secure thantypical chance-constrained formulations, yet less costly than solutions thatguarantee robust feasibility with only AGC.

Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations