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Uncertainty in renewable energy generation has the potential to adverselyimpact the operation of electric power systems. Numerous approaches to managethis impact have been proposed, ranging from stochastic and chance-constrainedprogramming to robust optimization. However, these approaches either tend to beconservative in their assumptions, causing higher-than-necessary costs, orleave the system vulnerable to low probability, high impact uncertaintyrealizations. To address this issue, we propose a new formulation forstochastic 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. This formulation can be understood as a combination of a jointchance-constrained problem, which enforces that AGC should be sufficient with ahigh probability, with specific treatment of large-disturbance scenarios thatallows for additional, more complex redispatch actions. The new formulation hasbeen compared with the classical ones in a case study on the IEEE-118 bussystem. We observe that our consideration of extreme scenarios enablessolutions that are more secure than typical chance-constrained formulations,yet less costly than solutions that guarantee robust feasibility with only AGC.

Integrating Automatic and Manual Reserves in Optimal Power Flow via Chance Constraints