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

Abstract

Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric power systems. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chanceconstrained programming to robust optimization. However, these approaches either tend to be conservative in their assumptions, causing higher-than-necessary costs, 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. This formulation can be understood as a combination of a joint chance-constrained problem, which enforces that AGC should be sufficient with a high probability, with specific treatment of large-disturbance scenarios that allows for additional, more complex redispatch actions. The new formulation has been compared with the classical ones in a case study on the IEEE-118 bus system. 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.