Mühlpfordt, TillmannTillmannMühlpfordtHagenmeyer, VeitVeitHagenmeyerFaulwasser, TimmTimmFaulwasser2024-03-062024-03-062018-08-2020th Power Systems Computation Conference, PSCC 20189781910963104https://hdl.handle.net/11420/46276The operation of power systems has become more challenging due to feed-in of volatile renewable energy sources. Chance-constrained optimal power flow (CCOPF) is one possibility to explicitly consider volatility via probabilistic uncertainties resulting in mean-optimal feedback policies. These policies are computed before knowledge of the realization of the uncertainty is available. On the other hand, the hypothetical case of computing the power injections knowing every realization beforehand-called in-hindsight OPF (hoPF)-cannot be outperformed w.r.t. costs and constraint satisfaction. In this paper, we investigate how ccOPF feedback relates to the full-information hOPF. To this end, we introduce different dimensions of the price of uncertainty. Using mild assumptions on the uncertainty we present sufficient conditions when ccOPF is identical to hOPF. We suggest using the total variational distance of probability densities to quantify the performance gap of hO PF and ccOPF. Finally, we draw upon a tutorial example to illustrate our results.enChance constraintsOptimal power flowOptimizationUncertaintyComputer SciencesMathematicsConference Paper10.23919/PSCC.2018.8442162Conference Paper