Predicting and mitigating congestion for an electric power system under load and renewable uncertainty

Transmission grids have to handle increasing uncertainty as the proportion of renewables in the generation portfolio rises. This necessitates probabilistic modeling of the impact of renewables uncertainty over the near-future state of the grid. We propose an approach to estimate the probability of the occurrence of a congestion event, which is defined as the event when power flow in a transmission line exceeds critical thermal limits or voltage at a bus exits its safety limits. Certain optimal mitigation actions to minimize the chances of experiencing a congestion event are also modeled. A decomposition algorithm is presented to efficiently solve the multi-period power flow-based optimization formulation. Rare-event simulation techniques are used to evaluate the risk of experiencing a congestion event under this operational model to create the congestion forecasts.