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.
['Dzung T. Phan', 'Soumyadip Ghosh']
Predicting and mitigating congestion for an electric power system under load and renewable uncertainty