Short-term probabilistic transmission congestion forecasting

This paper introduces a probabilistic method for short-term transmission congestion forecasting, which is recently developed by EPRI. The proposed method applies the sequential Monte Carlo Simulation (MCS) in a probabilistic load flow as the conceptual framework, adds all the significant uncertainties and their probability distributions to be modeled, develops the models, and most importantly specifies how to accurately model the key input assumptions in order to derive valid confidence levels of the forecasted congestion variables. The developed probabilistic method is successfully applied to the four-area WECC equivalent system. Focus is on the confidence levels of making such forecasts, so that a window of forecast-ability is defined, beyond which any forecast would be considered to contain little actionable information. Within the window of forecast-ability, the probabilistic forecasts of congestion would provide confidence limits and information for ranking the potential benefits of alleviating congestion at the various transmission bottlenecks.