Probabilistic load curtailment estimation using posterior probability model and twin support vector machine

Abstract

Estimating the potential load curtailments as a result of hurricane is of great significance in improving the emergency response and recovery of power grid. This paper proposes a three-step sequential method in identifying such load curtailments prior to hurricane. In the first step, a twin support vector machine (TWSVM) model is trained on path/intensity information of previous hurricanes to enable a deterministic outage state assessment of the grid components in response to upcoming events. The TWSVM model is specifically used as it is suitable for handling imbalanced datasets. In the second step, a posterior probability sigmoid model is trained on the obtained results to convert the deterministic results into probabilistic outage states. These outage states enable the formation of probability-weighted contingency scenarios. Finally, the obtained component outages are integrated into a load curtailment estimation model to determine the expected potential load curtailments in the grid. The simulation results, tested on the standard IEEE 118-bus system and based on synthetic datasets, illustrate the high accuracy performance of the proposed method.