Fast Security-Constrained Optimal Power Flow with Time-Aware Critical Contingency Prediction

Accelerating the solution process of preventive Security Constrained Optimal Power Flow (SCOPF) is studied. In an iterative algorithmic framework, knowledge of the relatively sparse critical contingencies can greatly reduce the problem size and hence solution time. Predictors that can predict the critical contingencies that must be included in the SCOPF formulation are trained and integrated into an iterative algorithm. As different types of prediction errors - false negatives and false positives - have markedly different impact on algorithm solution time, a novel time-aware loss function is designed and calibrated for training predictors that directly minimizes algorithm run-time. A multi-objective loss function that incorporates both this time-aware loss and the accuracy promoting binary cross entropy (BCE) loss is then designed and tuned. Comprehensive evaluation of the time-aware predictor-assisted iterative algorithm is conducted based on the IEEE 118-bus system. Effective re-balancing of false negatives and false positives is observed, and significant reduction of algorithm run-time is achieved with the developed time-aware predictors.