Quantitative comparison of cascading failure models for risk-based decision making in power systems

The accurate allocation and prediction of risk in power systems is vital for reliable operations of the electrical infrastructure. Several models with varying degrees of accuracy and computational cost are available. Relying on less computationally intensive methods increases the efficiency of risk assessment provided that the output does not impair control actions and decision making. This study focuses on comparing two established cascading failure models for determining their consistency to risk-based decision making. Effects of ambient conditions are captured via temperature-dependent dynamic transmission line ratings. The investigations on the IEEE 24-Bus reliability test system highlight that, when the power grid is subjected to elevated temperature and demand levels, the deviations between Manchester and OPA model can be significant. However, both models show the same general trends, namely, that the demand not served generally increases with increasing temperature and demand. Further similarities are found in terms of the most critical lines and the most heavily loaded generators, providing useful information for power system expansion planning. The OPA model displays a much larger area of elevated risk across the input space that also includes almost the entire area found by the Manchester model, providing conservative estimates in highly stressed power systems.