Towards Statistical Methods for Minimizing Effects of Failure Cascades

TL;DR

This paper develops statistical methods combining Monte Carlo and convex dynamic programming to evaluate and minimize the impact of transmission failures in power grids, using various loss functions and adaptive strategies.

Contribution

It introduces a novel learning scheme for AC and DC grid models that effectively assesses and reduces failure cascade effects through adaptive, statistically grounded approaches.

Findings

Effective evaluation of failure criticality using multiple loss functions.

Successful application of the method on IEEE-30 bus system.

Demonstrated potential for minimizing cascade effects in power systems.

Abstract

This paper concerns the potential of corrective actions, such as generation and load dispatch on minimizing the effects of transmission line failures in electric power systems. Three loss functions (grid-centric, consumer-centric, and influence localization) are used to statistically evaluate the criticality of initial contingent failures. A learning scheme for both AC and DC grid models combine a Monte Carlo approach with a convex dynamic programming formulation and introduces an adaptive selection process, illustrated on the IEEE-30 bus system.