Fast and Reliable $N-k$ Contingency Screening with Input-Convex Neural Networks

TL;DR

This paper introduces a novel neural network-based method for fast, reliable $N-k$ contingency screening in power systems, ensuring zero false negatives and significantly speeding up the process compared to traditional heuristics.

Contribution

The paper presents a new approach using input-convex neural networks with guaranteed reliability for contingency screening, improving speed and accuracy over existing methods.

Findings

Achieves 10-20x speedup in contingency screening

Guarantees zero false negatives in classification

Demonstrates effectiveness on IEEE 39-bus test network

Abstract

Power system operators must ensure that dispatch decisions remain feasible in case of grid outages or contingencies to prevent cascading failures and ensure reliable operation. However, checking the feasibility of all $N - k$ contingencies -- every possible simultaneous failure of $k$ grid components -- is computationally intractable for even small $k$, requiring system operators to resort to heuristic screening methods. Because of the increase in uncertainty and changes in system behaviors, heuristic lists might not include all relevant contingencies, generating false negatives in which unsafe scenarios are misclassified as safe. In this work, we propose to use input-convex neural networks (ICNNs) for contingency screening. We show that ICNN reliability can be determined by solving a convex optimization problem, and by scaling model weights using this problem as a differentiable optimization layer during training, we can learn an ICNN classifier that is both data-driven and has provably guaranteed reliability. Namely, our method can ensure a zero false negative rate. We empirically validate this methodology in a case study on the IEEE 39-bus test network, observing that it yields substantial (10-20x) speedups while having excellent classification accuracy.