Optimization of computational budget for power system risk assessment

TL;DR

This paper introduces a novel risk assessment method for power transmission networks that combines machine learning with physical simulations to efficiently predict dangerous grid states, enhancing security analysis.

Contribution

It proposes a new hybrid approach using neural networks and physical simulators for probabilistic risk assessment in power grids, improving tractability.

Findings

Neural networks effectively estimate grid risk levels.

The method outperforms traditional simulation-only approaches.

Validated on a standard 118-bus test case.

Abstract

We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators. New conceptual frameworks are calling for a probabilistic risk-based security criterion. However, these approaches suffer from high requirements in terms of tractability. Here, we propose a new method to assess the risk. This method uses both machine learning techniques (artificial neural networks) and more standard simulators based on physical laws. More specifically we train neural networks to estimate the overall dangerousness of a grid state. A classical benchmark problem (manpower 118 buses test case) is used to show the strengths of the proposed method.