Quantifying Tipping Risks in Power Grids and beyond

TL;DR

This paper introduces a Bayesian Langevin approach to quantify deterministic and stochastic dynamics in power grid frequency data, aiding early detection of critical transitions like blackouts.

Contribution

It presents a novel Bayesian Langevin method and open-source tool for analyzing both deterministic and stochastic factors in critical transitions, demonstrated on power grid data.

Findings

The model detects a grid state change two minutes before the blackout.

Analysis suggests early triggers like faults or load increases.

The approach helps distinguish destabilizing factors for better anticipation.

Abstract

Critical transitions, ubiquitous in nature and technology, necessitate anticipation to avert adverse outcomes. While many studies focus on bifurcation-induced tipping, where a control parameter change leads to destabilization, alternative scenarios are conceivable, e.g. noise-induced tipping by an increasing noise level in a multi-stable system. Although the generating mechanisms can be different, the observed time series can exhibit similar characteristics. Therefore, we propose a Bayesian Langevin approach, implemented in an open-source tool, which is capable of quantifying both deterministic and intrinsic stochastic dynamics simultaneously. After a detailed proof of concept, we analyse two bus voltage frequency time series of the historic North America Western Interconnection blackout on 10th August 1996. Our results unveil the intricate interplay of changing resilience and noise influence. A comparison with the blackout's timeline supports our frequency dynamics' Langevin model, with the BL-estimation indicating a permanent grid state change already two minutes before the officially defined triggering event. A tree-related high impedance fault or sudden load increases may serve as earlier triggers during this event, as suggested by our findings. This study underscores the importance of distinguishing destabilizing factors for a reliable anticipation of critical transitions, offering a tool for better understanding such events across various disciplines.