Publication Type

Journal Article

Version

publishedVersion

Publication Date

7-2026

Abstract

A power station or transmission line can be affected due to bushfires, increasing operation costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems under bushfires. We develop a model to capture the stochastic nature of bushfire spread based on Moore’s neighborhood model and propose an online optimization modeling framework to sequentially plan power flows in the electricity network. Our framework assumes that bushfire spread is non-stationary over time and that the spread and containment probabilities are unknown. To address these challenges, we develop a contextual online learning algorithm that treats the in-situ geographical information of the bushfire as a “spatial context”. The online learning algorithm learns the unknown probabilities sequentially based on the observed data, and then accordingly makes OPF decisions. The sequential OPF decisions aim to minimize the regret function, which is defined as the cumulative loss against the clairvoyant strategy that knows the true model parameters. We provide a theoretical guarantee of our algorithm by deriving a bound on the regret function, which outperforms the regret bound achieved by other benchmark algorithms. Our model assumptions are verified by the real bushfire data from NSW, Australia, and we apply our model to power systems to illustrate its applicability.

Keywords

online optimization, power systems, power flow management, bushfires, adaptive change point detection

Discipline

Energy Policy | Operations Research, Systems Engineering and Industrial Engineering

Publication

Automatica

Volume

189

First Page

1

Last Page

10

ISSN

0005-1098

Identifier

10.1016/j.automatica.2026.112985

Publisher

Elsevier Ltd

Additional URL

https://doi.org/10.1016/j.automatica.2026.112985

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