Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context

TL;DR

This paper presents an online optimization framework that uses spatial context and online learning to plan power flows in power systems during bushfires, aiming to minimize operational costs and adapt to uncertain, non-stationary fire spread.

Contribution

It introduces a novel spatial context-based online learning algorithm for dynamic power flow planning under bushfire uncertainty, with theoretical regret guarantees.

Findings

The model effectively captures bushfire spread dynamics using real data.

The proposed algorithm outperforms benchmark methods in regret minimization.

Application to real power systems demonstrates practical viability.

Abstract

The 2019-20 Australia bushfire incurred numerous economic losses and significantly affected the operations of power systems. A power station or transmission line can be significantly affected due to bushfires, leading to an increase in operational costs. We study a fundamental but challenging problem of planning the optimal power flow (OPF) for power systems subject to bushfires. Considering the stochastic nature of bushfire spread, we develop a model to capture such dynamics based on Moore's neighborhood model. Under a periodic inspection scheme that reveals the in-situ bushfire status, we propose an online optimization modeling framework that sequentially plans the power flows in the electricity network. Our framework assumes that the spread of bushfires is non-stationary over time, and the spread and containment probabilities are unknown. To meet 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 makes the OPF decision accordingly. 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 two power systems to illustrate its applicability.