Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context
<|reference_start|>Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context: 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.<|reference_end|>
@article{xu2024online,
    title={Online Planning of Power Flows for Power Systems Against Bushfires Using
  Spatial Context},
    author={Jianyu Xu, Qiuzhuang Sun, Yang Yang, Huadong Mo, Daoyi Dong},
    journal={arXiv preprint arXiv:2404.13391},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13391},
    primaryClass={eess.SY cs.LG cs.SY math.OC}
}