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

arXiv: https://arxiv.org/abs/2404.13391 · HTML: https://mlsanity.com/paper/2404.13391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 179 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 30 of 33)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Learning to Optimize Via Posterior Sampling](https://mlsanity.com/paper/1301.2609/) (2013)
- [(More) Efficient Reinforcement Learning via Posterior Sampling](https://mlsanity.com/paper/1306.0940/) (2013)
- [Explore no more: Improved high-probability regret bounds for non-stochastic bandits](https://mlsanity.com/paper/1506.03271/) (2015)
- [Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability](https://mlsanity.com/paper/2003.12699/) (2020)
- [Linear Contextual Bandits with Knapsacks](https://mlsanity.com/paper/1507.06738/) (2015)
- [DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow](https://mlsanity.com/paper/1910.14448/) (2019)
- [Optimal Exploration-Exploitation in a Multi-Armed-Bandit Problem with Non-stationary Rewards](https://mlsanity.com/paper/1405.3316/) (2014)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets](https://mlsanity.com/paper/1802.09639/) (2018)
- [Multiple Change-point Detection: a Selective Overview](https://mlsanity.com/paper/1512.04093/) (2015)
- [Detection of false data injection attacks in smart grids based on graph signal processing](https://mlsanity.com/paper/1810.04894/) (2018)
- [Strong SOCP Relaxations for the Optimal Power Flow Problem](https://mlsanity.com/paper/1504.06770/) (2015)
- [Balancing Wildfire Risk and Power Outages through Optimized Power Shut-Offs](https://mlsanity.com/paper/2004.07156/) (2020)
- [Recent Developments in Security-Constrained AC Optimal Power Flow: Overview of Challenge 1 in the ARPA-E Grid Optimization Competition](https://mlsanity.com/paper/2206.07843/) (2022)
- [On the performance analysis of resilient networked control systems under replay attacks](https://mlsanity.com/paper/1307.2790/) (2013)
- [The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey](https://mlsanity.com/paper/1502.00134/) (2015)
- [DC Optimal Power Flow with Joint Chance Constraints](https://mlsanity.com/paper/1911.12439/) (2019)
- [Distributed sampled-data control of nonholonomic multi-robot systems with proximity networks](https://mlsanity.com/paper/1609.02174/) (2016)
- [Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating](https://mlsanity.com/paper/1712.08015/) (2017)
- [Stealthy MTD Against Unsupervised Learning-based Blind FDI Attacks in Power Systems](https://mlsanity.com/paper/2004.07004/) (2020)
- [An Exact Sequential Linear Programming Algorithm for the Optimal Power Flow Problem](https://mlsanity.com/paper/2107.05164/) (2021)
- [Multi-period Power System Risk Minimization under Wildfire Disruptions](https://mlsanity.com/paper/2305.02933/) (2023)
- [A Cycle-Based Formulation and Valid Inequalities for DC Power Transmission Problems with Switching](https://mlsanity.com/paper/1412.6245/) (2014)
- [Unsupervised Disaggregation of PhotoVoltaic Production from Composite Power Flow Measurements of Heterogeneous Prosumers](https://mlsanity.com/paper/1706.04821/) (2017)
- [Communication-Constrained Expansion Planning for Resilient Distribution Systems](https://mlsanity.com/paper/1801.03520/) (2018)
- [Robust Optimization for Electricity Generation](https://mlsanity.com/paper/1803.06984/) (2018)
- [How to Secure Distributed Filters Under Sensor Attacks](https://mlsanity.com/paper/2004.05409/) (2020)
- [Bayesian adversarial multi-node bandit for optimal smart grid protection against cyber attacks](https://mlsanity.com/paper/2104.02774/) (2021)

## Abstract (arXiv)

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.