Data‐driven spatio‐temporal analysis of wildfire risk to power systems operation

Abstract

Wildfires are natural or man-made disasters that continuously threaten portions of the transmission and distribution grid, and thus the stability of the electric grid. This paper presents a two-stage framework for assessing power system-wildfire risk using a data-driven wildfire prediction model. The first stage of the framework estimates the spatio-temporal probability of potential wildfire ignition and propagation using a deep neural network in combination with the wildfire physical spread model. Analysis reveals similar spatial and temporal patterns between the model-predicted wildfire ignition potential and actual wildfire ignition. Motivated by these observations, the second stage assesses the wildfire risk in the power grid operation in terms of potential loss of load by de-energisation, through combining geospatial information system data of the power grid topology and the stochastic spatio-temporal wildfire model developed in the first stage. The electric power utility applications introduced by the proposed framework are twofold: 1) a spatio-temporal risk model for proactive de-energisation against potential power system failure-induced wildfire, and 2) a spatio-temporal spreading model for optimal grid operations against exogenous wildfire. The proposed model, based on real-world dataset, is demonstrated on the IEEE 24-bus test system mapped to a study area in Northern California, while the results illustrate the proposed model can achieve the best performance in potential wildfire ignition detection (AUC of 0.995) compared to other baselines, as well as demonstrates the risk-aware operation of the power system enabled by the proposed framework.