Analysis of animal-related electric outages using species distribution models and community science data

Abstract

1. Animal-related outages (AROs) are a prevalent form of outages in electrical distribution systems. Animal-infrastructure interactions vary across focal species and regions, underlining the need to study the animal-outage relationship in more species and diverse systems.2. Animal activity has been used as an indicator of reliability in the electrical grid system and to describe temporal patterns in AROs. However, these ARO models have been limited by a lack of available estimates of species activity, instead approximating activity based on seasonal and weather patterns in animal-related outage records and characteristics of broad taxonomic groups, e.g., squirrels.3. We highlight publicly available resources to fill the ecological data gap that is limiting joint analyses between ecology and energy sectors. Species distribution models (SDMs), a common technique to model the distribution of a species across geographic space and time, paired with data sourced from eBird, a community science database for bird observations, provided us with species-specific estimates of activity to model spatio-temporal patterns of AROs. These flexible, species-specific estimates can allow future animal-indicators of grid reliability to be investigated in more diverse regions and ecological communities, providing a better understanding of the variation that exists in animal-outage relationship. 4. AROs were best modeled by accounting for multiple outage-prone species 2 activity patterns and their unique relationships with seasonality and habitat availability.5. Different species were important for modeling outages in different landscapes and seasons depending on their distribution and migration behavior.6. We recommend that future models of AROs include species-specific activity data that account for the diverse spectrum of spatio-temporal activity patterns that outage-prone animals exhibit.

Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

“…There have been a few NWO studies that have focused on subsets of these outages, such as animal and vegetation related outages. For example, the prediction and analysis of animal related outages have been explored using a combination of statistical and advance machine learning techniques [18,20,38]. Also, similar predictive techniques for vegetation related outages (as a result of tree growth) have been explored [39].…”