Dynamic modeling of the effects of vegetation management on weather-related power outages.

• A machine learning outage prediction model (OPM) capable of predicting power outages at a circuit (the operational units of the power distribution network) resolution is developed to understand the impact of enhanced vegetation management standards on outages in the electrical distribution grid during storm events. • The authors find good matching between the increase in enhanced vegetation management (ETT) and reduction in trouble spots over the years from 2005 to 2019. • Most of the vegetation management on the electric grid was completed over the last five years of the study (2015–2019), and during those five years the OPM estimates annual reductions of trouble spots between 25.7 and 42.5 percent due to the tree trimming performed. • A novel feature of the OPM developed in this study is the ability to dynamically update it with different vegetation management scenarios at the circuit level and predict the effect of each scenario on trouble spots in the electric grid for storm events. • The authors also find increased prediction accuracy (a reduction in mean absolute percentage error of 4.1% and 5.2% for the two models trained) when including vegetation management data as an input variable to predict trouble spots in the electric grid. This paper develops a machine learning outage prediction model (OPM) to serve as a simulation framework capable of quantifying the reduction in damages to the distribution electric grid due to vegetation management for storm events. The model covers the Eversource Energy distribution grid territory in Connecticut and uses a random forest model with input variables for vegetation, vegetation management, land cover, drought, elevation, weather and electrical infrastructure to predict outages for each circuit (the operational units of the power distribution network). The model is trained on 165 storms from the years 2005 to 2019. The results show that over the last five years of the study (2015–2019) the annual reduction in trouble spots in the electric grid due to enhanced tree trimming is between 25.7 and 42.5% and there is good matching between increased trouble spot reduction and increased vegetation management. Further, we demonstrate improved outage predictions when including vegetation management data as an input variable, with a 4.1% reduction in mean absolute percentage error in leave-one-storm-out cross-validation. This framework could be used to examine varying vegetation management scenarios and the results should be useful for decision makers such as utilities, municipalities and regulators in optimizing vegetation management and broader grid resilience enhancement plans. [ABSTRACT FROM AUTHOR]