A Case Study on Power Outage Impacts from Future Hurricane Sandy Scenarios

Abstract

Hurricane Sandy (2012, referred to as Current Sandy) was among the most devastating storms to impact Connecticut’s overhead electric distribution network, resulting in over 15 000 outage locations that affected more than 500 000 customers. In this paper, the severity of tree-caused outages in Connecticut is estimated under future-climate Hurricane Sandy simulations, each exhibiting strengthened winds and heavier rain accumulation over the study area from large-scale thermodynamic changes in the atmosphere and track changes in the year ~2100 (referred to as Future Sandy). Three machine-learning models used five weather simulations and the ensemble mean of Current and Future Sandy, along with land-use and overhead utility infrastructure data, to predict the severity and spatial distribution of outages across the Eversource Energy service territory in Connecticut. To assess the influence of increased precipitation from Future Sandy, two approaches were compared: an outage model fit with a full set of variables accounting for both wind and precipitation, and a reduced set with only wind. Future Sandy displayed an outage increase of 42%–64% when using the ensemble of WRF simulations fit with three different outage prediction models. This study is a proof of concept for the assessment of increased outage risk resulting from potential changes in tropical cyclone intensity associated with late-century thermodynamic changes driven by the IPCC AR4 A2 emissions scenario.

Estimated Time to Restoration of Hurricane Sandy in a Future Climate

“…Computer models and simulations provide key insights as storms are forecasted. Power outage estimation models exist for tropical cyclones and hurricanes [11][12][13][14], thunderstorms [15], and various weather and vegetation inputs [16][17][18]. Han et al [12] recognized that accurate storm estimates are necessary to have the proper crews in place to make restoration as efficient as possible.…”

“…The ABM presented in [25] can be utilized, as shown in this study, to aid emergency managers in Resiliency Zones 1-3. As the storm is predicted in Zone 1, the weather forecasts can be utilized to predict the expected outages [11,15,17,18,26], which can then allow emergency managers to begin understanding the necessary resources. As the storm is better understood in Zones 2 and 3, the ABM can be run with the updated outage predictions to see how the predicted restoration time would change with available resources.…”

“…Wanik et al [11] studied how climate change could impact the power outages associated with Hurricane Sandy in a future climate using the IPCC AR4 A2 emissions scenario to track outage changes in the year 2112 (100 years after Hurricane Sandy made landfall). The study used six different Weather Research and Forecasting (WRF) model simulations: Control, Goddard scheme, Morris scheme, WRF double-moment 6-class microphysics scheme, "no TC flux", and the ensemble for weather inputs (hereafter referred to as CNTRL, Goddard, Morris, WDM6, NOTCFLX, ENS).…”

“…Additionally, three machine-learning algorithms were used to predict the outages: Random forest, gradient boosted trees, and Bayesian additive regression trees. Although this is only one of many outage prediction models [11][12][13][14][15][16][17][18], the results from Wanik et al [11] will be used as the inputs for the study presented here.…”

Environmental Hazards: A Coverage Response Approach

“…The resulting products are intended to serve as guidelines for an effective response. For example, the anticipation of a major hazard that may result in the closure of roadways, cancellation of events, curtailment of aircraft and transit operations, and loss of power informs the planned response [64]. The particular responses and their effectiveness (e.g., real or suggested, such as in validation studies [65]) can then be monitored and reported via news outlets (online or otherwise) before and during impacts.…”