ASCENT: Sensor-enabled Wildfire Awareness and Risk Management (WARM) for Electric Power Infrastructure

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The electric grid is susceptible to increasing wildfire risks, particularly in forested and rural areas. This is primarily due to a lack of risk awareness in such remote locations, which makes prevention and response management difficult. This NSF project addresses this problem by designing and testing a Wildfire Awareness and Risk Management (WARM) system that will use Internet of Things (IoT) wireless sensors to monitor the environment around remote electric power infrastructures and support resilient grid operation during high wildfire risk periods. The project will bring transformative changes to the way power utilities, regulatory agencies, and municipal managers react to wildfire threats by raising their real-time situational awareness and developing methods and strategies that increase their options for successful prevention and rapid response. These changes will be achieved by making advances in remote sensing, wireless communication, power system security analysis, and optimization. The intellectual merit of this project lies in understanding, anticipating, measuring, and mitigating wildfire risks associated with the electric grid. The sensor suites and risk management approaches developed through this project can have broader impacts in disaster mitigation, environmental monitoring, and public safety.Despite the critical societal impacts of wildfires and power outages, there are fundamental gaps in sensing and decision-making methods for wildfire risk management related to the electric grid. We address this knowledge gap by: (1) proposing a self-sustainable, low-maintenance sensor suite that will accurately and quickly detect wildfire risks occurring in its vicinity, and (2) creating data-informed wildfire risk management frameworks for preemptive and responsive decision-making that are consistent with evolving regulatory constraints and policy objectives. The result is a comprehensive WARM system that combines currently available data on power systems and ambient conditions (such as weather) with data from the sensor suites to provide fundamental insights and innovative approaches for improved spatio-temporal management of wildfire risks. The expected benefits of this project include significant improvements in the operational reliability of the power system during periods of elevated wildfire risk and more informed decisions about power shutoffs, leading to fewer, shorter, and more targeted power outages.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.