Abstract
Recent increases in extreme weather events such as severe thunderstorms, floods, and hurricanes are leading to destruction in power system equipment (transmission and distribution poles and lines, substations, power plants, etc.) and are causing widespread prolonged power outages. These outages often cause inconveniences in critical services (health care, transportation, national security, etc.) and significant losses in the economy, leading to human suffering. Therefore, understanding the spatiotemporal correlation of these events with power systems is crucial to planning and for maintaining reliable operation and control under such events. However, developing such correlation requires several datasets, including weather events and power outage datasets, along with coordination from multiple entities (e.g., electric utilities, government agencies, and research organizations). Also, high-resolution data collection is a time-consuming and tedious task because different interest groups are involved in the process. To this end, we propose an automated data framework that maps severe weather events with power outages to quantify power system resilience. This framework uses the publicly available National Weather Service dataset and Oak Ridge National Laboratory’s Environment for Analysis of Geo-Located Energy Information (EAGLE-I) power outage dataset to quantify the power system resilience. The proposed work can quantify power system resilience against extreme weather events at the county/state level for different weather event types (e.g., hurricanes, severe thunderstorms, and floods). The outcome of the proposed work will be useful for identifying vulnerability hot spots, developing weather event-based planning strategies (planning strategies might change with events types), developing asset management strategies, and developing predictive analysis tools.
