SCC-PG: Advanced Learning for Energy Risk Tracking (ALERT)
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abstract
The energy supply is the backbone of Smart and Connected Communities (S&CC). It ties together various energy stakeholders (providers, consumers, and services) across different social, economic and cultural layers, and among groups corresponding to the residential, commercial, or industrial settings. Recent statistics indicate that despite all the measures taken by the utility industry to maintain the reliability and security of the energy supply, the number of major blackouts throughout the world is increasing. Loss of electricity affects residents, students, private and public sector employees, small and large businesses, and critical city services, such as, police, firefighters, and first responders. The fundamental question is whether the data sciences and engineering, combined with social sciences and technology can help reduce the losses and related societal impacts. This project postulates that the combination of the Big Data spatiotemporal analytics and physical models will allow us to achieve predictive outage risk capabilities to address the mitigation options not presently available. The Advance Learning for Energy Risk Tracking (ALERT) approach will focus on predicting the outages and asset failure risks using historical data from utilities (outage, smart meter data, etc.), along with additional data from weather-related government and private sources (radar, satellite, ASOS, NLDN and NDFD, Vaisala), as well as topology and vegetation data. Such risk predictions will be shared with participating utility companies, and with their customers to mobilize mitigation measures, which needs a strong social study aspect to better understand customer behavior. Those measures may include: a) equipment repair/replacement and feeder switching actions, b) relocation of the volatile population, and local power back up for schools, businesses and essential city services, c) scheduling firefighter and emergency services for evacuation of at-risk populations, and d) dispatching police forces to prevent looting of vacated houses and businesses. The goal of the communities to minimize the risk of electricity supply failure and undesirable environmental impacts will be achieved by engaging community stakeholders in creating and sharing outage prediction risk maps to allow for individual and collaborative mitigation actions. The main objective is to build research capability to develop a methodology for predicting the risk of electricity outages, which emphasizes the S&CC aspects. To strengthen the community engagement, and to consolidate thinking in the research team and among the various stakeholder groups in metropolitan areas in Texas, different types of meetings will be held.. These multidisciplinary meetings will focus on defining ALERT goals: the need for data collection and behavioral pilots, the requirements for the integration platform, the logistics of the user portal implementations for the dissemination of ALERT messages and handling of false positive and false negative scenarios, as well as collection of panel polling data in future steps. The project will identify the research gaps and means of addressing them, the data requirements, and will develop a comprehensive network of contacts for each partner organization, and each prospective pilot participant. 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.
