EAGER: Leveraging Smart Meter Data for Enhanced Situational Awareness of Power Distribution Systems

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Electric power distribution systems are becoming active due to integration of consumer owned rooftop solar generation, electric vehicles, energy storage, and advanced metering infrastructure (AMI). The goal of the proposed research is to investigate fundamental approaches to effectively integrate AMI data to increase situational awareness of distribution systems for more effective operation. Further, the research will focus on integrating machine learning models to learn from historical data for increasing modelling accuracy and uncertainty quantification. The research will result in fundamental advancement in data-based operation of power distribution systems by demonstrating the value of AMI and other smart meter data for efficient operation of the distribution system. In addition to accelerating the adoption of data-driven grid management strategies, the proposed work can serve as a practical guide for utilities to leverage AMI data for situational awareness. The utilities will be able operate the system more efficiently which in turn will lead to economic benefits for the utilities and the consumer. The project will support and train a doctoral student on data-based approaches for operation of power distribution systems. The results will be disseminated through conference and journal publications. The research will investigate innovative approaches for optimal polling of smart meter to obtain a holistic picture of the low-voltage distribution system, reconciling multi-time scale heterogenous meter data to empower novel sparsity-based state estimation strategies, and systematic integration of historical data and real time measurements via a machine learning framework that includes uncertainty quantification. The proposed data-driven approach that leverages real time measurements from a limited subset of smart meters represents a significant paradigm shift in current operational practices. It will also lead to new pathways for future research on utilizing the AMI data to enhance reliability, resiliency, and efficiency of power distribution systems.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.