EAGER: Real-Time: Visual Analytics for Enhanced Decision-Making and Situational Awareness in Modern Distribution Systems, with a Focus on Outage Prediction and Management

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This project addresses the need to develop a fully visible, controllable, and resilient electric power distribution system. Although the distribution system accounts for over 75% of the outages in the power grid, power system operators currently have limited situational awareness at the distribution level, limited visibility beyond the substations, limited information on the network and its connectivity. With the rapid increase in amount and type of data accumulated in the distribution system, i.e. from Advanced Metering Infrastructure (AMI) and remotely monitored and controlled devices, the need to glean actionable intelligence from it is of paramount importance. Integrating data from these heterogeneous datasets (Geographical Information System (GIS), Supervisory Control and Data Acquisition (SCADA), AMI, Outage and Distribution Management Systems (OMS/DMS)) is a first step towards achieving this. The goal of this project is to develop a visual analytics platform to leverage the integrated dataset, enabling distribution system operators to visualize and analyze the state of the distribution system over time, empowering them to identify categorical patterns of events in space and time via highly coordinated visualizations. The project comprises three main components: 1. A data-driven approach to uncover useful information from streaming and historical data, strengthened by 2. Situationally-aware modeling and simulation of the electric power distribution system, and 3. A visual analytics system, leading to prescriptive analytics, actionable knowledge for the indispensable human in-the-loop. The developed infrastructure would enable and enhance real-time fast and confident decision-making, thus supporting overall efficiency and reliability improvements in the electric power distribution system, reducing current outage times and improving reliability indices. Benefits would accrue in terms of savings as well as in terms of customer satisfaction. The principal investigator of this project is the founder and faculty advisor of the local student chapter of IEEE Women in Engineering (WIE) and she plans to leverage that connection to broaden participation in this project. For complete situational awareness leading to decision-making, there needs to be powerful, automated analytics, enhanced by a deep understanding of the physical system, but it is also essential that the human be placed in the loop at the right place and time. For this reason, three components are integrated: 1. Data-driven real-time probabilistic outage prediction, coupled with 2. Situationally-aware modeling and simulation of the distribution system, and with 3. An interactive visual analytics system to provide visual and exploratory analytics. Utilizing real-time data streams coming from the distribution system (SCADA, AMI, Digital Fault Recorders) and from weather stations, together with historical data, advanced real-time data analytics algorithms are applied to strategically process the data, obtaining, for example, accurate loading conditions and developing probabilistic outage predictions. This sensed and processed information is then utilized, as needed, by the now situationally-aware distribution system electrical model to perform simulations. The simulations in turn provide input feedback to probabilistic scenarios and define system constraints for the analytics modules. Since human-in-the-loop is important, these analytics are closely coupled to the representations and modes of interaction in the interactive visual analytics system so that the right information is presented to the user at the right time. Thus, the developed framework results in a hybrid approach that leverages data-driven methodologies, the physical system, and visual analytics, to provide much improved decision-making capabilities. This project investigates the science of real-time learning and decision-making, while also looking closely at the technology for data-driven distribution system analysis coupled with deep understanding of the physical system. The project will provide theoretical underpinnings and novel methods to significantly move distribution modernization efforts forward, including improvements in system reliability and resiliency, new modes for management, new paradigms and paths for customer-utility cooperation, and a new approach to handling big data in the electric grid.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.