Sensor Data Enabled Utility Asset Capacity Utilization Maximization, Load Modeling, and Event Detection and Location

Abstract: This project aims to develop and demonstrate advanced data analytics for improved operations, monitoring, and control for a power utility system using AMI data, Supervisory Control And Data Acquisition data, and next generation grid-edge computing metering. With increasing penetration of interconnected distributed energy resources on the distribution grids, power system operation is becoming more dynamic than ever. Future EV charging could cause transformer and other “last mile” distribution overloading. Further, the customer load is more dynamic and unpredictable than other electrification transitions due to EV charging and residential level PV generation, which imposes challenges to utility operation and control. This project aims to tackle such challenges using big data methods with the following objectives: 1. We will propose a real time measurement based transformer thermal modeling and verification system for maximizing utilization of transformer capacity and prolonging its life. 2. We will develop a method for load model identification including EV charging characteristics. 4. We will develop a data driven event detection and location method for improved system operation. 5. We will demonstrate the developed technologies in a utility environment.