Author Description

With the continuing deployment of distributed energy resources (DER), behind-the-meter resources, advanced sensors, and grid-edge intelligence, monitoring and operation of the distribution power grid are increasingly becoming complex. Effectively leveraging the massive amount of data available with digital automation to achieve situational awareness (SA) for the right operational decisions is critical to enhancing system resiliency, reliability, and sustainability. Improving SA can be achieved through sensor data acquisition, real-time data analytics, physics-aware machine learning for the estimation of unmeasured data, efficient data management, and information extraction. Existing state-of-the-art grid monitoring approaches typically utilize data stovepiped into repositories of operational data, planning data, and third-party or enterprise data. Data from distribution phasor measurement units (D-PMUs) & supervisory control, and data acquisition (SCADA) are examples of operational data. Data from advanced metering infrastructure (AMI) meters is an example of enterprise data. In addition, historical data and asset data are examples of planning data. Furthermore, local news and forecasted weather are examples of third-party data. This segmentation limits realizing the unabridged value of available data. In addition, this set of data varies in terms of volume, velocity, variety, veracity, and value. The objective of this work is to coordinate, estimate and process the segmented data, and enhance the situational awareness of the distribution power grid with high penetration of DER. Specific goals of this work include: a) PV forecasting, nowcasting, and estimation, and b) utilizing estimated data and aggregation for efficient system monitoring and resilient operation. Results demonstrate the superiority of the developed PV estimation approach, and matching behavior of the reduced aggregated system with a fully modeled distribution network.