Background

The national electric grid requires constant monitoring to allow utility companies to maintain the infrastructure properly and operate efficiently. The exact amount of electricity being consumed, known as the grid load, and the phase topology of the grid are both key data points.

Currently, the phase topology data is collected manually in most cases. This is both highly labor intensive and prone to human error, thus causing substantial costs and inefficiencies respectively. Gathering load data is also problematic, as the data is collected automatically; however, it is commonly down-sampled to a low resolution in an effort to lower data transmission and storage costs. This low-resolution data is less useful for energy storage purposes, and can cause circuit overloads and weaken voltage stability.

As more electricity consumers are using Smart Meters, there exists an opportunity to improve on the methods of grid monitoring data collection. Therefore, a software infrastructure package that could utilize end consumer Smart Meters for enhanced grid monitoring would be highly desirable.

Technology Overview

NC State researchers have developed a software infrastructure package that utilizes end consumer Smart Meters for automatic and enhanced grid monitoring. These software packages use Smart Meter data to automatically build out full grid phase topology in real time. This eliminates the need for manual examination, saving tens of thousands of man-hours per year and increasing accuracy.

The package further includes a method of generating high-resolution load data from the low-resolution data profile that is substantially more accurate than any other currently available method. This will allow grid operators to more effectively plan their energy storage utilization, increasingly efficiency.

This software package has been fully coded and validated in simulations. NCSU is now ready for a licensing partner to implement this turnkey solution in their grid monitoring applications.

Further Details

A Novel Data Segmentation Method for Data-driven Phase Identification: https://ieeexplore.ieee.org/abstract/document/9916891

ProfileSR-GAN: A GAN Based Super-Resolution Method for Generating High-Resolution Load Profiles: https://ieeexplore.ieee.org/abstract/document/9731506?casa_token=S3L2zi72qusAAAAA:ABhWJx7hpEuZtuV93zl-BwSEDsBQXUUJJhyZw88amNWrzYzUFD-LSyhH1_79ivzGKwzzbge_mQ

Benefits

Increased grid data collection.
Improved grid monitoring capabilities.
Reduced labor costs for grid maintenance.
Improved grid efficiency.

Applications

Grid monitoring.
Grid maintenance.
Battery Energy Storage Systems (BESS) efficiency improvements.

Utilizing End Consumer Smart-Meters for Enhanced Grid Monitoring and Maintenance