Entries for Digitizing Utilities Prize

A novel tool that allows for the monitoring of key performance indicators across a wide range of models.

SecurEV is a data acquisition technology that utilizes intelligent data replication for secure redundant monitoring of EV charging data.

Failing infrastructure causes unmeasurable damage to humans & the world’s environment. Software can help inspect, improve, & replace it.

Using ML & data analytics to improve weather-sensitive load modeling & electrification forecasting for a more stable BPA power grid.

The TEAM STEEL weather-sensitive load model will enable engineers to make the grid more resilient to extreme weather events.

Automatility is a modular, platform-agnostic systems integrator focused on utility needs.

Leaptran will work closely with CPS Energy to integrate high solar penetration in its utility operations.

We are proposing a set of advanced modeling techniques using a distributed computing platform to analyze large utility datasets.

We combine machine learning and simulations to provide analytics and highly configurable scenario-based forecasts for any region.

Grid edge waveform technology to monitor and predict distributed energy resource adoption, utilization and performance health

Machine Learning reviews voltage and load interval data from smart meters to detect physical connection issues to mitigate wildfires.

This proposal is an innovative solution to automate the PMU anomaly detection based on real PMU measurements.

Advanced load modeling application that utilizes physical- and data-driven methods to perform load forecasting & electrification estimation.

To develop automated & scalable machine learning (ML) methods for robust event diagnostics using large amounts of imperfect PMU data.

Use available data to accurately forecast load and create/improved digital twin models of assets. E.g. dynamic interaction asset models

Leaptran team will use a hybrid load modeling solution to classify weather insensitive and sensitive load percentages.