AI Edge Computing for Grid

The Problem

“Real-time grid reliability limited by centralized analytics”

Organizations face these key challenges:

Latency and connectivity constraints prevent real-time use of high-frequency grid data, causing delayed detection of faults, oscillations, and voltage excursions

Rule-based alarms generate false positives/negatives and do not adapt to changing grid conditions (DER variability, topology changes, seasonal load patterns)

High data volumes from sensors and DER telemetry overwhelm backhaul, storage, and centralized analytics, limiting scalability and increasing costs

Impact When Solved

Milliseconds-to-seconds local inference enables faster fault localization and corrective action, improving reliability indices (5–15% SAIDI reduction)Asset health scoring and anomaly detection reduce catastrophic failures and extend maintenance intervals (10–25% fewer unplanned failures; 5–12% fewer truck rolls)Event-driven edge processing cuts data transmitted to the cloud by 30–70% while increasing DER hosting capacity by 10–20% through improved voltage/VAR control

Real-World Use Cases

Smart Grid Management and Optimization

A smart grid is like upgrading from an old landline to a modern smartphone for your electricity network. Instead of just pushing power one way from big plants to homes, the grid becomes two‑way, with sensors and software that can see what’s happening in real time, shift loads, use home batteries and solar panels, and prevent or shorten outages.

Time-SeriesEmerging Standard

9.0

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0