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AI Edge Computing for Grid

The Problem

“Real-time grid reliability limited by centralized analytics”

Organizations face these key challenges:

1

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

2

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

3

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

The Shift

Before AI~85% Manual

Human Does

  • •Monitor centralized grid alarms and review delayed feeder or substation data
  • •Investigate voltage violations, faults, and equipment stress using engineering thresholds
  • •Coordinate switching, DER settings, and outage response through manual operator workflows
  • •Review historian data and studies to plan maintenance and operating changes

Automation

  • •Apply fixed alarm rules to SCADA and telemetry streams
  • •Flag threshold breaches such as overcurrent or undervoltage events
  • •Aggregate periodic sensor and operational data for centralized review
With AI~75% Automated

Human Does

  • •Approve high-impact control actions and operating changes during abnormal conditions
  • •Review prioritized edge alerts and decide on switching, dispatch, or crew response
  • •Handle exceptions when local recommendations conflict with safety, policy, or field conditions

AI Handles

  • •Continuously analyze local high-frequency grid data for faults, oscillations, and voltage excursions
  • •Prioritize and triage feeder or substation events based on severity and likely impact
  • •Generate asset health scores and early failure warnings for maintenance planning
  • •Execute approved local optimization and event-driven responses when connectivity or latency is constrained

Operating Intelligence

How AI Edge Computing for Grid runs once it is live

AI runs the operating engine in real time.

Humans govern policy and overrides.

Measured outcomes feed the optimization loop.

Confidence88%
ArchetypeOptimize & Orchestrate
Shape6-step circular
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapecircular

Step 1

Sense

Step 2

Optimize

Step 3

Coordinate

Step 4

Govern

Step 5

Execute

Step 6

Measure

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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