Prioritizing grid restoration after storms
Utilities restore circuits with incomplete situational awareness of critical loads and crew travel times.
Problem
After extreme weather, outage tickets flood in while hospitals, water pumps, and vulnerable customers need priority.
Current workflow
Dispatchers triage tickets, assign crews, and update estimated restoration times manually.
Consequences
Longer outages for critical facilities and inequitable restoration.
How AI might help
Optimization can propose crew routes maximizing critical load restoration under constraints.
Limits & risks
Needs accurate critical-load registries and road conditions.
Opaque prioritization perceived as unfair; bad data harming hospitals.
Alternatives today
Static priority lists and experienced dispatcher judgment.
Static lists drift; humans cannot optimize large ticket sets under stress.
Evidence
- DOE grid resilience program materials
Emphasizes data-driven restoration and critical infrastructure protection.
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