Energy & Utilities: Grid Operations Under Constraints¶
Case study → energy & utilities
Grid decisions need safety gates and replayable traces.
Utilities operate under strict safety and reliability constraints. A decision-support system must enforce what actions are allowed, and provide a trace that survives audits and post-incident reviews.
The question
Can AI support grid operations (outage response, switching plans, capacity constraints) while preventing unsafe actions, and producing decision artifacts that can be reviewed and replayed?
Failure modes to avoid
Unsafe suggestions
Recommending actions that violate safety procedures or operating limits.
Non-local constraints
Switching constraints depend on topology, equipment state, and work orders across systems.
Evidence gaps
Telemetry and tickets disagree; the system must be able to abstain and request missing data.
Unreplayable incidents
Postmortems fail if reasoning exists only as transient chat output.
What changes with governed causal memory
We connect topology, telemetry, work orders, and procedures into a constraint-gated reasoning layer.
The result is a recommended plan with evidence paths — or a deterministic escalation.
flowchart TB;
A["Alarm / outage"] --> E["Expand evidence graph"];
E --> P["Causal path candidates"];
P --> G["Safety + operating constraints"];
G -->|"Pass"| R["Recommended plan + trace"];
G -->|"Fail"| X["Abstain + escalate"];Diagram: typical evidence path (illustrative)
flowchart LR;
T["Telemetry"] --> F["Fault hypothesis"];
F --> C["Constraint check"];
C --> S["Switching plan"];
S --> TR["Trace"];Outputs
Safe-by-design recommendations
Plans that are validated against procedure and operating constraints.
Incident traces
Evidence, rules applied, decisions, and escalations captured as artifacts.
Faster postmortems
Replayable reasoning reduces time-to-resolution and improves learning.
Governed automation boundaries
Clear lines between auto-suggest, auto-execute, and mandatory human review.