Fictional training simulation. D2V Mock is an invented training organization. All people, data, incidents, and documents are fictional. Any resemblance to a real entity is coincidental.
MISSION 05 · STORM RESPONSE AND RESILIENCE
COMPLETE GUIDED LAB
D2V Mock
Energy
Design a storm-response prioritization and crew-assignment system that improves restoration decisions without hiding safety, critical-load, geographic, and fairness tradeoffs.
Guided lab available: review the public mission specification here, then enter the paid workspace for staged evidence, decisions, artifacts, progress tracking, and portfolio export.
01 / CORE QUESTION
What should be restored first when every signal is incomplete?
The simulated regional utility operates a mixed urban and rural distribution network. During severe storms, the control center receives smart-meter outages, customer calls, feeder alarms, weather updates, crew reports, and requests from emergency agencies. Existing priority rules favor visible customer counts, but they do not consistently represent electrical dependencies, time-to-consequence, field safety, crew qualification, fatigue, materials, road access, source freshness, or uncertainty about the damaged asset.
The mission is not solved by producing a fluent recommendation or an opaque score. The learner must define the command boundary, reconstruct changing evidence, apply hard constraints before ranking, build a replayable decision record, and establish what would justify movement from replay to shadow advisory use.
DECISION TO IMPROVEWhich incidents require immediate dispatcher attention, which crew options are operationally feasible, and when must the system block, verify, escalate, or enter degraded mode?
CORE METHOD → MISSION
One operating decision. Multiple deployment boundaries.
The Core Labs isolate individual failure modes. This mission forces several of them to coexist under one customer timeline, one evidence room, and one final deployment recommendation.
$59STANDARD INDIVIDUAL PRICE · ADVANCED MISSION · LAUNCH ACCESS IS CURRENTLY FREE
Should storm-response prioritization and crew-assignment recommendations be used in live restoration, and what authority must disappear when data or capability degrades?
SCADA, AMI, outage calls, topology, crew state, critical-facility records, weather, and field reports disagree while time-to-consequence is shrinking.
Define a constrained release, degraded mode, incident response, re-approval rules, and protected outcomes before any live expansion.
CORE METHOD MAP
Review all 19 Core Labs →6 Core Labs are exercised here
02 / STAKEHOLDERS
Success has competing definitions.
The design must represent authority, incentives, operational constraints, and unacceptable outcomes rather than collapsing stakeholder needs into a generic requirements list.
Control-center supervisor
Needs a stable operating picture and defensible priorities as conditions change.
Field operations lead
Owns crew safety, skills, travel, switching authority, equipment, and work sequencing.
Emergency coordination officer
Represents hospitals, shelters, water systems, communications, and public-safety dependencies.
Regulatory and customer lead
Requires auditable treatment of critical loads, rural customers, estimated restoration times, and complaints.
03 / EVIDENCE PLAN
The operating truth must be reconstructed.
The guided evidence room combines structured data, policies, interviews, and operational records. Every material conclusion must remain traceable to source evidence and freshness.
Smart-meter last-gasp events, feeder alarms, breaker states, call clusters, and confidence.
Feeders, switches, protective devices, service areas, access roads, and asset dependencies.
Location, certifications, shifts, fatigue limits, vehicles, materials, and mutual-aid crews.
Wind, flooding, lightning, wildfire, road closures, and forecast uncertainty.
Facility type, backup power, contact, dependency, last verification date, and priority policy.
Field photos, drone observations, voice reports, predicted failure type, and estimated repair time.
Event timelines, assignments, switching actions, safety incidents, and actual restoration.
Medical-needs reports, emergency requests, complaint escalation, and public updates.
EXPECTED DATA DEFECTSDuplicate outage clustersStale criticality recordsBlocked roadsUnknown feeder stateCrew fatigueChanging weather
04 / REQUIRED BUILD
A thin slice that can change a real decision.
The build must connect evidence, logic, human authority, failure handling, and measurement. A model or dashboard alone is not a complete intervention.
Unified incident model
Fuse alarms, calls, topology, field reports, facilities, weather, and confidence into traceable incidents.
Priority policy engine
Represent customer impact, critical dependencies, safety, estimated effort, geography, and policy overrides explicitly.
Constraint-aware crew assignment
Match skills, authority, equipment, travel, shift limits, dependencies, and switching sequence.
Operator map and queue
Expose source evidence, uncertainty, blocked routes, affected facilities, and why priorities differ.
Override and escalation controls
Allow accountable operator changes with reasons, approvals, and incident history.
Resilient operating mode
Define degraded-data behavior, offline exports, refresh cadence, rollback, and manual fallback.
05 / STAGED COMPLICATION
The largest queue leaves a rural medical facility waiting.
The complication is released only after the learner commits the first problem frame, architecture, and evaluation plan.
REVEAL CASE COMPLICATION+
The first prioritization run maximizes customers restored per crew-hour. A rural feeder serving relatively few accounts remains low in the queue, but a dialysis center on that feeder reports that backup generation may fail within four hours. The critical-facility registry is nine months old and lists the facility as having eight hours of backup. Flooding also makes the direct route uncertain.
Required response: Reconcile the conflicting evidence, decide how criticality and freshness alter priority, and revise crew assignment, communications, and policy. The system must not silently treat either the registry or the phone report as certain.
Immediate reprioritization
Protects a potentially critical load but may delay restoration for thousands of customers.
Verify first
Improves evidence quality but consumes a narrow decision window.
Alternate support
Coordinate fuel, evacuation, or mobile generation while grid repair remains sequenced.
Split response
Send assessment capacity first while preserving the main restoration crew assignment.
06 / EVALUATION
Value and harm must be measured together.
The final deployment decision must use predeclared technical, operational, adoption, financial, and risk measures. The strongest metric cannot erase a critical failure.
| Measure | What it tests | Target behavior |
|---|---|---|
| Critical-load exposure | Time critical services remain without adequate primary or backup power. | Minimize severity-weighted exposure. |
| Customer restoration | Customers and load restored over time. | Improve without becoming the only objective. |
| Crew safety and utilization | Travel, fatigue, hazard exposure, productive work, and reassignment. | No optimization may bypass safety constraints. |
| Priority stability | Frequency and cause of queue changes as evidence arrives. | Avoid churn while allowing justified revision. |
| Geographic burden | Restoration duration across rural, urban, and vulnerable service areas. | Identify persistent inequity or data neglect. |
| Estimate calibration | Accuracy and uncertainty of repair and restoration estimates. | Communicate ranges and update reasons. |
Safety and switching authority are hard constraints, not adjustable score weights.
Critical-facility evidence includes freshness, backup assumptions, and dependency uncertainty.
Every priority exposes the factors and source records that materially affected it.
Crew assignment accounts for skills, travel, materials, fatigue, and task dependencies.
The workflow remains usable when connectivity or one data source fails.
The final recommendation addresses both restoration performance and distribution of harm.
HOW TO WORK
Bring your own agent. Keep the evidence chain visible.
The lab permits agent-assisted investigation and implementation. Strong work records material agent recommendations, checks them against authorized evidence, and documents what the learner accepted, changed, rejected, or left unresolved.
Give bounded context.
Provide the mission objective, approved evidence, constraints, and required output rather than asking for a generic solution.
Ask for alternatives.
Require multiple hypotheses, failure modes, and disconfirming evidence before choosing an intervention.
Trace every material claim.
Check source IDs, calculations, code behavior, policy constraints, and unsupported causal language.
Own the recommendation.
Record why the final decision follows from the evidence and what would cause it to change.
07 / REQUIRED DELIVERABLES
The complete record of the deployment decision.
The reviewed lab will score evidence traceability, technical judgment, implementation quality, risk handling, operating readiness, and the consistency of the final recommendation.
Storm decision doctrine
Objectives, non-negotiable constraints, override authority, and escalation policy.
Incident and topology model
Source reconciliation, confidence, dependency, and critical-facility lineage.
Priority and dispatch design
Scoring, constraints, optimization, operator controls, and degraded mode.
Working operational slice
Incident queue, map, crew assignment, explanations, and audit history.
Simulation and evaluation
Historical replay, scenario tests, safety gates, fairness review, and failure analysis.
Executive resilience review
Recommended launch boundary, operating risks, policy changes, and rollout decision.
GUIDED LAB
Enter the D2V Mock Energy guided lab.
The complete guided lab is available with eight gated stages, twelve evidence sources, a storm replay starter kit, ten mission artifacts, and a published 100-point safety and release rubric.