Power Plant Outage Scheduling

The Problem Today

Generation managers manually optimize outage calendars across 20+ generation units, balancing equipment condition, fuel contracts, demand forecasts, and wholesale power market prices. The planning cycle runs 12–36 months. 15–20% of outages extend beyond plan due to unexpected findings. Poor timing – scheduling during summer peak instead of spring shoulder – can cost $10–50M in replacement power.

How It Works

Every outage scheduling solution is powered by a Decision Value Loop – a continuous cycle of five stages:

1. Sense: Continuous equipment monitoring (vibration, temperature, efficiency degradation), demand forecasts, market prices, weather data, and fuel supply status.
2. Analyze: Multi-objective optimization weighing cost of outage (lost production + replacement power) vs. risk of continued operation vs. market conditions.
3. Decide: Recommend fleet-wide outage calendar with constraint satisfaction. Flag scope changes when equipment data warrants.
4. Act: Generate outage work plans. Coordinate with maintenance contractors and fuel suppliers.
5. Learn: Every outage outcome (duration, findings, cost vs. plan) refines degradation models and planning accuracy.

Why Not Off-the-Shelf AI?

Outage scheduling is a multi-objective optimization across equipment physics (degradation models), economics (replacement power costs, market prices), and grid reliability (NERC requirements). No generic scheduling tool understands the interplay between turbine vibration data and wholesale electricity markets.

The metricsIQ Advantage

OT/IT convergence expertise is scarce in generalist firms. Our team has built industrial data platforms that integrate SCADA with enterprise systems. The utility sector benefits from the same geospatial + time-series capabilities proven in upstream energy – applied to the decisions that cost utilities the most.