Resource Planner
Capacity expansion modeling
What You Do Today
Run capacity expansion models to identify the least-cost portfolio of generation, storage, and demand-side resources that meets reliability, emissions, and policy requirements across planning scenarios.
AI That Applies
AI-powered optimization engines evaluate millions of possible resource combinations across scenarios, incorporating unit commitment, dispatch simulation, and reliability constraints.
Technologies
How It Works
For capacity expansion modeling, the system evaluate millions of possible resource combinations across scenarios. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Planners can explore more scenarios and sensitivities as AI reduces model run times and enables broader optimization.
What Stays
Defining model inputs and assumptions, interpreting results, and making the critical judgment calls about which portfolios to recommend — models inform, humans decide.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for capacity expansion modeling, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long capacity expansion modeling takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What's our current scheduling lead time, and how often do we have to reschedule due to changes?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Which scheduling constraints are genuinely fixed vs. which are we treating as fixed out of habit?”
They understand the workflow dependencies that AI tools need to respect
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.