Transmission Planner
Updating the long-range transmission expansion plan
What You Do Today
Develop 10-20 year system expansion plans that account for load growth, generator retirements, new resource additions, and policy mandates. Billions of dollars of capital investment decisions depend on these plans.
AI That Applies
ML ranks transmission expansion candidates by benefit-cost ratio across hundreds of load, resource, and policy scenarios, identifying investments that perform well under uncertainty.
Technologies
How It Works
For updating the long-range transmission expansion plan, the system draws on the relevant operational data and applies the appropriate analytical models. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The output is a recommended plan or schedule that accounts for the identified constraints and optimization criteria.
What Changes
Plan development evaluates orders of magnitude more scenarios. Capital allocation becomes robust to uncertainty instead of being driven by a few hand-picked futures.
What Stays
Stakeholder engagement, policy judgment, and the political reality of siting new transmission. The best plan is useless if you cannot build it.
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 updating the long-range transmission expansion plan, 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 updating the long-range transmission expansion plan 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 the current accuracy of our forecasting, and how would we know if an AI model is actually better?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Which historical data do we have that's clean enough to train a prediction model on?”
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.