Transmission Planner
Processing generator interconnection requests
What You Do Today
Review interconnection applications, perform system impact studies, determine required network upgrades, and estimate costs. The queue is years deep and every developer wants their study done first.
AI That Applies
ML pre-screens queue positions, estimates upgrade costs based on similar historical requests, and automates the initial impact assessment to focus engineering time on complex cases.
Technologies
How It Works
The system ingests similar historical requests as its primary data source. 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 results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Initial screening and cost estimation are automated for standard cases. Engineers focus on the complex interconnections that actually require creative solutions.
What Stays
Final engineering review and customer negotiations. Each interconnection is unique, and developers need a human who understands their project.
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 processing generator interconnection requests, 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 processing generator interconnection requests 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
“Which steps in this process are fully rule-based with no judgment required?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“What's the error rate on the manual version, and what would "good enough" look like from an automated version?”
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.