Utility Planner
Planning for grid modernization and DER integration
What You Do Today
Plan the integration of distributed energy resources — rooftop solar, battery storage, EVs — into a grid designed for one-way power flow. This is the fundamental challenge of modern utility planning.
AI That Applies
AI models DER adoption patterns, assesses hosting capacity at the feeder level, and identifies grid upgrades needed to accommodate distributed resources.
Technologies
How It Works
The system reads the current state — resource availability, demand patterns, and constraints — to inform its scheduling logic. Predictive models fit to historical outcome data identify which variables are the strongest leading indicators, then apply those weights to current inputs to generate forward-looking scores. The output is a recommended plan or schedule that accounts for the identified constraints and optimization criteria.
What Changes
Hosting capacity analysis is automated and continuously updated. You see where the grid can handle more DERs and where it can't, feeder by feeder.
What Stays
Planning the grid modernization strategy — which feeders to upgrade, what technologies to deploy, how to sequence investments — is strategic engineering.
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 planning for grid modernization and der integration, 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 planning for grid modernization and der integration 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.