01
Where will EV adoption cluster?
Forecast household-level EV adoption and charging loads by block group, ZIP code, neighborhood, feeder or service area.
Grid Impact Model for Electric Utilities
Know where EV growth will affect your grid—and whether managing it pays.
Forecast localized EV adoption and hourly loads, screen transformer and feeder exposure, and build the financial case for managed charging—all in one practical planning platform.
7+ millionidentity-protected customer records
8,760hourly load forecasts
150+utility, government and business clients
Localized EV Grid RiskPLANNING VIEW
Forecast EVs1,248
Priority areas7
Peak shift31%
One connected planning process
Move from general EV concern to actionable utility decisions
GIM connects the questions that system forecasts, engineering studies and program assessments often address separately.
02
Where could grid risk emerge?
Screen localized hourly loads against user-defined transformer, feeder and planning assumptions to prioritize engineering review.
03
When does managed charging pay?
Compare technologies, participation, incentives and program costs with G&T demand savings and additional EV revenue.
Decision-ready outputs
See the load, location, risk and economics
Interactive Excel results support planning discussions, scenario testing, board presentations and detailed engineering follow-up.
Hourly load analysis
Managed vs. unmanaged EV peaks
Planning-level screening
Transformer and feeder exposure
Current load
66%
2030 EV
88%
2035 EV
112%
Utility business case
Find the managed-charging breakeven point
Test G&T demand rates, enrolled EVs, incentives, customer acquisition and ongoing program costs. Compare telematics, AMI and meter-based approaches.
Breakeven EV enrollment
Risk management and stress testing
Test whether today's managed-charging strategy will still work as EV ownership grows
GIM extends utility EV planning from a single forecast or business case to a transparent risk-management process. Utilities and their consultants can establish a baseline, change important assumptions, compare adverse scenarios and identify the breakpoints at which a recommended strategy should change.
Stress tests can examine higher EV charging loads, lower participation, increased program or technology costs, extreme weather and localized EV clustering—then show how each change affects financial results and grid exposure.
The analysis can be updated as actual conditions change, turning managed charging into an ongoing process of identify, stress test, monitor and adapt.
Stress-test the business caseCompare baseline and adverse financial scenarios.
Find decision breakpointsIdentify participation, load and cost thresholds.
Evaluate localized grid riskReveal where EV clustering may create the greatest exposure.
Monitor and adaptReassess program strategy as EV ownership and conditions change.
Built for practical utility planning
Useful whether or not you have detailed circuit models
GIM provides a rapid, consistent way to identify priority locations and scenarios before committing scarce engineering resources.
For utilities with CYME, Synergi, WindMil, OpenDSS or GIS models, GIM exports localized hourly profiles for detailed analysis.
No AMI data or customer contact required
Utility assumptions remain visible and adjustable
Current-year, 2030 and 2035 scenario analysis
EV, electrification, extreme weather, DSM, DER and VPP impacts
Experience behind the model
Utility analytics grounded in real customer data
MAISY databases and forecasting models are developed by Jackson Associates, an energy-data and analysis firm with more than four decades of utility-modeling experience.
40+ yearsof utility forecasting, hourly-load and investment analysis
7+ millionactual identity-protected residential and commercial records
150+ clientsincluding electric utilities, agencies and energy companies
Proven methodsincluding patented drill-down analytics licensed by major BI companies
Explore the detail
Planning resources and model information
See how GIM applies to your utility
Request a guided online demonstration using a representative utility planning case.