Set the forecast
Select the utility geography, forecast year, EV-adoption scenario, charging technologies and program assumptions.
Localized residential EV forecasting
See where EV adoption and charging loads will grow
The MAISY EV model forecasts household EV adoption and 8,760 charging loads at the local level—so utilities can identify emerging hot spots, test managed charging and focus planning resources where they matter most.
7 million+identity-protected customer records
26,000+household EV modeling records
8,760charging-load hours per year
No AMI requiredor customer contact
Customer-based forecasting
Why local EV forecasts differ
Current EV ownership alone is not a reliable guide to which areas will grow fastest. Future adoption also depends on the mix of household income, age, commuting, vehicle ownership, housing and other characteristics within each area.
The model estimates an EV-ownership probability for each household in a statistically representative local customer database. Those household probabilities allocate future EV ownership across the service area under user-selected adoption assumptions.
Each forecast EV is then connected to a household load profile and charging scenario, producing localized hourly loads rather than vehicle counts alone.
Bottom-up adoptionForecasts begin with individual household characteristics and probabilities.
Local aggregationResults roll up to block group, ZIP code and utility service-area totals.
Time-sensitive loadsCharging is added to 8,760 household loads to reveal coincidence and peak impacts.
Modeling process
From household characteristics to grid-planning results
Forecast household adoption
Match local household characteristics with the EV-ownership database to estimate adoption probability for each customer record.
Locate future EVs
Allocate forecast EV ownership across block groups, ZIP codes and the full utility service area.
Build hourly charging loads
Add unmanaged or managed charging to statistically matched, calibrated household load profiles.
Identify local risk
Screen areas and representative transformers against user-specified planning limits and rated capacity.
Compare strategies
Test participation, incentives and technology choices, then quantify peak reduction and utility economics.
Utility planning outputs
More than an EV count forecast
The EV model is part of the Grid Impact Model, connecting localized adoption with charging loads, planning-level transformer screening and the financial case for managed charging.
EVs by block group and ZIP
Managed and unmanaged 8,760 loads
Coincident-peak contribution
Transformer planning flags
G&T demand-charge impacts
Program costs, benefits and NPV
2030 and 2035 scenarios
Engineering and GIS exports
Find emerging hot spots
Prioritize monitoring and detailed engineering analysis in local areas with the greatest potential constraints.
Compare program designs
Evaluate AMI, telematics and meter-collar approaches with alternative participation, timing and incentives.
Connect loads to economics
Quantify demand savings, incremental EV revenue, program costs, break-even and net present value.
Export usable load data
Prepare forecasts for CYME, Synergi, WindMil, OpenDSS and GIS-based planning workflows.
Illustrative application
Rhode Island ZIP-level forecast
An earlier MAISY analysis allocated a statewide 2030 EV scenario across Rhode Island ZIP codes using household-level ownership probabilities. It showed why a uniform growth assumption can conceal significant local differences.
Some ZIP areas were projected to grow rapidly while others changed more modestly, reflecting different local combinations of household income, age, education, commuting and vehicle ownership.
These figures are retained as results of the original scenario, not as a current Rhode Island market forecast. A current application would use updated customer, market and scenario assumptions.
12.24%average household EV saturation in the original 2030 scenario
28%maximum ZIP-level saturation in that scenario
8 ZIPsabove 25% household EV saturation
25%of ZIPs below 7% saturation
Planning progression
Turn adoption uncertainty into an investment plan
ForecastWhere will EVs locate?
QuantifyWhen will charging affect peaks?
ScreenWhich local assets warrant attention?
DecideUpgrade, monitor or manage load?
Related resources
Continue from EV forecasting to utility action
Questions and answers
Residential EV forecasting FAQ
What geographic detail does the EV model provide?
Results can be developed for utility service areas, ZIP codes and census block groups, with planning-level transformer-risk screening based on local household concentrations and user-specified transformer assumptions.
Does the model require AMI interval data?
No. The model uses customer data already developed by Jackson Associates and does not require customer contact or transfer of utility AMI interval data.
What customers does the Grid Impact Model cover?
The Grid Impact Model applies to single-family residential utility customers. It does not forecast commercial fleets, school buses or multifamily customers.
Is the model a replacement for distribution power-flow analysis?
No. It is a planning and risk-screening tool that identifies where more detailed engineering analysis may be warranted and supplies localized hourly-load forecasts for those workflows.
See the model with your planning questions in mind
Review localized EV forecasts, charging scenarios, transformer screening and managed-charging economics in a focused demonstration.