01
Locate potential growth
Use household characteristics and ownership probabilities to assess how a selected EV adoption scenario may be distributed across block groups and customer segments.
MAISY EV Hourly Charging Loads Databases
Plan with household charging profiles—not just EV totals.
Connect EV ownership potential, travel patterns and charging assumptions to hourly demand. MAISY provides customer-level data for localized load analysis, with optional whole-home profiles to show how charging combines with existing residential loads.
Identity-protected household records · Hourly charging loads · CSV delivery
Why customer detail matters
The same EV count can create very different utility impacts
Annual charging energy does not reveal the hours or locations that drive capacity needs and wholesale demand charges. Household-level profiles preserve differences before loads are aggregated.
02
Understand charging diversity
Travel requirements, arrival timing and charger assumptions affect how much energy each vehicle needs and when that energy is delivered.
03
Evaluate peak coincidence
Compare charging with residential peaks and the wholesale demand-charge window instead of equating charger ratings with diversified demand.
04
Test alternatives consistently
Use a common customer-load foundation to compare unmanaged charging with selected timing and control assumptions.
Database contents
A practical foundation for EV load analysis
Define the geography, household coverage, forecast assumptions and required fields for the intended application. Deliverables are specified for each engagement.
| Data component | What it describes | Planning use |
|---|---|---|
| Household and driver characteristics | Income, household composition, dwelling and travel-related characteristics, as specified for the selected database. | Segment customers and investigate differences in adoption potential and charging needs. |
| EV ownership indicators | Current-owner or potential-owner designation and estimated household ownership probability. | Allocate adoption scenarios and compare customer groups without identifying named future buyers. |
| Hourly charging loads | Modeled charging demand for current or potential EV owners under the agreed travel and charging assumptions. | Analyze annual energy, charging diversity, peak contribution and local aggregation. |
| Geographic detail | Block group and ZIP-defined coverage and agreed geographic identifiers. GIM applications can provide block-group results. | Compare areas and prioritize locations for further review. |
| Optional whole-home loads | Baseline end-use and whole-home hourly profiles, with and without EV charging. | Evaluate combined residential demand rather than EV load in isolation. |
| CSV deliverables | Household-level fields and charging-load data in an agreed CSV structure. | Import into spreadsheets, analytical software and utility modeling workflows. |
Scope matters: This page describes the EV data offering. Current Grid Impact Model applications cover single-family residential customers. Geographic coverage, vehicle definitions, record counts, available fields and time resolution are confirmed in the project scope; broader household databases should not be assumed to have the same coverage as GIM.
From household records to hourly loads
Preserve the differences that system averages conceal
MAISY combines identity-protected customer characteristics, travel information and selected charging assumptions. The broader MAISY database resource includes more than 7 million utility customer records.
Define the area
Select the block-group or ZIP-defined coverage and household population relevant to the analysis.
Estimate adoption potential
Proprietary analysis estimates EV ownership probability for each household.
Specify charging
Travel distance, arrival patterns, efficiency and charger assumptions establish energy and timing.
Develop hourly demand
Build charging profiles and optionally combine them with baseline whole-home loads.
Aggregate and compare
Analyze individual profiles, customer segments and geographic totals under consistent assumptions.
Modeled load profiles are based on metered end-use 15-minute load data.
Historical charging-load example
A ZIP average can hide a neighborhood hot spot
The original Rhode Island example compares individual charging profiles, aggregate whole-home impacts and a selected household segment. It illustrates why the maximum output of one charger differs from average coincident demand across all households.
More localized customer groups can have substantially different EV adoption and demand patterns. Examining those groups helps identify where detailed distribution analysis may be warranted.
Example, not a current forecast: The graphic reflects an earlier application and its scenario assumptions. Its values should not be generalized to another utility or interpreted as current Rhode Island projections.
Open the original charging-load graphic full sizeCurrent Grid Impact Model applications
Connect the data to planning and investment decisions
GIM adds an Excel interface, scenario controls, dashboards and analytical worksheets to the customer-level load foundation.
Localized EV growth and loads
Forecast single-family residential adoption and 8,760 hourly charging loads at customer, block-group, ZIP and service-area levels.
Transformer-risk screening
Apply utility-selected transformer capacity, existing loading, power factor and planning limits to screen localized demand. Distinguish planning-limit exposure from rated-capacity exposure.
Managed-charging business case
Evaluate coincident demand-charge savings, retail energy margins, enrollment, engagement, technology costs and incentives. Compare payback, NPV, benefit-cost ratio and cash flow.
Flexible-load and NWA studies
Compare charging strategies with residential demand response and storage to identify portfolios worth further engineering and economic evaluation.
Choose the deliverable
Data, an interactive model or a prepared analysis
Choose the form that fits your staff, analytical workflow and planning question.
EV charging-load data
License agreed customer fields and hourly profiles for use in your own analytical tools. Specify the geography, assumptions and optional whole-home loads.
Utility-specific GIM
Use a prepared Excel interface and forecasting engine to compare adoption, charging, local-grid and business-case scenarios in-house.
Jackson Associates report
Have JA conduct the agreed analysis and deliver results and supporting outputs for utility planning, proposals or program decisions.
GIM can support planning without utility AMI interval data or customer contact. Utility-specific tariff, asset-loading and program-cost assumptions improve the relevance of financial and transformer-screening results.
Frequently asked questions
EV charging-load data and GIM
Are these measured charging loads?
The hourly profiles are modeled using individual household commuting departure and arrival times and charging assumptions.
Can I obtain charging loads without the full GIM?
Yes. A data engagement can specify household fields, charging profiles, geography and optional baseline loads for your own analytical workflow.
Does the data include both EVs and PHEVs?
The standard databases include only EVs because the vast majority of PHEV owners use Level 1 wall outlets that draw only about 1.4 kW while most EVs charge with level 2 chargers which, on average, draw between 7.2 kW and 9.6 kW of power. About 80 percent of new electric vehicles are EVs versus PHEVs. As an option, the databases can include both EVs and PHEVS.
Does this replace utility AMI data?
It provides a planning foundation where interval data are unavailable. Available utility measurements can still be valuable for calibration and validation.
Can the profiles support engineering models?
Hourly profiles can provide load inputs for engineering workflows. Mapping representative customers to actual assets and preparing the required import structure remain part of the agreed modeling work.
What is needed for a proposal?
Identify the geography, single-family customer population for a GIM application, planning question, forecast scenarios, desired data fields and required outputs. JA can then define scope and cost.
Build your EV analysis on customer-level hourly loads.
Discuss a focused database, a utility-specific GIM or a prepared analysis report.