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EV Hourly Charging Loads Databases

 

 


Hourly Charging Loads Databases for Individual US EV/PHEV Owners and Potential Owners

Federal government EV purchase incentives along with a growing charging infrastructure and future lower price points are expected to significantly increase EV ownership and charging loads (we are including both EV and PHEV (plug-in hybrid electric vehicles) in the term EV hereafter). These market changes create new challenges for electric utility distribution systems, public charging station installation strategies, and impacts on microgrids.

MAISY EV Hourly Charging Loads Databases are the first resource to provide a comprehensive small-geographic data (ZIP-level) on both current and future potential EV charging loads. Databases encompass the entire US with forecasts of future potential EV ownership and charging loads.

The EV Ownerhsip and Hourly Charging Loads Databases are developed using AI applications to integrate MAISY Database household work commuting travel and charging loads from travel survey data from 300,000 households (including details on 1 million trips), and state/ZIP auto/truck registrations detail for individual PHEV and EV vehicles.

This process is illustrated in the schematic below: EV ownership

Each record includes an AI KNN estimated household EV ownership probability. EV ownership and charging hourly loads can also be provided as optional data items in the MAISY Residential Energy Use and Hourly Loads Databases which include household income, demographics, building, appliance, CO2e emissions, energy use and prices and other data items for each of the 7+ million individual US household records.

MAISY EV Hourly Load Charging Databases Schematic

Summary: EV Hourly Charging Loads Databases

  • EV hourly loads required to charge current PHEV AND EVs.
  • EV ownership probability for drivers including current EV owners.
  • EV hourly loads required to charge current future potential PHEV and EVs in each household.
  • ~5 million single family households and ~3 million multifamily households.
  • AI-generated charging loads based on data from individual household commuting EV charging requirements, 300,000 household travel surveys and state/ZIP registration detail for individual PHEV and EV vehicles.
  • Optionally, dwelling unit appliance loads can be included to provide total whole-house loads including charging loads.
Hourly charging loads for potential EV owners support planning analysis for increases in total charging loads with geographic areas down to ZIP code level.

Three potential ownership categories are 1- unlikely, 2- moderately likely, and 3-highly likely.

Providing hourly loads for current owners and for three potential ownership categories allows users to quantify the likely impact of increasing ownership over time. Vehicle owner demographic and income characteristics provide users with the ability to do what-if scenarios with their own EV adoption assumptions. Ownership and potential ownership designations are based on statistical analysis of data on over 6,600 PHEV and EV owners across the US.

Example EV Hourly Charging Loads

The chart below on the left shows individual charging loads for a sample of household EV/Phev owners. The chart on the right reflects cummulative loads from all the EV/PHEV owners included in the left chart.
MAISY EV Hourly Load Charging Databases Sample Loads MAISY EV Hourly Load Charging Databases Sample Loads

MAISY EV Hourly Charging Loads Database Record Data Items

For each household: Identification of an EV owner or potential owner along with:
  • Household and driver characteristics (income, household members, driver age, traffic congestion measures, etc.).
  • EV Ownership status (current EV owner or potential likelihood of ownership).
  • Current hourly charging loads for current EV owners or hourly charging loads for potential EV owners.
  • (Optional) whole building hourly loads with and without EV charging loads).
Data are provided in CSV formats with data items for each household/driver on a single database row.

Two recent papers illustrate insights provided with the MAISY EV Databases

  • New AI Model Now Forecasts Future EV Hourly Load Impacts for any ZIP Area or Neighborhood in the US, January 23, 2025

     EV Charging kW Loads Most electric utilities are struggling to understand how the rapidly growing number of EVs (electric vehicles) will impact their distribution systems. Adding just a few level 2 EV chargers in some neighborhoods can cause low voltage, flickering lights, reduced transformer lifetimes and blown transformers. Our recent paper presented an AI agent-based EV ownership model and 2030 EV ownership forecasts for individual Rhode Island ZIP codes.

    The AI agent-based model has been extended to forecast hourly EV charging kW loads. The model provides whole-building 8760 hourly loads and EV charging loads for any ZIP-defined geographic area in the US including utility service and metro areas, states as well as neighborhoods and census tracts. A two-page description of the AI EV charging load profile model and example 2030 analysis results are available at here .

  • Agent-Based Model Uses AI to Map Future Utility EV Distribution Challenges; Identifies ZIPs with Greatest Future EV Increases

    Rhode Island EV Ownerships Most electric utilities are struggling to understand how the rapidly growing number of EVs (electric vehicles) will impact their distribution systems. Adding just a few level 2 EV chargers in some neighborhoods can cause low voltage, flickering lights, reduced transformer lifetimes and blown transformers.

    Estimates from the National Renewable Energy Laboratory suggest that the 2023 EV household saturation (EVs as a percent of households) will increase from 2.6 percent to 18.3 percent by 2030 – a sevenfold increase.

    The huge cost of required distribution upgrades requires utilities to prioritize areas that are most likely to see the greatest increases in EVs. Unfortunately, many utilities, even those with AMI systems, have no customer visibility beyond the substation and do not currently have a reliable way to estimate EV growth for different distribution areas.

    A new AI agent-based model provides EV growth forecasts at the ZIP and census tract level to support utility evaluation/planning/upgrading. The MAISY AI agent-based model can be applied to any ZIP defined area (utility service area, state) for any year.

    A four-page paper describes this new resource