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Forecasting EV, Electrification, Climate Extremes, DSM, DER, and VPP Distribution Load Impacts for Utilities, ZIPs, Neighborhoods and Individual Customers

 

 

Index of Topics

Grid Impact Model (GIM) Summary

With intuitive options for scenario testing, flexible inputs, and real-time visualization, the Grid Impact Model makes complex hourly load modeling both accessible and actionable. The GIM
  • Forecasts the size and location of new load growth,
  • Evaluates the potential for reducing or shifting these loads with utility programs, and
  • Assesses the potential for a utility virtual power plant (VPP) to manage and provide load support.
More Specifically, the Grid Impact Model:
  • Is an AI machine learning agent-based digital twin model that incorporates data from your utility customers along with data from dozens of supporting databases.
  • Applies an Excel user interface with easy-to-use option selections and dashboard, chart and tabular output.
  • Simulates real-world localized grid behavior to identify potential impacts of EV ownership, electrification, customer growth, and extreme weather.
  • Provides analysis of DSM programs and technologies that can achieve load reduction and shifting as well as virtual power plant (VPP) strategies to support grid reliability.
  • Provides hourly load forecasts for utility service areas, ZIP code areas, neighborhoods and individual customers.
  • Forecasts current, 2030 or 2035 EV ownership and hourly loads, electrification, population growth, and extreme weather impacts along with load reduction and load shifting impacts of utility DSM & DER programs.
  • Requires no contact with your utility customers and no utility staff time for implementation.

Grid Impact Model (GIM) Session

The Excel-based Grid Impact Model GIM is designed for in-house applications. Intuitive Excel forms provide easy-to-use option selections, application of the compiled forecasting engine, with output dashboards, charts and table presentations.

An example GIM model session illustrates each of these features and provides additional model documentation.

Download the Grid Impact Model Demo

The Excel-based Grid Impact Model Demo runs safely and securely on your computer - details and instructions are provided on the Demo download page.

Grid Impact Model (GIM) Distribution Analysis, Forecasting and Planning Support

The GIM model bridges the disconnect between high-level awareness of emerging distribution threats and detailed planning required to protect transformers, feeders, and substations. Model results turn broad trends into quantifiable, location-specific load effects along with analysis of the potential for utility programs to mitigate those load impacts and potentially support grid reliability with VPP programs.

Some examples of GIM model distribution system analysis and planning support are provided below:

Utility System Planning

  • Identify areas where EV growth will stress feeders, transformers, and regulators
  • Develop timelines for upgrades before overloads occur
  • Model weather-driven peak impacts and resilience needs
  • Support general distribution planning with ZIP-level and sub-ZIP forecasts
  • Guide siting of microgrids, storage, non-wires alternatives
  • Provide data for budget planning, board reporting, and long-term load strategy

Program Evaluations & Investment Decisions

  • Quantify benefits of managed charging and load shifting
  • Support business cases for ADMS, DERMS, or time-series DMS tools
  • Prioritize SCADA, AMI analytics, and voltage management upgrades
  • Evaluate ROI of load control for HVAC, water heat, and EV charging
  • Provide defensible inputs for grid-modernization and resilience grants
  • Justify hosting-capacity tools, digital twins, and GIS model buildout
  • Help secure funding for software, sensors, and automation deployments

Existing Distribution Planning Tools

  • Import EV growth as load multipliers or time-series profiles in DSA Software
  • Map ZIP and sub-ZIP forecasts to feeders, transformers, or GIS service areas
  • Run unmanaged vs. managed charging scenarios to test peak and voltage impacts
  • Prioritize capital upgrades using overload and hotspot forecasts
  • Apply weather-based load curves in time-series simulations
  • Layer water heater, HVAC, and AC load-control savings to show deferred upgrades
  • Use outputs in hosting-capacity screening and interconnection studies
  • Support screening even without full circuit models by linking loads to ZIP-level transformer counts

Features That Make the Grid Impact Model (GIM) Utility Applications Unique

  • Actual utility customer data drawn from 7+ million households MAISY digital twin database
  • AI modeling process
  • Easy-to-use Excel interface
  • Traditional residential load forecasts
  • Hourly load EV, electrification, extreme weather, demand management, VPP impacts
  • Forecast and analysis results support utility business, investment and distribution system management
  • Jackson Associates models and data development vetted by over 150 organizations

Grid Impact Model Implementation

Utility implementation is entirely turn-key. No customer contact or utility staff resources are required.

Our model implementation activities include:
  • Assembling the digital twins sample of your utility customers from our 7+ million residential customer digital twin database
  • Assembling supporting databases reflecting weather characteristics, commuting data, and other information associated with your customers and applied in the AI forecast engine
  • Calibrating the EV ownership probability model for your service area
  • Estimating ZIP-level customer growth models to support 2030 and 2035 forecasts
  • Incorporating your residential tariff data
  • Delivering the Grid Impact Model along with user training sessions

Background: How a General Reliability Concern can Easily Become a Local Distribution Threat

EV, Electrification and Climate Extremes Reflect Electric Grid Threats Electric utilities are facing new unprecedented challenges providing service in geographic areas with rapidly increasing EV ownership and increasing electrification.

A single level 2 charger on a transformer that is close to maximum capacity can result in voltage sags with flickering lights, reduced transformer lifetimes and even transformer failures. EVs represent about 10% of new car sales; the NREL forecasts that 1 in 6 US households will own an EV by 2030.

In addition, new construction is trending towards more electric appliances contributing disproportionately to loads in critical utility peak periods (think water heaters, ovens).

And finally, extreme weather can boost AC and space heating load contributions at system peak time, an impact that becomes more important with new load additions from EV charging and increased electrification.

This distribution capacity problem is a new wrinkle on grid management. Grid designs have been based on “typical” household kW load served. Load diversity among individual households averages out load spikes so design load maximums were reasonably easy to calculate and incorporate in original grid design.

Grid capacity based on this traditional planning (i.e., most established residential areas) may no longer be sufficient with increasing EV ownership and electrification and more extreme weather events.

The following example illustrates how a general reliability issue can easily become a local distribution threat.

Example: A single 25 kVA transformer serves 10 houses in RI with relatively low AC loads.

Before: Average 2 kVA design peak load across 10 customers = 20 kVA so a 25 kVA transformer has a 25 % safety margin.

After: One typical level-2 EV charger added: Add 7.7 kVA in the peak hour results in 27.7 kVA assuming the design average, 11% above the transformer rating which is enough to cause low-voltage light flickering when the EV charger is activated. Two EVs chargers activated within the same hour on this transformer boosts peak kVA to 35.4 kVA, 42 % above the transformer rating causing a significant low voltage situation and shortened transformer life or even failure.

Plus: Adding an extreme AC weather event makes transformer overloading even worse.

Depending on the extent of new EV ownership and increased electrification plus potential weather impacts, many existing substations, feeders, and transformers will require upgrading or active demand management to avoid low voltage problems and/or premature equipment failures.

See our recent paper: Next-Gen Models: Forecasting EV Loads with Digital Twins

Hourly-15min loads Click Here to see advantages of MAISY/SGRC data/analysis compared to Department of Energy, NREL and other engineering model-based sources.