Useful for generation and revenue planning, but it can smooth away customer clustering and local peak coincidence.
Distribution Grid Stress Analysis
Find localized load risk before it becomes a grid problem.
Forecast where EVs, electrification, customer growth and extreme weather could increase hourly loads—then screen priority areas for transformer exposure, detailed engineering review and load-management alternatives.
Block-group and customer-level forecasting · 8,760 hourly profiles · Planning-level transformer screening
The planning gap
A manageable system forecast can conceal a local problem
EV adoption, heat-pump conversion, new construction, rebuild activity and weather-sensitive loads are not distributed evenly. A utility may have adequate service-area capacity while a particular neighborhood, lateral or transformer experiences much faster load growth.
GIM begins with actual service area identity-protected utility customer household record data and hourly end-use loads. It forecasts emerging load at the household level and aggregates the results upward, preserving the geographic and time detail that broad system averages remove.
Block-group, neighborhood and customer results show where EV adoption and other loads are concentrated.
Screening narrows the field before transformer measurements, circuit modeling and capital planning begin.
Practical distribution-planning workflow
Move from emerging-load forecasts to defensible next steps
GIM does not take the place of a power-flow study. Its value is identifying where detailed engineering attention and mitigation analysis are most likely to pay off.
Forecast
Build customer-level EV, electrification, growth and weather scenarios.
Locate
Aggregate 8,760 hourly loads by block group, ZIP, neighborhood or utility area.
Screen
Test transformer assumptions and flag planning-limit or rated-capacity exposure.
Prioritize
Identify locations, hours and scenarios that warrant utility data and engineering review.
Respond
Compare upgrades with managed charging, DSM, DER, storage or VPP strategies.
What the analysis evaluates
More than an EV forecast
Distribution exposure often reflects several changes occurring together. GIM allows planners to evaluate those interacting drivers on a consistent hourly basis.
EV
EV adoption and charging
Household-level ownership probabilities reveal likely clusters rather than spreading EV growth uniformly.
- Unmanaged charging
- Time-of-use response
- Managed charging
8760
Hourly peak coincidence
Baseline and scenario profiles show when new end-use loads overlap with existing residential peaks.
- Average weekdays
- Seasonal peak days
- Every hour of the year
WX
Weather and electrification
Extreme heat or cold and electric space-conditioning growth can amplify emerging local constraints.
- Heating and cooling extremes
- Heat-pump conversion
- End-use contributions
BG
Block-group variation
Small-area results expose differences in income, commuting, housing, adoption and load growth.
- Maps and heat maps
- Customer-segment filters
- Selected-area dashboards
GR
Customer and housing growth
Forward scenarios incorporate new customers, demolition and rebuilding instead of freezing the current housing stock.
- 2030 and 2035 horizons
- Editable growth assumptions
- New-construction electrification
FLEX
Flexible-load mitigation
Test whether targeted load management could reduce or defer conventional infrastructure needs.
- DSM and demand response
- DER and batteries
- VPP portfolios
New transformer-risk worksheet
Interrogate any block group with utility-selected assumptions
The worksheet combines localized GIM load forecasts with practical engineering inputs so users can test plausible transformer conditions without claiming asset-specific precision.
Inputs the utility controls
Select a block group and revise the assumptions that best represent the local distribution configuration.
Transformer size (kVA)Existing loading (% of nameplate)Households per transformerPlanning limit (% of nameplate)Loading-calculation hoursPower factor
Signals reduced operating margin and a need to monitor, validate assumptions or examine mitigation—before the transformer necessarily exceeds its rating.
Receives a separate, stronger flag as a potential early-replacement candidate requiring transformer-specific engineering review.
Does not mean “no risk.” The result remains scenario-dependent and should be reconsidered as adoption, weather and operating conditions change.
Planning-level screening and prioritization: Results are not a detailed engineering analysis and do not replace measured transformer loading, phase balance, voltage-drop, thermal, protection, power-quality, asset-condition or circuit power-flow studies.
Decision value
Turn a broad grid concern into a ranked work program
The objective is not to declare that an asset will fail. It is to improve where the utility spends its next hour of engineering, field and program-development effort.
01
Prioritize field verification
Direct loading measurements and asset-data checks toward locations with the greatest modeled exposure.
02
Focus circuit studies
Supply scenario-based hourly loads for the feeders, laterals or transformer areas most likely to matter.
03
Sequence capital planning
Distinguish emerging planning-margin concerns from possible nameplate-capacity exceedance and early replacement.
04
Target managed charging
Compare the cost of local EV exposure with the utility-wide economics of telematics, AMI or meter-collar strategies.
05
Screen non-wires alternatives
Test whether DSM, DER, batteries or VPP portfolios reduce the timing or magnitude of localized peaks.
06
Document the rationale
Use consistent assumptions, dashboards and exported data to explain why an area or strategy merits further review.
Works at different data-maturity levels
Useful with or without complete circuit models
For utilities with limited feeder and transformer models, GIM provides a rapid geographic screen. For utilities with mature engineering models, it provides an upstream analytics layer that identifies priority scenarios and supplies localized hourly load inputs.
CYMESynergiWindMilOpenDSSGISCSV / Excel
Frequently asked questions
What this analysis does—and does not do
Why is block-group analysis useful?
Block groups are small enough to reveal meaningful differences in customer and housing characteristics while remaining suitable for geographic screening. Where utility asset mappings are available, results can support more targeted feeder or transformer-area analysis.
Does GIM predict a specific transformer failure?
No. It screens potential exposure under stated assumptions. Asset loading history, condition, ambient temperature, phase balance and other engineering factors determine actual risk.
Why report both planning-limit and rated-capacity results?
The planning limit identifies reduced operating margin before overload, while the nameplate threshold highlights a more urgent potential capacity issue. Treating them separately supports better prioritization.
Can the utility change the assumptions?
Yes. Users can test alternative transformer sizes, current loading, customers per transformer, planning thresholds, hours and power factor for any selected block group.
Is AMI required?
No. MAISY customer data provide the initial modeling foundation. Available AMI, GIS, asset and circuit data can improve calibration and support subsequent validation.
How does this connect to a managed-charging business case?
Grid screening identifies the local problem; the GIM business-case worksheet quantifies program costs, avoided G&T demand charges, added energy margins, payback, benefit-cost ratio and NPV.
See where localized grid exposure may emerge.
Request a guided GIM demonstration or discuss a prepared Grid Impact Analysis Report for your service area.