01
Define the scenario
Select forecast year, EV growth, electrification, weather and flexible-load options.
Example Grid Impact Model Session
See how localized forecasts become utility planning decisions.
This screenshot walkthrough follows an Excel-based GIM session from scenario inputs and customer filters to 8,760 hourly loads, block-group results and flexible-load strategies.
One connected workflow
From service-area assumptions to local grid exposure
GIM gives utility teams an accessible workbook interface while retaining customer, time and geographic detail needed for distribution planning.
02
Model each customer
Apply customer digital twins and adoption probabilities instead of uniform averages.
03
Locate the impact
Review service-area, ZIP, block-group, neighborhood and customer results.
04
Test responses
Compare managed charging, DSM, DER, storage and VPP strategies.
Step 1 · Session definition
BEGIN tab: choose the analysis
Select a distribution-system small-area analysis or extract individual customer 8,760 hourly loads. The session can incorporate EVs, electrification, weather extremes, DSM, DER and VPP options for the current year, 2030 or 2035.
Open the full-size workbook screenshot ↗Step 2 · Localized adoption
EV SETUP tab: forecast who is likely to own an EV
Specify the service-area increase in EV ownership. GIM estimates household-level EV ownership probabilities using customer characteristics such as income, education, commuting distance and householder age, then allocates growth to the households and neighborhoods most likely to adopt.
Open the full-size workbook screenshot ↗Step 3 · Customer segmentation
Filter the customer population
Focus the analysis by income, educational attainment, dwelling size, construction year, EV ownership or all-electric status. This helps planners evaluate neighborhoods and customer groups that may experience emerging load sooner than service-area averages suggest.
Open the full-size workbook screenshot ↗Step 4 · System context
Review service-area hourly load results
The RESULTS dashboard compares baseline and forecast load shapes for average weekdays and peak days. Tables summarize hourly loads, end-use contributions, annual energy, peak demand and demand-management potential.
Open the full-size workbook screenshot ↗Step 5 · Geographic differences
Compare ZIP-code adoption and load impacts
ZIP-level results connect customer characteristics with EV ownership and peak-load forecasts. Differences in income, education, housing and commuting patterns help explain why adoption and grid impacts vary across the utility service area.
Open the full-size workbook screenshot ↗Step 6 · Small-area screening
Identify block groups with greater emerging-load exposure
Block-group tables and street-map heat maps show where EV ownership, EV peak contribution and hours above selected load thresholds are concentrated. Larger red symbols indicate higher values and help prioritize engineering review.
Open the full-size block-group heat map ↗Step 7 · Customer-level detail
Export customer records and 8,760 hourly loads
Selected customer records combine identity-protected demographics, dwelling characteristics and hourly loads. Planners can examine representative neighborhood groups and export data for transformer, feeder or other engineering analysis.
Step 8 · Mitigation comparison
Compare unmanaged and managed EV charging
Customer-sample charts reveal the evening charging spike that can occur when commuters return home. Scenarios compare unmanaged charging with time-of-use response and managed charging; the latter can stagger charging more effectively and avoid a new synchronized peak.
Open the full-size charging comparison ↗Step 9 · Flexible-load strategies
Evaluate DSM, DER, storage and VPP options
Test individual or combined strategies for EV charging, space heating, air conditioning, water heating, appliances, home batteries and EV batteries. Dispatch rules reflect the timing and operating characteristics of each end use.
Open the full-size DSM and VPP setup screenshot ↗Expanded current applications
The current GIM extends this session workflow
The screenshots document the core workbook process. New worksheets add more direct transformer screening and utility business-case analysis.
Transformer-risk worksheet
Select any block group and enter transformer size, current loading and houses per transformer. Results estimate potential load and flag equipment above planning limits or rated capacity, including potential early-replacement candidates.
Managed charging business case
Compare telematics, AMI and meter-collar strategies using utility-specific rates, program costs and participation assumptions. Outputs include benefit-cost ratio, payback, cash flow, NPV and per-customer metrics.
Grant-compatible results
Detailed cost, benefit and investment results are organized to support utility review and documentation consistent with federal grant-program requirements.
Planning use: GIM screening results identify locations and equipment for follow-up engineering review; they do not replace utility power-flow, protection or asset-condition studies.
Questions about the session
What utility teams usually want to know
Why use an Excel workbook?
The familiar interface makes scenario selection, dashboard review, charting and export accessible to utility staff without requiring a new enterprise software workflow.
How local are the results?
Outputs are available for the service area, ZIP codes, Census block groups, selected neighborhoods and individual identity-protected customer records.
Can GIM support transformer planning?
Yes. Localized hourly forecasts and the transformer-risk worksheet help screen for planning-limit and nameplate-capacity exposure before detailed engineering analysis.
Can it evaluate program economics?
Yes. The managed charging worksheet connects avoided demand charges and added energy margins with program, incentive and technology costs.
See GIM with your utility questions in mind.
Request a guided demonstration or discuss a prepared Grid Impact Analysis Report.