REWIRED · NRECA / DOE · 2024–2027
A data-informed planning tool for electric cooperatives — projecting EV adoption impacts across your distribution network at the substation, circuit, and transformer level.
About the Grant
REWIRED — Rural Electric Utility Workflow Improvements for Rapid EVSE Deployment — is a cooperative agreement between NRECA Research and the U.S. Department of Energy's EERE, providing over $2 million in federal funding from June 2024 to May 2027.
Led by NRECA Research in partnership with BrillIT and guided by a Cooperative Advisory Board representing all ten NRECA regions, REWIRED is a direct response to the unique challenges cooperatives face in deploying EV charging infrastructure at scale.
Key Objectives
Streamline and standardize EVSE deployment processes, reducing time, cost, and complexity in rural areas.
Develop tools to help cooperatives manage increased EV-driven demand and ensure reliable integration of charging infrastructure.
Enable faster, more informed deployment of EVSE so members have reliable and accessible charging options.
Engage co-op representatives from all ten NRECA regions to contribute real-world data and insights.
Capabilities
Projects EV growth annually through 2050 using EIA AEO scenarios — Reference, Low EV, and High EV cases.
Estimates incremental demand impact at the substation and circuit level with coincident peak adjustments.
Evaluates transformer loading under residential non-coincident peak demand with RUS and IEEE guidance.
Interactive map of transformer load status — current state and projected 2036 — across your service territory.
Tabular summaries ranking substations and circuits by overloaded transformer count for capital planning.
Switch between EIA AEO scenarios without re-uploading data to stress-test against multiple futures.
Workflow
Review what data you'll need, how projections are built, and what outputs the analysis provides.
You are hereUpload a Distribution Transformer file and a Meter Data file listing each residential meter with peak load.
Upload your data →The dashboard runs load modeling and EV adoption projections producing tabular and visual outputs.
View results →Data Privacy
Uploaded datasets drive a one-time analysis then are wiped. Nothing is ever written to disk.
All data paths are secured with TLS from upload through completion of analysis.
We never retain copies of your raw data or share it with third parties.
Data is available only within your active browser session and does not persist between visits.
Two input datasets are required. All data is validated client-side before upload and processed entirely in memory — nothing is written to disk.
Purpose
A simplified picture of your grid — every residential transformer with capacity limits and geographic coordinates.
Required fields
NOTE — Headers must match exactly. Use the template below as a starting point.
Purpose
A simplified view of residential demand — each meter's transformer assignment and peak load value (ideally individual peak demand within the last 12 months).
Required fields
NOTE — Headers must match exactly. PEAK_LOAD should be in kW.
EV Adoption Projections
The charts below display projected substation and circuit-level EV penetration (left) paired with system-wide adoption curves and demand across all EIA scenarios (right). These projections are the result of the REWIRED EV propensity model, which estimates the number of EVs likely to be adopted by consumers in the future. Projections are based on the EIA Annual Energy Outlook report, using a proprietary modeling process to adapt to the specific cooperative's service territory based on the data provided. Further details can be found in the methodology section above.
| Substation / Circuit | Trend | 2026 | 2027 | 2028 | 2029 | 2030 | 2031 |
|---|
Transformer Load Map
Each point represents a distribution transformer. Dot color shifts as projected load increases (see chart legend). Scrub or play the timeline to watch stress patterns emerge across the network.
Transformer Load Analysis
This analysis evaluates distribution transformer loading under residential non-coincident peak demand conditions based on provided data, with adjustments for diversity to approximate coincident peaks in accordance with RUS and IEEE planning guidance [1] [2]. Transformer classifications: Overloaded — estimated coincident demand exceeds rated capacity at 0.95 PF; Car — headroom supports one residential Level 2 charger; Truck — headroom supports a full truck-rated charger.
| Substation | Circuit | Total | Car | Truck | Overloaded |
|---|
REWIRED answers two planning questions: where EVs are likely located now, and how EV counts could change over time under different EIA scenarios. The sections below explain what is estimated, how it is built, and how it should be used.
REWIRED estimates battery electric vehicles (BEVs) associated with residential members at a census-tract level, then projects how those local counts could evolve under selected EIA Annual Energy Outlook scenarios.
The estimate includes BEVs regardless of charging method (Level 1, Level 2, or other). It does not include conventional hybrids, plug-in hybrids, or hydrogen vehicles.
Results describe community-level prevalence, not household-level ownership. They reflect resident-associated EVs rather than temporary through-traffic or visitors.
Use case: planning estimates for grid and infrastructure decisions, not exact counts for every tract or a guaranteed forecast of future adoption.
BrillIT's proprietary model starts with observed adoption, adds nationwide Census descriptors, aligns boundaries with crosswalks, and estimates present-day EV prevalence in every tract.
Future trajectories come from the U.S. Energy Information Administration (EIA) Annual Energy Outlook. REWIRED does not invent a separate national growth curve; it applies EIA scenario multipliers to each tract's local baseline.
[Future EV count] = [Present-day EV count] x [EIA growth multiplier (year, scenario)]
Each census tract starts from its own estimated baseline. The selected EIA scenario then scales that baseline over time, allowing lower- and higher-growth planning ranges.
Scenario selection changes the entire growth path, not a single point estimate
Performance metrics below are from held-out test data (locations not used for model training). They evaluate the present-day prevalence model, not the future accuracy of any EIA scenario.
Like any planning model, REWIRED depends on assumptions about data coverage, transferability, boundary alignment, and local conditions that may diverge from national trends.
Treat outputs as planning estimates rather than exact counts. The model estimates BEV prevalence and does not independently predict charging level mix or exact charging behavior by time and location.
As new registration data, ACS inputs, and EIA outlooks are published, the analysis should be refreshed so planning stays aligned with best-available evidence.
REWIRED scenarios are intended to evaluate a range of plausible futures, not to declare one guaranteed outcome. External forecasts can provide directional reasonableness checks, but they do not validate every local tract estimate.