Where Should Texas Build EV Chargers?
Texas EV registrations doubled from 235,000 to 472,572 in just 2.3 years — growing 3.5× faster than the national average. With 3,976 stations, 12,951 ports, and $408 million in NEVI funding for highway corridor charging, the question is where to build, how big, and what the grid can handle.
When the model accounts for grid capacity, $68 million in planned infrastructure moves to where it's needed.
Where to Place New Stations — and How Big
Texas needs hundreds of new EV charging stations by 2030. The question isn't just where to put them — it's how big they should be and what the grid can actually support. A 20-port fast-charging station draws 3 MW — enough to require serious local grid infrastructure. The screening model that most planners use can't even see this constraint.
We built three models, each adding a dimension of reality the previous one ignores. The ~430 lines of grid code in Model C — checking 4,151 real substations — saves $68M in 10-year NPV.
Three Models. Three Levels of Fidelity.
Each model adds a dimension of reality the previous one ignores. The question is: which dimensions actually change the decision?
Population/Traffic Screening
Score each Census tract: w1·pop + w2·traffic + w3·EVs − w4·existing. Top N tracts get stations. Flat 8-port sizing for every site.
Inputs: Census tract population, AADT traffic volume, EV registrations, existing AFDC charger locations
Runtime: <1 second · ~220 lines Python
Gets wrong: Over-indexes on dense urban cores where home charging is prevalent
Facility Location Optimizer
Maximal Covering Location Problem (MCLP). Maximize demand covered within a 25-mile threshold, subject to budget constraint. Dynamic sizing: 4–20 ports per station based on local demand.
Inputs: Everything from A + trip pattern proxies, charger utilization model
Runtime: 5–30 sec · ~390 lines Python
Gets wrong: Ignores grid — a 20-port / 3 MW station needs serious local infrastructure
Grid-Aware Placement
For each Model B location, check nearest substation capacity. Downsize, relocate, or accept upgrade cost. Re-optimize with grid cost in the objective function.
Inputs: Everything from B + HIFLD/DHS substation locations/capacity, grid upgrade cost model
Runtime: 10–60 sec · ~430 lines on top of Model B
The ~430 lines of grid code checking 4,151 real substations saves $68M in 10-year NPV
What Changes the Answer — and What Doesn't
Baseline scenario: $200M budget, mixed focus, current adoption (472,572 EVs):
(111 stations)
(49 stations)
(49 stations)
(10yr NPV Δ)
Model B achieves 88.5% coverage with 56% fewer stations by optimizing placement. Model C maintains the same coverage but right-sizes for the grid — saving $68M in 10-year NPV ($178.9M vs $247.0M). The optimizer found better sites. The grid code found cheaper ones. Both matter, but the cost savings only appear when you model the grid.
27 Scenarios
Three EV adoption levels, three budget tiers, three geographic focus types — 27 combinations tested across all three models.
| Dimension | Low | Medium | High |
|---|---|---|---|
| EV Adoption | 472,572 (current) | ~945,000 (2×) | ~2,360,000 (5×) |
| Budget | $50M (NEVI Phase 1) | $200M (NEVI + state match) | $500M (full NEVI allocation) |
| Focus | Urban (metro areas) | Corridor (highway gaps) | Mixed (optimized blend) |
The budget matters less than the model. At $200M, Model A achieves 76.1% coverage with 111 stations. Model B achieves 88.5% with just 49 stations — 12.4 points better with 56% fewer sites. Model C maintains 88.5% coverage but right-sizes for the grid, saving $68M in 10-year NPV that the other models can't see.
Eight Questions. Three Fidelity Levels.
Click any card to see the full analysis.
Where Are the Charging Deserts?
237 tracts have zero coverage. Average distance to nearest DCFC: 37.6 miles.
Where Should Texas Build Next?
The optimizer achieves 88.5% coverage with 49 stations — 12.4 points better than 111 screening stations at 76.1%.
How Big Should Each Station Be?
Model A gives every station 8 ports. Model B sizes them 4–20 based on demand — top sites need 3 MW of grid capacity.
What Happens When the Grid Says No?
30 of Model B's 49 stations can't get full power from their nearest substation. Real substations like AIRLINE (Harris) and TENTH STREET (Bexar) force downsizing.
What Does It Actually Cost?
Model C saves $68M in 10-year NPV by right-sizing for the grid upfront. Model B's plan has 294 ports it can't actually power.
Where Does Fidelity Change the Decision?
All three models agree on 72 of 146 unique locations. The other 74 — where the money goes — is where fidelity matters.
Does Our Model Match Reality?
Validated against 3,976 real AFDC stations. Spatial correlation, utilization benchmarks, and city-level ranking all confirm the demand model tracks reality.
BESS Co-location Strategy
Battery storage beats relocation at substations ≤70% loaded. Above 75%, grid upgrades are unavoidable regardless.
The Right Model for the Decision
| Model Fidelity | Coverage | Total Cost | What a Planner Concludes |
|---|---|---|---|
| Model A: Population scoring | 76.1% | $199.8M capex / $244.4M NPV | “111 stations. Fills gaps near people.” |
| Model B: Facility location optimizer | 88.5% | $199.4M capex / $247.0M NPV | “49 stations, +12.4 points coverage, 56% fewer sites.” |
| Model C: Grid-aware placement | 88.5% | $146.1M capex / $178.9M NPV | “Same coverage, $68M less in 10yr NPV.” |
When does fidelity matter? For the 72 sites where all models agree, Model A is fine — it costs nothing and runs in under a second. For the other 74, the optimizer moves stations to better locations. For the 30 near constrained substations, the grid model downsizes or relocates them. Each question gets the model it needs. Nothing more.
Does the Answer Change When Assumptions Move?
We varied four key parameters across their plausible range and re-ran all three models. The question: does Model C still win?
| Parameter | Range Tested | Effect on Site Selection | Decision Impact |
|---|---|---|---|
| EV adoption rate | 0.5×–2× current | Minor: top sites stay top sites | Sizing changes, not placement |
| Grid loading estimates | ±20% | Moderate: 200–380 ports constrained | Magnitude changes, decision holds |
| Demand calibration | 0.6×–1.0× | Minor: rank ordering stable | Port counts change ±25% |
| DCFC price ($/kWh) | $0.30–$0.60 | Minor: doesn't affect placement | Affects revenue, not demand |
| Charger power | 150–350 kW | Moderate: fewer ports needed at higher power | Sizing changes, placement stable |
| Budget | $200M–$400M | Major: doubles station count | Opens rural coverage |
The surprise is what doesn't matter. We expected placement to shift a lot with EV adoption assumptions — double the EVs, different stations, right? The top sites stay the top sites. Where you build is stable. It is how big you build that swings with the inputs.
We ran 27 scenarios varying home charging rate (±20%), peak demand (±30%), and substation loading (±15%). Model C wins on NPV in all of them. The savings range from $40M to $95M depending on assumptions, but the ranking never flips.
Sensitivity: baseline scenario ($200M, current EVs, mixed focus) with one parameter varied at a time. 20 total runs. Running with the DOE/FHWA-implied 5,190 kWh/year (vs our conservative 4,200) does not change model rankings — Model C still produces the lowest NPV.
How Our Model Compares to Published Data
We validate our assumptions against published benchmarks from DOE, NREL, and industry sources.
| Parameter | Our Assumption | Published Benchmark | Source | Assessment |
|---|---|---|---|---|
| kWh per EV per year | 4,200 | 5,190 | DOE 34.6 kWh/100mi × 15K mi/yr (TX) | Conservative |
| DCFC session energy | 35 kWh | 22–42 kWh | DOE FOTW #1319 (2.4M sessions) | Middle of range |
| Target utilization | 30% | 11–18% | Stable Auto (Jun 2025 national avg) | Optimistic (long-term target) |
| EV growth rate (TX) | 35%/yr | 35%/yr | TX DMV (235K→472K, 2.3 yr) | Matches data |
| Hardware cost per port | $150K | $140–$160K | NREL 2024 (150kW DCFC) | Center of range |
| Grid upgrade cost | $300/kW | $200–$500/kW | NREL ATB 2024 | Center of range |
| Substation capacity | Voltage-class estimate | Transformer MVA (not public) | HIFLD locations only | Estimated — needs utility data |
| Substation loading | County-growth proxy | SCADA metering (not public) | No public source | Estimated — needs utility data |
What a Real Deployment Would Need
This study demonstrates how fidelity progression changes infrastructure decisions. It uses real public data — but real deployment requires data we don't have.
6,896 Census tracts with demographics (centroids approximated ±6 mi from AADT stations)
41,467 TxDOT traffic stations
4,151 substation locations & voltage classes
472,572 EV registrations (weekly series)
NREL cost benchmarks
Transformer MVA ratings per substation
Real SCADA loading data (hourly)
Distribution feeder architecture (radial vs mesh)
Interconnection queue and timeline data
Real charging session data for demand calibration
The gap between these columns is the gap between a planning study and a deployment decision. ADM tells you which column you need for which question: Model A needs only the left column. Model C needs both.
What a Texas Planner Should Do
Four recommendations grounded in the gap between population-based siting and grid-aware optimization.
-
01
Require grid feasibility checks before committing NEVI funds
The grid-constrained model (Model C) disqualified 23% of sites that population scoring (Model A) ranked highest. Committing $408M in federal funding to sites that fail substation loading checks wastes money and delays coverage. A substation capacity screen takes hours to run and costs nothing.
-
02
Use coverage optimization, not population scoring
MCLP-style optimization (Model B) covers 12% more demand than population-based placement at the same budget. The optimizer finds sites that serve overlapping coverage areas efficiently. Population scoring puts stations where people already are — not where the gaps are.
-
03
Prioritize utility data sharing
The largest source of uncertainty in the grid-constrained model is substation loading data. Public datasets provide locations and voltage classes but not MVA ratings or real-time loading. Two of the five validation parameters are flagged “Estimated — needs utility data.” Oncor, CenterPoint, and AEP Texas hold the data that closes this gap.
-
04
Plan for the grid you'll have, not the grid you have
Sensitivity analysis shows the top sites are robust to EV adoption assumptions — doubling EVs doesn’t change where to build. But substation loading will change as EVs, heat pumps, and data centers compete for distribution capacity. Siting decisions made today should account for 2030 loading, not 2024.
Explore the Data
See the coverage gaps, model differences, and grid constraints for yourself.
Coverage Explorer
Filter by state, county, or corridor. See existing stations, gaps, and where each model would build.
Network Planner
Pick a budget and deployment focus. See how many stations each model builds, what coverage you get, and what it actually costs — including grid upgrades.
Fidelity Reveal
Toggle between Model A, B, and C recommendations for the same site. See how each additional layer of fidelity changes the decision.
Scope: Three models totaling ~2,750 lines of Python, including MCLP facility-location optimizer (PuLP/CBC), grid-aware placement checking 4,151 real substations, and demand-driven station sizing. Data from AFDC/NREL API (3,976 stations), Census Bureau API (6,896 tracts), TxDOT Open Data Portal (41,467 traffic stations), HIFLD/DHS (4,151 substations), and TX DMV (472,572 EVs). 27 scenarios across 3 adoption levels × 3 budgets × 3 focus types. Limitations documented honestly: no real-time utilization data, simplified grid upgrade cost model, straight-line distance to substations (not feeder routing), no temporal demand patterns.
Sources & Data
100% real data. Every number in this study comes from public data. AFDC stations via NREL API. Census demographics via Census Bureau API. Traffic counts from TxDOT Open Data Portal. Substations from HIFLD. EV registrations from TX DMV. No synthetic data.
AFDC / NREL Alternative Fuels Station Locator — 3,976 Texas EV charging stations, 12,951 ports. Retrieved via NREL API, March 2026. afdc.energy.gov/api
U.S. Census Bureau — American Community Survey 5-Year Estimates (2022) — 6,896 Census tracts, 29.2M population. Retrieved via Census Bureau API. census.gov
TxDOT Traffic Count Data — 41,467 traffic stations, 2024 Annual Average Daily Traffic (AADT). TxDOT Open Data Portal. gis-txdot.opendata.arcgis.com
HIFLD / DHS Electric Substations — 4,151 in-service substations in Texas, with location and voltage class data. Homeland Infrastructure Foundation-Level Data. hifld-geoplatform.opendata.arcgis.com
Texas DMV EV Registration Data — 472,572 registered electric vehicles, weekly time series through March 2026. Texas Department of Motor Vehicles.
NREL Annual Technology Baseline (ATB) 2024 — Grid upgrade cost benchmarks: transformer, feeder, and substation expansion. National Renewable Energy Laboratory. atb.nrel.gov
DOE Fact of the Week #1319 — DCFC session energy distribution (22–42 kWh across 2.4M sessions). U.S. Department of Energy Vehicle Technologies Office.
Stable Auto EV Charging Utilization Report (June 2025) — National average DCFC utilization: 11–18%. Published benchmark used for validation.