National price context+31.6% price · +32.5% CPI
- Metric
- Change in residential average revenue per kWh versus all-items CPI-U
- Scope
- United States; nominal annual averages, not causal attribution
- Period
- 2014–2024
- Status
- derived
Are data centers driving up electricity bills?
There is no single national formula linking data-center load to a household bill. Fuel, generation, transmission, timing, and the approved tariff determine who pays.
Key facts, with scope and limits.
Exploratory state comparisonr = −0.12 · R² = 0.015
- Metric
- Pearson correlation between EPRI's modeled 2024 medium data-center power share and exact EIA 2014–2024 residential price growth
- Scope
- 50 U.S. states; cross-sectional, not causal
- Period
- 2014–2024 prices; analysis dated 2026
- Status
- analysis
Virginia high-growth scenario+$37 / month
- Metric
- Modeled increase in the generation and transmission portion of a typical 1,000 kWh monthly bill, in constant 2024 dollars
- Scope
- Dominion Energy Virginia; unconstrained load growth with Virginia Clean Economy Act requirements
- Period
- 2040 versus a no-data-center-growth baseline
- Status
- modeled
Tariff evidence55 tariffs
- Metric
- Published large-load tariffs reviewed
- Scope
- United States sample assembled by LBNL
- Period
- Available through 2026
- Status
- administrative
State data show no clear relationship
In this descriptive cross-section, states with a larger EPRI-modeled data-center electricity share in 2024 did not have faster nominal residential price growth over the prior decade.
Pearson r = −0.12; R² = 0.015; Fisher 95% interval for r: −0.39 to +0.16; n = 50. Share = EPRI 2024 medium modeled energy ÷ EIA 2024 total retail sales. Price growth = EIA residential average revenue/kWh, 2014–2024. This association alone cannot establish causation.
| State | Modeled 2024 data-center electricity share (%) | Residential price growth, 2014–2024 (%) |
|---|---|---|
| AK | 0% | 29.7% |
| AL | 1.9% | 32.2% |
| AR | 0% | 29.5% |
| AZ | 8.1% | 25.3% |
| CA | 3.7% | 96.7% |
| CO | 3.4% | 22.5% |
| CT | 1% | 45.6% |
| DE | 0.2% | 24.7% |
| FL | 0.5% | 18.9% |
| GA | 5.4% | 20.9% |
| HI | 0.2% | 15.7% |
| IA | 14.4% | 20.1% |
| ID | 1% | 18.5% |
| IL | 6.9% | 33.2% |
| IN | 0.8% | 28.9% |
| KS | 0.2% | 16.3% |
| KY | 2% | 25.9% |
| LA | 0.1% | 22.6% |
| MA | 2.8% | 68.8% |
| MD | 0.3% | 31% |
| ME | 0.2% | 59.1% |
| MI | 0.4% | 33.5% |
| MN | 1% | 28.6% |
| MO | 1.7% | 21.3% |
| MS | 0.2% | 18.3% |
| MT | 3.8% | 24.4% |
| NC | 2.7% | 27.3% |
| ND | 6.5% | 25.8% |
| NE | 13.6% | 10.9% |
| NH | 0.2% | 33.5% |
| NJ | 5.7% | 22.6% |
| NM | 3.2% | 15.6% |
| NV | 12% | 16% |
| NY | 3.1% | 21.7% |
| OH | 4.4% | 27.9% |
| OK | 2.7% | 22% |
| OR | 18.4% | 40.4% |
| PA | 1.9% | 33.4% |
| RI | 0.2% | 66.9% |
| SC | 2.1% | 14.3% |
| SD | 0.8% | 22.8% |
| TN | 1.9% | 20.3% |
| TX | 6.6% | 26% |
| UT | 9.4% | 14.7% |
| VA | 24.6% | 29.8% |
| VT | 0.1% | 25.4% |
| WA | 7.1% | 37.3% |
| WI | 0.3% | 25.7% |
| WV | 0% | 61.3% |
| WY | 9.3% | 18.8% |
Use the national baseline to read the local record.
National baseline
State price history does not isolate one cause, and this near-zero correlation cannot rule out an effect. Regulators and utilities are now adopting special large-load tariffs to assign infrastructure costs and forecast risk more directly.
Local case
A local bill answer lives in the tariff and the cost allocation. Ask who pays for dedicated infrastructure, what happens if the load misses its forecast, and whether ordinary customers are protected from stranded costs.
What the evidence supports.
Data centers are why residential electricity bills rose.
Not established by state-level data
The 50-state comparison shows no clear cross-state relationship. It cannot establish that data centers never affect rates because state averages mix fuel costs, policy, storms, utility investments, and many other drivers.
Data centers can never raise anyone else's bill.
Also unsupported
Virginia's official scenarios found material possible future bill effects under rapid unconstrained growth. The finding is a forecast for one utility territory and cannot be read as a measured national result.
Ask for these local records.
Without these inputs, a project-specific verdict is incomplete. Treat missing evidence as an open question.
- 01The utility's cost-of-service study and the exact customer class
- 02Direct-assignment rules for substations, transmission, and interconnection upgrades
- 03Minimum bill, minimum billing demand, contract length, collateral, and exit fees
- 04Forecast-miss scenarios: slower ramp, cancellation, early departure, and stranded assets
- 05A bill-impact model that separates generation, transmission, distribution, taxes, and riders
Sources used on this page.
- Agency dataGrade AElectricity retail-sales data, Form EIA-861
All 50 states; residential average retail price in cents per kWh
State residential average prices are annual averages. They do not isolate a causal driver of bills or represent an individual utility tariff. - Official reportGrade BPowering Intelligence 2026
U.S. data-center annual energy by state; 2024 low, medium, and high estimates
These modeled estimates draw on incomplete public and commercial project data rather than a census of metered load. EPRI includes cryptocurrency mining, unlike the LBNL national series. - Agency dataGrade AHistorical Consumer Price Index
U.S. city average, all-items CPI-U, annual averages for 2014 and 2024
National CPI-U is a broad cost-of-living benchmark. It cannot serve as a state-specific electricity-cost index or causal control. - Official reportGrade BRevisiting the relationship between demand growth and electricity prices
U.S. electricity-demand growth, prices, infrastructure cost, and cost allocation
A synthesis of mechanisms and empirical literature. It does not estimate the bill effect of a named data-center project. - Official reportGrade BElectricity Rate Designs for Large Loads: 2026 Update
55 U.S. large-load tariffs available through 2026
The survey covers 55 published large-load tariffs. It cannot show that any one design will eliminate every cross-subsidy or forecast error. - AuditGrade BData Centers in Virginia
Virginia, primarily FY2021–FY2023, with selected forecasts
Virginia-specific. Several values are stakeholder estimates or model outputs, and future utility-cost scenarios are explicitly uncertain. - RegulationGrade AData Center Initiatives factsheet
Dominion Energy Virginia qualifying large-load class and contracts
This is a prospective Virginia rate design. Its safeguards reduce risk but cannot establish the future bill effect of a particular load forecast. - RegulationGrade AFERC large-load integration show-cause orders
All six U.S. regional transmission organizations and independent system operators
Show-cause orders begin a federal process; they do not establish a final national interconnection rule or determine one project's reliability effect.