This page is meant for external readers. It explains how to interpret the results, why some states show larger savings than others, and which sources support the cost, deployment, and policy logic used in the tool. The calculator is transparent by design: direct observations anchor the starting point, while the 2035 pathways are scenario outputs rather than official forecasts.
The main page compares a more expensive 2035 status quo against a lower-cost pathway built around clean generation, better grid utilization, and faster deployment. The wedges show where cost reductions come from, while the donut converts those rate cuts into annual savings.
For the United States, the starting point is official EIA state sales, revenue, retail price, and emissions data. For India, the starting point is built from official CEA tariff, sales, capacity, and planning data.
The tool asks what happens if load grows strongly and wires costs keep rising, then compares that path with a more disciplined strategy built around lower-cost clean power and better use of existing infrastructure.
Generation strategies mainly affect fuel and generation cost. Surplus interconnection, reconductoring, and storage placement affect transmission. Load shaping and flexible demand affect distribution. Caps are used so overlapping levers do not double count the same cost bucket.
It shows whether the affordability pathway also moves the power sector toward a cleaner 2035 mix. It is not a full dispatch model, but it is useful for confirming that lower cost and lower emissions move together in the scenarios shown.
The model is not assuming that every state can save the same amount. Differences in starting rates, system size, fuel exposure, and grid constraints drive the variation in results.
States such as California and New York already start from high retail rates, so lower clean-energy costs and better control of transmission and distribution spending show up more clearly in cents per kilowatt-hour.
Texas, California, Maharashtra, and Gujarat can show very large annual savings because even a moderate rate reduction is multiplied by a very large electricity-sales base by 2035.
Where gas or coal still set a large share of system cost, replacing marginal fossil capacity with lower-cost solar, wind, storage, virtual power plants, or demand response produces a bigger affordability gain.
Where transmission and distribution costs are already large or rising quickly, strategies such as surplus interconnection, reconductoring, storage placement, and off-peak load shaping can materially reduce the need for more expensive infrastructure build-out.
The tool assumes that clean-energy technologies continue to get cheaper through 2035. That is why the affordability pathway improves not only by replacing expensive fossil generation, but also by lowering the cost of serving new load.
States that already have a relatively cleaner supply mix still show affordability gains, but more of those gains come from avoiding unnecessary transmission and distribution costs than from fuel substitution alone.
The tool uses the same logic across both countries, but the public data structure is different. These short notes explain the most important distinctions.
The 2025 retail-rate and electricity-sales anchors come from official EIA state data, and the power-sector emissions anchors come from official EIA state emissions data. The 2035 rates, savings, clean share, and emissions outcomes are scenario outputs layered on top of that official starting point.
The India calculator uses official CEA tariff data, official state sales volumes from the latest available CEA General Review tables, official installed-capacity context, and state planning references. In other words, it is grounded in official data, but the rate anchor is a calibrated state tariff proxy rather than the U.S.-style observed average retail-price series.
The observed or calibrated anchor tells you where the state starts. The 2035 affordability path tells you how much of the future bill could be avoided if states deploy lower-cost clean resources faster and make better use of existing interconnection, corridors, and distribution infrastructure.
The calculator is designed to answer a practical question: if policymakers and utilities choose cheaper and faster options, how much of the future cost increase could be avoided? That is why the focus is on wedges, annual savings, and policy direction rather than on predicting one exact tariff number.
The source stack is intentionally built around official public datasets, primary research papers, and implementation-oriented references on grid utilization and clean-energy deployment.
| Source family | Key references | How they are used |
|---|---|---|
| U.S. observed dataRetail price, sales, emissions | EIA state sales, revenue, and price workbook EIA annual state power-sector emissions | Used for official state starting rates, electricity-sales anchors, and power-sector CO2 anchors in the U.S. version. |
| U.S. 2035 cost structureGeneration, transmission, distribution split | EIA Annual Energy Outlook 2026 Table 54 Berkeley Lab retail price trends 2026 Grid Strategies load growth report | Used to build the 2035 status-quo floor and the higher-load, higher-wires pressure context that the affordability pathway is designed to avoid. |
| India observed data and planning contextTariffs, sales, capacity, planning | CEA Tariff and Duty Book 2025 CEA General Review 2024 CEA installed-capacity reports CEA integrated resource planning division | Used for India tariff proxies, state demand anchors, installed-capacity context, and sanity checks for the state pathways. |
| Affordability strategy evidenceGeneration, transmission, and distribution levers | Grid Growth, Utilization, and Affordability: A Playbook for States Surplus interconnection WP343 reconductoring Scarcity to Surplus | Used for the utilization-first logic behind surplus interconnection, reconductoring, strategic storage placement, and broader affordability framing. |
| Solar and distributed-energy cost evidenceSoft costs, permitting, VPPs | Berkeley Lab U.S.-Germany PV price analysis DOE PV system cost benchmarks SolarAPP+ DOE virtual power plants overview | Used to support the solar soft-cost, permitting, competition, and distributed flexibility story in the generation and distribution strategies. |
| Implementation and policy examplesState action pathways | Generation strategy page Transmission strategy page Distribution strategy page Methods and data page | Used to connect the modeled wedges back to actions states can take on procurement, permitting, competition, interconnection, and grid planning. |
The calculator produces larger savings where three conditions overlap: electricity is already expensive or on track to get more expensive, clean generation can replace relatively costly fossil or peaking supply, and better use of existing interconnection and grid assets can defer new transmission and distribution spending. In that sense, the model is not making an abstract claim that clean energy is always cheaper. It is making a more practical claim: if states choose lower-cost supply, faster deployment, and higher utilization of existing infrastructure, a meaningful share of the projected 2035 cost increase can be avoided.
Use the landing page to compare wedges and annual savings, then come back here if you want the source stack and the interpretation notes behind the results.