A worked example of how to use grid data to shortlist a site. We take one realistic brief and run it across five candidate grids on the four metrics that actually decide a build, system-level room, power price, carbon intensity and reliability, then show how to reproduce it live for your own parameters.
No single grid wins on all four. The exercise is about trade-offs, and seeing them side by side is exactly what a grid-intelligence view is for.
Indicative profiles for comparison. Open the live app for current, sortable figures, values move hourly.
| Grid | Power cost | Carbon (gCO₂/kWh) | Headroom | Connection outlook | Fits brief? |
|---|---|---|---|---|---|
| Texas · ERCOT | Low | ~320 | High | Fast by US standards; energy-only market | Cost + speed |
| Virginia · PJM | Mid | ~290 | Tight | World's largest hub, but queue & capacity now constrained | Ecosystem, not capacity |
| Québec | Very low | ~5 | Moderate | Hydro-rich, cool climate; allocation-dependent | Cost + carbon |
| Sweden (SE) | Low | ~45 | Moderate | Hydro + nuclear + wind; northern zones favourable | Carbon + cost |
| Pacific NW · USA | Low | ~80 | Moderate | Hydro-backed, cool; established DC corridor | Balanced |
Carbon figures are matched to national/official sources; system-level room and connection outlook are directional. The point isn't the exact number, it's that the ranking flips depending on which metric you weight.
Cheapest fast-to-connect option, but ~320 gCO₂/kWh works against a net-zero story unless paired with renewable PPAs (which ERCOT has plenty of).
Near-zero (~5) and low (~45) carbon with low cost. Trade-off is allocation and distance from major demand centres.
Unmatched connectivity and operators, but system-level room and the interconnection queue are now the binding constraint, not power itself.
Low cost, low carbon (~80), cool climate and an established corridor, a strong all-rounder when no single metric dominates.
This case study is a snapshot; the real value is doing it with current data for your own brief. In PowerGridIQ you can:
① Open the live map and switch the layer to Carbon, Price or Utilization to see the world re-colour by your priority. ② Use the access read to rank regions on system-level room, which is a screen rather than confirmation that a specific site can take 100 MW. ③ Open the Data Hub, sort the all-regions table by any column, filter to your shortlist, and export it to CSV for your own model. ④ Open any region for its fuel mix, reserve margin and reliability detail.
There isn't one, it's the combination. Power cost dominates operating expense, carbon intensity drives location-based Scope 2 emissions, system-level room and the interconnection queue decide whether you can connect on schedule, and reserve margin signals reliability risk. The right site is the best weighted trade-off for your priorities.
Generation can be built far faster than the transmission and interconnection that connect new load. In the busiest US markets, queues stretch years and only a fraction of projects ever connect, so a cheap grid you can't join on time may be worse than a pricier one you can.
Live grids update sub-hourly from system operators (EIA, ENTSO-E, IESO, AEMO and others); carbon and fuel-mix figures are matched to national/official sources. Regions without a live feed are clearly labelled as estimates.