Grid connection studies
The effect of a new data centre load on local networks is studied for capacity, voltage and reinforcement needs.
GCRI · Data and Compute
Power engineering for data centres
Power engineering connects computing facilities to the grid and keeps them powered. In this field it covers grid connection, reliability of supply, backup power and the flexibility data centres can offer to the grid.
01Register
Power engineering for data centres on the record.
Method recordSTEM method
- MethodPower systems engineering
- StewardEnergy Nexus
- Applied inData and Compute
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What power systems engineering does here.
Power systems engineering designs and operates the networks that generate, transmit and distribute electricity. It ensures that supply meets demand reliably as generation mixes and loads change. In data and compute and energy, power engineering shows how grid failures cascade into water, communications, health and finance. It informs investment and operating rules that protect dependent services. The canonical description is on the STEM Commons.
Power supply resilience
Uninterruptible power supplies, generators and dual feeds are assessed for how long and how reliably they carry critical computing loads.
Demand flexibility
The potential for computing facilities to shift or reduce load to support the grid is estimated with workload and backup constraints.
03Geography
Energy now in the contiguous United States.
Night lights, population density and true-colour imagery over the contiguous United States, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Energy now, for this page
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
Energy now, framed on the contiguous United States.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA HLS S30Sources and licences
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- MERRA-2 reanalysis, NASA Global Modeling and Assimilation Office; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
- Past year, daily temperature (ERA5 via Open-Meteo): Contains modified Copernicus Climate Change Service information 2026.
- Past year, daily temperature and sunshine (NASA POWER): These data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program..
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (10 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Incoming sunlight at the surface over land, monthly mean: MERRA-2, NASA GMAO / GES DISC · July 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Land surface temperature, day: VIIRS NOAA-20, NASA LANCE · 13 Oct 2026
04Data
What the method consumes.
Datasets and records used by power systems engineering on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Grid network data | Connection capacity | Published statistics |
| Facility load profiles | Demand | OBSERVED |
| Backup test records | Resilience | OBSERVED |
05Simulator
A cascade through interdependent networks.
A hazard footprint takes out co-located assets in every layer; fragmentation, dependency between layers and overload of the survivors then spread the failure. The 3D view separates the layers by depth. Teaching model with stated illustrative values, not a prediction. The map on this page shows The contiguous United States; the model itself is not tied to a place.
Controls
- Assets per layer
- 18
- Hazard footprint
- 8% of area
- Assets that depend on another layer
- 60%
- Spare capacity (tolerance)
- 30%
Readouts
- Assets hit by the hazard
- 9%
- Assets still working
- 11%
- Amplification of the initial damage
- 9.6 times
- Steps to settle
- 5
Numbers, methods and sources
| Step | Data centres working (%) | Networks working (%) | Power working (%) |
|---|---|---|---|
| 0 | 83 | 100 | 89 |
| 1 | 72 | 56 | 50 |
| 2 | 28 | 50 | 50 |
| 3 | 11 | 22 | 39 |
| 4 | 11 | 11 | 17 |
| 5 | 11 | 11 | 11 |
Methods
- Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
- Overload cascades Motter and Lai (2002), Physical Review E 66
- Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
- Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
- Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Major roads Natural Earth
- Railways Natural Earth
- Seaports Natural Earth
- Airports Natural Earth
- Urban extents Natural Earth
- Night lights tonight, outages (VIIRS Day/Night Band) NASA/NOAA VIIRS (Suomi NPP) · register nasa.gibs.context-live-layers
- Natural events (NASA EONET) NASA EONET · register nasa.eonet.events
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Data and Compute in Foundry
- Tested at Data and Compute in Universe
- Codified in Data and Evidence in Standards
- Published in Data and Compute in Reports
- Taught in Faculty of Technology and Digital Systems in Academy
06Validation
How results are checked.
Models are calibrated against measured system disturbances and operating data. Planning studies are checked through operator review and post-event analysis.
| Check | How it is done |
|---|---|
| Load profile validation | Studies use measured load profiles where available |
| Backup testing | Backup systems are tested under load |
| Flexibility verification | Claimed flexibility is demonstrated in trials |
Rapid growth in inverter-based resources changes system dynamics that legacy models represent poorly. Correlated weather extremes can exceed planning assumptions.
07Twin and cascade simulator
Model power engineering for data centres as a system. Run the cascade.
Assembled from the Nexus Standards area profile Digital, telecom and platforms: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Sea-ice loss
- Coastal erosion (set off downstream)
- Biodiversity loss (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
- Digital, telecom and platforms
- Energy utilities and grids Coupling
- Media and information Coupling
- Finance, insurance and capital markets Coupling
- Defence and civil protection Coupling
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-T8Software and supply-chain integrity riskNRS-X6Cyber and digital security risk
Observation
- ITU DataHub ICT indicators (listed for reference)
- World Development Indicators (listed for reference)
- WMO Alert Hub and Register of Alerting Authorities (listed for reference)
- Proportion of population covered by a mobile network, by technology
- Fixed broadband subscriptions per 100 inhabitants, by speed
- Individuals using the Internet (% of population)
- Number of people per 100,000 that are covered by early warning information through local governments or through national dissemination mechanisms
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
08Connections
Topics that rely on this method.
09Methods and evidence
How power engineering for data centres is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Datasets and provenance
The data classes behind power engineering for data centres and what is recorded about each before it is used.
| Data class | Provenance recorded | Resolution stated | Licence checked |
|---|---|---|---|
| Satellite imagery, optical and radar | sensor, processing level, acquisition date | pixel size and revisit | per scene |
| Reanalysis and climate model output | model, experiment, member, version | grid and time step | per release |
| In-situ sensors and gauges | station, instrument, calibration record | point and sampling rate | per network |
| Population and asset exposure | method, vintage, input sources | cell size and unit | per release |
| Boundaries and reference geography | producer, edition, date | scale | per edition |
- Lineage
- W3C PROV record from source to figure
- Principles
- FAIR: findable, accessible, interoperable, reusable
- Gaps
- shown as missing, never filled silently
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
10Stewardship
Who maintains the method.
Energy Nexus stewards power systems engineering for the Consortium and reviews how it is applied across platforms.
11Connected
Where this page leads across the Consortium.
12Questions
Questions and answers.
What are the limits of power systems engineering in this field?
Rapid growth in inverter-based resources changes system dynamics that legacy models represent poorly. Correlated weather extremes can exceed planning assumptions.
How are results checked?
Models are calibrated against measured system disturbances and operating data. Planning studies are checked through operator review and post-event analysis.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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