Weather-driven generation profiles
Wind turbine power curves and photovoltaic models convert reanalysis wind and irradiance into hourly output for each site, with losses, availability and curtailment assumptions stated beside the series.
GCRI · Energy Nexus
Energy technology assessment
Energy technology assessment is how this platform reads the hardware of the energy system: what generators, storage devices, converters and grid equipment can deliver, under which conditions and with which failure modes. It connects tested and field performance to the reliability, cost and materials questions that transition planners have to answer.
01Register
Energy technology assessment on the record.
Method recordSTEM method
- MethodEnergy generation, storage and grid technology
- StewardEnergy Nexus
- Applied inEnergy Nexus
- Data4 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What energy generation, storage and grid technology does here.
Energy generation, storage and grid technology cover the devices and systems that produce, store and deliver energy. They shape the reliability, cost and emissions of energy supply on which all other sectors rely. In energy, critical infrastructure and climate and hazards, energy technology assessment informs transition pathways that keep supply reliable during shocks. It links climate, materials and financial risks in the energy system. The canonical description is on the STEM Commons.
Storage technology comparison
Batteries, pumped hydro, compressed air and hydrogen are compared by duration, efficiency, degradation, safety and material needs, using published test data and operating records rather than vendor claims alone.
Equipment performance under extremes
Transformer, inverter and panel ratings are compared with field behaviour in heat, cold, dust and flooding, so scenario assumptions reflect the conditions equipment will actually meet.
03Geography
Energy now in Europe.
Night lights and population density over Europe, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 2
Energy now, for this page
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest month GIBS publishes)
- Source
- MERRA-2 (NASA GMAO / GES DISC)
- Provider
- NASA GMAO / GES DISC
- Licence
- NASA open data
- Vintage
- monthly; Reanalysis model; latest month GIBS publishes
- Newest at build
- 2026-07-01
- Resolution
- about 2 km grid
- Uncertainty
- Reanalysis model estimate of monthly mean incoming sunlight over land; an indicator of solar resource, not a site yield assessment.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- VIIRS NOAA-20 (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite retrieval; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground on the day shown, an indicator of cooling demand; not air temperature or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
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 Europe.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/NOAA VIIRS · NASA JPL OPERA DIST-ALERTSources 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/NOAA VIIRS (Suomi NPP). 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).
- Observed (EO): NASA JPL OPERA DIST-ALERT (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 energy generation, storage and grid technology on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Plant and fleet performance records | Availability and capacity factors | REPORTED |
| Reanalysis weather fields | Resource inputs to generation models | MODELLED |
| Equipment type-test and certification results | Rated performance under standard conditions | Published statistics |
| Storage project databases | Installed technologies and sizes | REPORTED |
05Monitor
Power flow study.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Power flow study
Energy · Energy technology assessment · Run R-7995
- Voltage range0.95to 1.05 puNormal
- Losses2.5pct of load
- Converged in4iterationsNormal
Two branches above 90 pctGCRI · Energy Nexus
Single-line diagram
example network
Branch loading
| L1-2 | 66 | 539 | Normal |
| L2-5 | 91 | 391 | High |
| L3-4 | 53 | 303 | Normal |
| T7 | 104 | 151 | Overload |
06Validation
How results are checked.
Technologies are evaluated through pilot deployments, standard testing and operational monitoring. Model results are compared with metered performance.
| Check | How it is done |
|---|---|
| Metered comparison | Modelled generation is compared with metered output for plants with public data before a profile is used |
| Standard test conditions | Ratings are read against the IEC test standards they were measured under, and the gap to field conditions is stated |
| Degradation tracking | Storage and panel performance assumptions are checked against operating records over time, not first-year results |
Performance under extreme weather and at large scale may differ from tests. Supply chains for components and minerals are concentrated.
07Simulator
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 Europe; 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
- 11%
- Assets still working
- 17%
- Amplification of the initial damage
- 7.5 times
- Steps to settle
- 5
Numbers, methods and sources
| Step | Power working (%) | Gas working (%) | Telecoms working (%) |
|---|---|---|---|
| 0 | 83 | 89 | 94 |
| 1 | 56 | 50 | 78 |
| 2 | 44 | 22 | 61 |
| 3 | 17 | 22 | 50 |
| 4 | 17 | 6 | 39 |
| 5 | 17 | 6 | 28 |
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 Energy Nexus in Foundry
- Tested at Energy Nexus in Universe
- Codified in Energy and Critical Infrastructure in Standards
- Published in Energy Nexus in Reports
- Taught in Faculty of Infrastructure, Energy and Industry in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Model energy technology assessment as a system. Run the cascade.
Assembled from the Nexus Standards area profile Energy risk standards: 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
- Heatwave
- Windstorm
- Widespread power outage (set off downstream)
- Large forest disturbance (set off downstream)
- Wildfire (set off downstream)
- Extreme fire weather (set off downstream)
- Heat-driven grid stress (set off downstream)
- Agricultural drought (set off downstream)
- Flash drought (set off downstream)
- Snow drought (set off downstream)
- Ground-level ozone episode (set off downstream)
- Meteorological drought (set off downstream)
- Extreme night-time heat (set off downstream)
- Lake and river heatwave (set off downstream)
- Cooling-water curtailment of power plants (set off downstream)
- Heat-induced track buckling (set off downstream)
- Shoreline ice shove (set off downstream)
- Seiche (set off downstream)
Systems and couplings
- Energy risk standards
- Water risk standards Common driver
- Critical infrastructure and critical entities Cascade
- Climate risk standards Feedback
- Space weather, spectrum and timing risk standards Common driver
Controls
NS-0Constitution and principlesNS-1The UNI and the artefact catalogue (grammar v0.2: domain, sector, region and environment segments)NS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NRS-H84Hazard family: Energy system instability and shortfall (NHS-1 infra.energy)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-U8Universe platform profile: Energy NexusNRS-H48Hazard family: Utility and communication network outage (NHS-1 infra.utilities)NRS-H109Hazard family: Energy-transition technology failures (NHS-1 tech.energy-transition)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)
Observation
- Global Power Plant Database (listed for reference)
- NASA Black Marble night-time lights (VNP46) (listed for reference)
- Ember electricity data (listed for reference)
- ENTSO-E Transparency Platform (listed for reference)
- EIA Open Data API (listed for reference)
- Proportion of population with access to electricity
- Renewable energy share in total final energy consumption
- Number of disruptions to basic services attributed to disasters
- Damage to critical infrastructure attributed to disasters
Example geography
- Europe: 113 hexagonal cells
- Surface air temperature, daytime (AIRS), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
10Stewardship
Who maintains the method.
Energy Nexus stewards energy generation, storage and grid technology for the Consortium and reviews how it is applied across platforms.
11Sector signature
The sector signature of energy technology assessment.
The sectors energy technology assessment touches and how strongly they depend on each other: what a study has to observe and model together.
Select corners · hover lines6 / 15 relations
Signature
Energy and its partners
Energy gives power for transport and telecoms to infrastructure (0.8), pumping and treatment to water (0.5) and fertiliser, machinery and cold chains to food (0.5).
W · E · I · F · H · B
- Sectors
- 4 of 6
- Relations
- 6 of 15
- Coupling
- 0.46 share of total
Strongest relations in play
- Energy → Infrastructure · power for transport and telecoms0.8
- Water → Energy · cooling water and hydropower0.6
- Water → Food · irrigation0.8
- Infrastructure → Water · pipes, dams and treatment works0.6
12Questions
Questions and answers.
What are the limits of energy generation, storage and grid technology in this field?
Performance under extreme weather and at large scale may differ from tests. Supply chains for components and minerals are concentrated.
How are results checked?
Technologies are evaluated through pilot deployments, standard testing and operational monitoring. Model results are compared with metered performance.
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.
13Methods and evidence
How energy technology assessment 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.
System model
The couplings between the systems in energy technology assessment: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
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
ReportsContribute
Contribute evidence from this page.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIEnergy NexusGlobal Sector Risk and ResilienceStrategic SystemsScenario and Stress-Test
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
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