GCRI · Critical Infrastructure

Energy technology for infrastructure resilience

Energy technologies, from batteries and fuel cells to distributed solar and microgrids, change how infrastructure keeps power through outages. In this field they are assessed as backup and resilience options for critical facilities and networks.

01Register

Energy technology for infrastructure resilience on the record.

Method recordSTEM method
  1. MethodEnergy generation, storage and grid technology
  2. 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 critical infrastructure and cities and built environment, 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.

Backup power options

Diesel generators, batteries and hybrid systems are compared for endurance, fuel logistics and maintenance at critical facilities.

Microgrids for critical services

Microgrids that can island from the main grid are assessed for keeping hospitals, water plants and shelters running.

Fuel supply in emergencies

Fuel storage, resupply contracts and priority arrangements for generators are reviewed against outage scenarios of different lengths.

03Geography

Energy now in Western Europe.

Night lights, population density and sunlight over Western Europe, 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

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 Global Flood Product (NASA LANCE)
Provider
NASA LANCE
Licence
NASA open data
Vintage
daily; Satellite detection; clouds leave gaps
Newest at build
2026-10-14
Resolution
about 250 m grid
Uncertainty
Satellite detection; clouds leave gaps. 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

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 Western 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
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 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.

DataRoleLabel
Facility energy auditsLoads and backupOBSERVED
Technology cost and performance dataOptionsESTIMATED
Fuel supply recordsLogisticsDECLARED
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 Western 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
17%
Assets still working
22%
Amplification of the initial damage
4.7 times
Steps to settle
3
Numbers, methods and sources
StepTransport working (%)Power working (%)Telecoms working (%)
0838383
1394456
2282817
3282811

Methods

  1. Interdependent networks Buldyrev and others (2010), Catastrophic cascade of failures in interdependent networks, Nature 464
  2. Overload cascades Motter and Lai (2002), Physical Review E 66
  3. Betweenness load Brandes (2001), Journal of Mathematical Sociology 25
  4. Multilayer networks Kivela and others (2014), Journal of Complex Networks 2
  5. Infrastructure interdependencies Rinaldi, Peerenboom and Kelly (2001), IEEE Control Systems 21(6)
  6. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  7. 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
A cascade through interdependent networks. Illustrative teaching model: it runs on the values set by the controls, not on measured data. 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).
06Validation

How results are checked.

Technologies are evaluated through pilot deployments, standard testing and operational monitoring. Model results are compared with metered performance.

CheckHow it is done
Load testingBackup systems are tested under realistic load and duration
Fuel logistics reviewResupply assumptions are checked against road access and supplier capacity
Maintenance recordsBackup equipment maintenance is verified

Performance under extreme weather and at large scale may differ from tests. Supply chains for components and minerals are concentrated.

07Twin and cascade simulator

Model energy technology for infrastructure resilience as a system. Run the cascade.

Assembled from the Nexus Standards area profile Critical infrastructure and critical entities: 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

  • Dam failure
  • River flood (set off downstream)
  • Flash flood (set off downstream)
  • Mass displacement (set off downstream)
  • Widespread power outage (set off downstream)
  • Water supply shortfall (set off downstream)

Controls

  • NS-0 Constitution and principles
  • NS-3 Risk record UNIs (grammar, legacy crosswalk, allocation authority)
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NRS-H61 Hazard family: Cascading failure (NHS-1 sys.cascade)
  • NRS-H94 Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)
  • NRS-H107 Hazard family: Digital infrastructure concentration and monoculture (NHS-1 tech.digital-concentration)
  • NRS-K31 Domain pack profile: Cyber national resilience (DOMAIN-031)
  • NRS-K36 Domain pack profile: Critical infrastructure (DOMAIN-036)
  • NRS-K48 Domain pack profile: Financial system resilience (DOMAIN-048)

Observation

  • OpenStreetMap planet and replication (listed for reference)
  • Global Power Plant Database (listed for reference)
  • NASA Black Marble night-time lights (VNP46) (listed for reference)
  • Global Human Settlement Layer (listed for reference)
  • Damage to critical infrastructure attributed to disasters
  • Number of disruptions to basic services attributed to disasters
  • Proportion of the rural population who live within 2 km of an all-season road

Example geography

  • Europe: 113 hexagonal cells
  • Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
  • Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
  • Rain rate, last half hour (IMERG), NASA GIBS
  • Surface soil moisture (SMAP), NASA GIBS
08Connections

Topics that rely on this method.

09Sector signature

The sector signature of energy technology for infrastructure resilience.

The sectors energy technology for infrastructure resilience 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
Teaching values for a typical city-region, not measurements for any place. Coupling is the summed strength of the relations in play, as a share of all fifteen.
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.

11Methods and evidence

How energy technology for infrastructure resilience 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 for infrastructure resilience: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.

++−+Critical infrastructureCitiesCommunitiesEconomyR reinforcingB balancing
Causal loop diagram from the page’s systems; signs show the direction of influence. Illustrative structure for a system dynamics or network model, not estimated couplings.
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
12Connected

Where this page leads across the Consortium.

13Questions

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.
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